A preliminary study of the ability of the 4Kscore test, the Prostate Cancer Prevention Trial-Risk Calculator and the European Research Screening Prostate-Risk Calculator for predicting high-grade prostate cancer | Actas Urológicas Españolas (English Edition)

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En la línea de abscisas se ubican los diferentes puntos de corte de decisión clínica coincidentes con la predicción ofrecida por 4KsT; al trazar una línea perpendicular sobre un determinado valor del punto de corte, esta corta la línea azul (CaP-AG no diagnosticado) en un determinado punto, y al trazar una línea horizontal al nivel de ese punto cuando, esta se encuentra con el eje de ordenadas, nos indica el porcentaje de pacientes de CaP-AG no diagnosticado para ese punto de corte. La misma línea perpendicular anterior corta la línea roja en un punto (biopsias evitadas), y su traslación al eje de ordenadas nos indica el número de biopsias evitadas para un determinado punto de corte. Por ejemplo: un punto de corte correspondiente a una probabilidad de CaP-AG del 9% ofrecida por 4KsT conduce a un 0% de pérdidas de diagnóstico de CaP-AG en nuestra serie de 51 pacientes, y a un ahorro del 22% de las biopsias. Del mismo modo, para una probabilidad del 50% que supondría biopsiar a todo aquel que 4KsT le asigne una probabilidad de CaP-AG superior al 50%, ello supondría que un 50% de los CaP-AG de nuestra serie no serían diagnosticados, y por otro lado se evitarían un 76% de Bx." ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Á. Borque-Fernando, L.M. Esteban-Escaño, J. Rubio-Briones, A.C. Lou-Mercadé, R. García-Ruiz, A. Tejero-Sánchez, M.V. Muñoz-Rivero, T. Cabañuz-Plo, J. Alfaro-Torres, I.M. Marquina-Ibáñez, S. Hakim-Alonso, E. Mejía-Urbáez, J. Gil-Fabra, P. Gil-Martínez, R. Ávarez-Alegret, G. Sanz, M.J. Gil-Sanz" "autores" => array:17 [ 0 => array:2 [ "nombre" => "Á." "apellidos" => "Borque-Fernando" ] 1 => array:2 [ "nombre" => "L.M." "apellidos" => "Esteban-Escaño" ] 2 => array:2 [ "nombre" => "J." "apellidos" => "Rubio-Briones" ] 3 => array:2 [ "nombre" => "A.C." "apellidos" => "Lou-Mercadé" ] 4 => array:2 [ "nombre" => "R." "apellidos" => "García-Ruiz" ] 5 => array:2 [ "nombre" => "A." "apellidos" => "Tejero-Sánchez" ] 6 => array:2 [ "nombre" => "M.V." "apellidos" => "Muñoz-Rivero" ] 7 => array:2 [ "nombre" => "T." "apellidos" => "Cabañuz-Plo" ] 8 => array:2 [ "nombre" => "J." "apellidos" => "Alfaro-Torres" ] 9 => array:2 [ "nombre" => "I.M." "apellidos" => "Marquina-Ibáñez" ] 10 => array:2 [ "nombre" => "S." "apellidos" => "Hakim-Alonso" ] 11 => array:2 [ "nombre" => "E." "apellidos" => "Mejía-Urbáez" ] 12 => array:2 [ "nombre" => "J." "apellidos" => "Gil-Fabra" ] 13 => array:2 [ "nombre" => "P." "apellidos" => "Gil-Martínez" ] 14 => array:2 [ "nombre" => "R." "apellidos" => "Ávarez-Alegret" ] 15 => array:2 [ "nombre" => "G." "apellidos" => "Sanz" ] 16 => array:2 [ "nombre" => "M.J." 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Open procedures are represented in cold colors and minimally invasive procedures in warm colors. The total number of procedures is indicated at the top of each column." ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "A. Vilaseca, M. Musquera, D.P. Nguyen, G. Di Paola, L.R. Romeo, A. Melnick, E. García-Cruz, M.J. Ribal, J. Huguet, A. Alcaraz" "autores" => array:10 [ 0 => array:2 [ "nombre" => "A." "apellidos" => "Vilaseca" ] 1 => array:2 [ "nombre" => "M." "apellidos" => "Musquera" ] 2 => array:2 [ "nombre" => "D.P." "apellidos" => "Nguyen" ] 3 => array:2 [ "nombre" => "G." "apellidos" => "Di Paola" ] 4 => array:2 [ "nombre" => "L.R." "apellidos" => "Romeo" ] 5 => array:2 [ "nombre" => "A." "apellidos" => "Melnick" ] 6 => array:2 [ "nombre" => "E." "apellidos" => "García-Cruz" ] 7 => array:2 [ "nombre" => "M.J." "apellidos" => "Ribal" ] 8 => array:2 [ "nombre" => "J." "apellidos" => "Huguet" ] 9 => array:2 [ "nombre" => "A." 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Borque-Fernando, L.M. Esteban-Escaño, J. Rubio-Briones, A.C. Lou-Mercadé, R. García-Ruiz, A. Tejero-Sánchez, M.V. Muñoz-Rivero, T. Cabañuz-Plo, J. Alfaro-Torres, I.M. Marquina-Ibáñez, S. Hakim-Alonso, E. Mejía-Urbáez, J. Gil-Fabra, P. Gil-Martínez, R. Ávarez-Alegret, G. Sanz, M.J. Gil-Sanz" "autores" => array:17 [ 0 => array:4 [ "nombre" => "Á." "apellidos" => "Borque-Fernando" "email" => array:1 [ 0 => "aborque@salud.aragon.es" ] "referencia" => array:3 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "g" "identificador" => "aff0035" ] 2 => array:2 [ "etiqueta" => "*" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "L.M." "apellidos" => "Esteban-Escaño" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "b" "identificador" => "aff0010" ] 1 => array:2 [ "etiqueta" => "g" "identificador" => "aff0035" ] ] ] 2 => array:3 [ "nombre" => "J." "apellidos" => "Rubio-Briones" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "c" "identificador" => "aff0015" ] ] ] 3 => array:3 [ "nombre" => "A.C." "apellidos" => "Lou-Mercadé" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "d" "identificador" => "aff0020" ] ] ] 4 => array:3 [ "nombre" => "R." "apellidos" => "García-Ruiz" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 5 => array:3 [ "nombre" => "A." "apellidos" => "Tejero-Sánchez" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 6 => array:3 [ "nombre" => "M.V." "apellidos" => "Muñoz-Rivero" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 7 => array:3 [ "nombre" => "T." "apellidos" => "Cabañuz-Plo" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 8 => array:3 [ "nombre" => "J." "apellidos" => "Alfaro-Torres" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "e" "identificador" => "aff0025" ] ] ] 9 => array:3 [ "nombre" => "I.M." "apellidos" => "Marquina-Ibáñez" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "e" "identificador" => "aff0025" ] ] ] 10 => array:3 [ "nombre" => "S." "apellidos" => "Hakim-Alonso" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "e" "identificador" => "aff0025" ] ] ] 11 => array:3 [ "nombre" => "E." "apellidos" => "Mejía-Urbáez" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "e" "identificador" => "aff0025" ] ] ] 12 => array:3 [ "nombre" => "J." "apellidos" => "Gil-Fabra" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 13 => array:3 [ "nombre" => "P." "apellidos" => "Gil-Martínez" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 14 => array:3 [ "nombre" => "R." "apellidos" => "Ávarez-Alegret" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "e" "identificador" => "aff0025" ] ] ] 15 => array:3 [ "nombre" => "G." "apellidos" => "Sanz" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "f" "identificador" => "aff0030" ] 1 => array:2 [ "etiqueta" => "g" "identificador" => "aff0035" ] ] ] 16 => array:3 [ "nombre" => "M.J." "apellidos" => "Gil-Sanz" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] ] "afiliaciones" => array:7 [ 0 => array:3 [ "entidad" => "Servicio de Urología, Hospital Universitario Miguel Servet, Zaragoza, Spain" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Escuela Universitaria Politécnica La Almunia, Zaragoza, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Servicio de Urología, Instituto Valenciano de Oncología, Valencia, Spain" "etiqueta" => "c" "identificador" => "aff0015" ] 3 => array:3 [ "entidad" => "Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain" "etiqueta" => "d" "identificador" => "aff0020" ] 4 => array:3 [ "entidad" => "Servicio de Anatomía Patológica, Hospital Universitario Miguel Servet, Zaragoza, Spain" "etiqueta" => "e" "identificador" => "aff0025" ] 5 => array:3 [ "entidad" => "Departamento de Métodos Estadísticos, Facultad de Ciencias, Universidad de Zaragoza, Zaragoza, Spain" "etiqueta" => "f" "identificador" => "aff0030" ] 6 => array:3 [ "entidad" => "Grupo Consolidado de Investigación “Modelos Estocásticos”, Gobierno de Aragón, European Social Fund, Zaragoza, Spain" "etiqueta" => "g" "identificador" => "aff0035" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "4Kscore Test, Prostate Cancer Prevention Trial-Risk Calculator y European Research Screening Prostate-Risk Calculator en la predicción del cáncer de próstata de alto grado; estudio preliminar" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1202 "Ancho" => 3146 "Tamanyo" => 183665 ] ] "descripcion" => array:1 [ "en" => "Box-plot diagrams of the probabilities assigned by each model depending on whether they are patients with/without HGPCa (Green: patients with HGPCa; blue: patients without HGPCa)." ] ] ] "textoCompleto" => "IntroductionOverdiagnosis, and consequently overtreatment, is a reality in prostate cancer (PCa). Strategies such as active surveillance and focal therapy are used to compensate for the side effects from conventional treatments such as surgery or radiotherapy. Such treatments are especially burdensome for patients whose tumor will not result in lethal or even morbid consequences.<a class="elsevierStyleCrossRefs" href="#bib0100">1–3</a>The best strategy to prevent overtreatment is to avoid overdiagnosis. If we were able to select as candidates for a prostate biopsy (Bx) only patients who have high risk tumours, strategies such as active surveillance and even focal therapy may not be necessary.Aware of this reality, the urological community is focusing its efforts on pre-biopsy identification of high-grade tumors with a Gleason score Bx>7, either by multiparameter magnetic resonance<a class="elsevierStyleCrossRef" href="#bib0115">4</a> or by new optimized markers for the diagnosis of high-grade prostate cancer (HGPCa) such as the 4Kscore Test (4KsT).The 4KsT is actually a weighted combination of seven variables, 4 kallikrein (total PSA, free PSA, intact PSA and hK2) and 3 clinical variables (age, digital rectal examination and existence or not of previous biopsy) that provides individual probability of harboring HGPCa. Its ability to identify prebiopsy highly aggressive tumors makes it an attractive option. It was recently validated in a US cohort study of more than 1000 patients with satisfactory results.<a class="elsevierStyleCrossRef" href="#bib0120">5</a> However, there are other multivariate models available, and although they are developed in somewhat different clinical scenarios, they also offer an individualized risk of PCa in general and HGPCa in particular.<a class="elsevierStyleCrossRef" href="#bib0125">6</a>Our goal in this project is to conduct a preliminary comparative study of the capacity of 4KsT against Prostate Cancer Prevention Trial Risk Calculator 2.0 (PCPTRC 2.0)<a class="elsevierStyleCrossRef" href="#bib0130">7</a> and European Randomized Study of Screening for Prostate Cancer-Risk Calculator 4 (ERSPC-RC 4)<a class="elsevierStyleCrossRef" href="#bib0135">8</a> to identify HGPCa.Materials and methodsWe prospectively evaluated 51 patients undergoing prostate Bx on suspicion of PCa, as indicated according to standard clinical practice. The characteristics thereof are detailed in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>.<elsevierMultimedia ident="tbl0005"></elsevierMultimedia>The Bx was performed through transrectal procedure with the number of cylinders set according to age and prostate volume, according to the Vienna nomogram<a class="elsevierStyleCrossRef" href="#bib0140">9</a> modified with a minimum of 10 cylinders. First, the Bx was focused on the peripheral zone and included the transition zone in case of repeated Bx or ultrasound findings. The Bx was practiced under sedation and prophylaxis with 400mg/IV single dose of ciprofloxacin in the run-Bx. The uropathologist from the Department of Pathology of our hospital analyzed the Bx samples. They reached a consensus, and in each case, the Gleason score of samples diagnosed with PCa were in accordance with the post-ISUP 2005<a class="elsevierStyleCrossRef" href="#bib0145">10</a> criteria. They considered HGPCa to be the one with a Gleason score of Bx≥7.Patients signed the informed consent form for the blood sample extraction and calculation of 4KsT and its study. We collected demographic data necessary for the calculation of risk in the different models analyzed.The risks of HGPCa were calculated according to PCPTRC 2.0 with and without free PSA, ERSPC-RC 4 (as it is not mass screening population) with prostate volume estimate, either by rectal examination or by transrectal ultrasound as well as 4KsT by the reference laboratory. For the computation of predictions of PCPTRC 2.0 and ERSPC-RC 4, we employed PSA and free PSA values obtained and used for calculating the 4KsT.We compared the predictions obtained by analyzing their distributions in cases of HGPCa vs. non-HGPCa (Mann–Whitney U test, since it does not follow normal distribution), areas under the ROC curve (AUC) (compared with DeLong test), probability density functions, box-plots, and curves of clinical utility. Those analyses were performed with the statistical programming language Rv3.1.0.ResultsIn the total of 51 biopsies performed, 24 of them were identified as carriers of PCa (47%), while 12 cases were HGPCa (23.5%). It is a very similar proportion to the study carried out by Parekh et al.<a class="elsevierStyleCrossRef" href="#bib0120">5</a> Those results may be possibly influenced by the fact that it is a preselected population that undergoes routine clinical practice with a high risk of suffering from PCa based on potentially different restrictive indication criteria. But they presumably match between the US multicenter and our center (age, PSA, free PSA, rectal exam, family history, prostate volume).The median of probabilities assigned by 4KsT to the 12 patients with HGPCa was 51.5% (25th–75th percentile: 25–80.5%) vs. 16% (25th–75th percentile: 8–26.5%) in the 39 patients who had no HGPCa (p=0.002). Likewise, PCPTRC-2.0 with and without PSA-free and ERSPC-RC 4 with prostate volume estimated by rectal exam or transrectal ultrasonography showed significant differences between the distribution of probabilities of developing HGPCa between patients with and without HGPCa. However, there was not much gap between the two groups (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).<elsevierMultimedia ident="tbl0010"></elsevierMultimedia>Although all models show a good discrimination capacity with above 0.7 AUC, the highest AUC being the most relevant to the ERSPC-RC 4 with 0.807 (95% CI: 0.672–0.904) followed by 4KsT with 0.794 (95% CI: 0.657–0.894), there is no statistically significant differences between the AUCs of the models except in the comparison between the two models of ERCPC-RC 4 (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>).<elsevierMultimedia ident="tbl0015"></elsevierMultimedia>As we did earlier in the analysis of prognostic models of organ-confined tumor,<a class="elsevierStyleCrossRef" href="#bib0150">11</a> we have implemented the application of probability density functions (PDFs) to the visual comparison of the distribution of the probabilities offered by each model to patients with HGPCa and without HGPCa. In general, we can observe that except for the 4KsT case, and subtly in the case of ERSPC RC4-DRE, the different models assign significantly low probabilities (below 50%) of developing HGPCa to patients that do not really suffer from that. That shows a zero rate of false positives in probabilities, both for PCPTRC as well as for ERSPC above 50% (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>A). And in the same PDF we can observe how 4KsT offers a growing distribution of HGPCa risk to patients who suffer from it. Unlike other models, except in the PCPTRC 2.0 case that has a peak distribution in high probability, the rest assign low probabilities of HGPCa to patients who really do have HGPCa. Additionally, those assigned probabilities in real HGPCa overlap to those accurate low probabilities of patients without HGPCa (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>B).<elsevierMultimedia ident="fig0005"></elsevierMultimedia>In <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>, and as a simplification of the density functions, boxplots are created with the probabilities assigned by the models to the 51 patients of the study, and they are represented according to whether they have or not HGPCa. We can observe more clearly how the range of probabilities assigned by 4KsT to patients with HGPCa is broader than in the case of the other models. In fact, a patient who was biopsied with a PSA of 356.6ng/ml (59, caucasian, suspicious DRE, first biopsy, free PSA ratio:18%) and which was confirmed as Gleason score 9 (4+5) was labeled as a probability of 95% to have HGPCa by 4KsT, vs. a 76% and 59% of probability by ERSPC-RC 4-DRE and without volumetric estimation by DRE, but by transrectal ultrasound. And a probability of 79% and surprisingly of 9% if the PCPTRC 2.0 is used with or without free-PSA.<elsevierMultimedia ident="fig0010"></elsevierMultimedia>In any case, this figure confirms how the probabilities assigned by 4KsT and ERSPC-RC 4 are the least overlapped between patients with HGPCa and without it. On the one hand, we observe in turn how a cut of 9% with 4KsT identifies the total of 12 cases with HGPCa, and excludes beneath it to just over 25% of patients without HGPCa. On the other hand, the figure presents a 4% of chance (according to “>3%” suggested in the ERSPC-RC 4). In this case of ERSPC-RC 4 this cutoff identifies almost all HGPCa with a similar saving of Bx with ERSPC- RC 4-DRE among non-HGPCa and 4KsT, and which is still above with ERSPC-RC 4 without DRE.As for the clinical utility of the models in a screening process, we have also analyzed the number of biopsies avoided and undiagnosed HGPCa; as well as their characteristics using different predictive models. These models represent biopsy performance savings concretely, as well as diagnostic losses of HGPCa and especially its characteristics according to the different models. In general, the findings reflect that diagnostic losses of HGPCa with 4KsT seem to be less relevant, to a lesser extent than with the other models. It is particularly striking how the diagnostics losses of Gleason 8 (4+4) with PCPTRC 2.0 are more remarkable than with other models (Appendix B, supplementary material S1).In order to get a clearer interpretation of the applicability of the marker, we have implemented in this study our designed curves of clinical decision that offer the diagnostic performance of each marker according to different cutoffs. The graphic exhibits two distinct curves both measured in percentage in the Y axis. In the first one, for each potential cut-off score, we can see the percentage of biopsies avoided, and in the second curve, the percentage of high-grade cancers underdiagnosed for the same cut-off points. Therefore, the clinical utility curves (CUC) are particularly enlightening to implement the marker in daily clinical practice, showing how, for example, a cut-off of probability of HGPCa of 9% with 4KsT (Bx indication only to 4KsT≥9%) would identify correctly all patients with HGPCa. That could bring about a BxP saving of 22% in case the results of this preliminary study of 51 patients (<a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>) are confirmed. If we think it is permissible up to 16% of high-grade cancers with delayed diagnosis, the percentage of avoided biopsies would rise to 45%. Those same CUCs are very illustrative, as well as for the other models of PCPTRC 2.0 (<a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a>) and of ERSPC-RC 4 (<a class="elsevierStyleCrossRef" href="#fig0025">Fig. 5</a>).<elsevierMultimedia ident="fig0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia><elsevierMultimedia ident="fig0025"></elsevierMultimedia>DiscussionIn the list of markers in PCa, 4KsT has burst onto the scene as a specific marker for HGPCa. However, this is actually a weighted combination of 7 variables of which 4 are real biological markers, serum markers in their conventional concept (total PSA, free-PSA, PSA-intact, human kallikrein 2 or hK2) and the other 3 are clinical factors associated with the disease (directly proportional in the case of age and DRE and vice versa in the case of the existence of prior Bx). Those specific characteristics of 4KsT are an adjusted multivariate analysis model of logistic regression optimized by the existence or inexistence of HGPCa. Other available markers that determine the existence of HGPCa can be compared to these properties. However, their initial analysis is not optimized for predicting the risk of HGPCa but rather for PCa (specifically, PCA3<a class="elsevierStyleCrossRef" href="#bib0155">12</a> and the Prostate Health Index (PHI)<a class="elsevierStyleCrossRef" href="#bib0160">13</a>). The most consistent comparison is to other multivariate risk models having HGPCa, such as those derived from the American study on the possible preventive nature of the finasteride in the appearance of PCa (the PCPTRC 2.0, in versions of free PSA or without it).<a class="elsevierStyleCrossRef" href="#bib0130">7</a> Additionally, those coming from the European study on the impact of mortality of mass population screening in PCa (ERSPC-Risk Calculator, in its model 4 since our sample matches a population not subject to mass screening).<a class="elsevierStyleCrossRef" href="#bib0135">8</a> They are both available on-line.<a class="elsevierStyleCrossRefs" href="#bib0165">14,15</a>The 4KsT score has burst onto the scene of PCa diagnosis by focusing its prediction in estimating HGPCa. Its clinical development began in the estimation of PCa, where HGPCa was extrapolated as a combination of 4 kallikreins and age, based on the mass screening series and sextant Bx of the ERSPC series and the evaluation criteria of prostate Bx pre-ISUP 2005.<a class="elsevierStyleCrossRef" href="#bib0145">10</a> However, the current clinical development and its design as 4KsT with 7 variables was developed on contemporary patients based on Bx criteria according to routine clinical practice in 26 US centers, involving Bx of at least 10 cylinders and HGPCa interpretive criteria post-ISUP 2005.<a class="elsevierStyleCrossRef" href="#bib0120">5</a>According to the specific purpose of 4KsT and in light of current concerns to avoid unnecessary overdiagnosis and overtreatment, we focused our analysis on patients with HGPCa (Gleason score≥7) vs. non-HGPCa (including no PCa as well as PCa with Gleason score<7).There are some potential limitations on the use of each of the three predictive models. PCPT RC 2.0 is built on patients undergoing sextant biopsy; therefore, we can expect a lower ability to identify PCa and HGPCa against the current Bx of at least 10 cylinders. That can influence the odds offered by this model to be lower than reality since it is a model fitted to the lower identifying capacity of the sextant biopsy. In the case of ERSPC-RC 4, the model does not allow introducing prostate volumes higher than 110cc or a total PSA higher than 50ng/ml, as these are the highest limits that can be entered for computation. And again, the models are built on the identifying capacity of sextant biopsy, which means the probabilities offered by the models may be lower than reality again. In the case of prostate volume values, or extreme PSA, they do not influence enough in predicting real proportion to the true value, but rather to the limit that can be entered in the input interface. On the other hand, the HGPCa consideration includes both Gleason≥7 and clinical stage≥T2b, together or separately. Consequently, it could have exaggerated the estimated HGPCa. In addition, both PCPTRC 2.0 as well as ERSPC-RC 4 in its estimate of HGPCa have not included the post-ISUP 2005 criteria with the usual migration of some Gleason 6 to current Gleason 7. There is no limitation for 4KsT regarding the values of the variables, the prostate Bx is at least 10 cylinders and the histological interpretation criteria are post-ISUP 2005 since there are biopsy cases between 2013 and 2014. In any case, the most contemporary series and indication criteria according to the clinical practice routine are potentially more reproducible and close to our current development. These facts could have conditioned that in these patients the range of probabilities seems to be more comprehensive and individualized in the case of 4KsT than in the other models (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>).However, we must take into account that these forecasting models have to fulfill two purposes. On the one hand, assign each patient the individual probability of event, the closest to the real one, which corresponds to a good calibration.<a class="elsevierStyleCrossRef" href="#bib0175">16</a> On the other hand, these models must be good discriminatory and classificatory elements because our objective really is to know whether our patient is a candidate for Bx if he has a high enough probability of HGPCa according to each model. In this sense, the models can generate different probabilities of HGPCa to a single subject, as it occurs in reality, which there are some of them even more adjusted to reality than others. But if a specific cut-off point for each model is able to identify optimally patients with or without HGPCa, this would be sufficient, and even a priority in the results, even if the odds are not realistic enough.We explored the distribution of the probabilities of the various models (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>) in this way, as well as for the classification given to each model according to different cut-offs (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>), and especially the characteristics of errors of classification (Appendix B, supplementary material S1). Hence, a cut of 4% (>3%) as ERSPC-RC 4 suggests to apply in their model, achieves biopsy savings of over 50% when applying the model with volumetric by transrectal ultrasound and over 25% in volume estimation by digital rectal exam (ERSPC-RC 4+DRE), with 2 diagnostic losses in the case of ERSPC-RC 4 and none with ERSPC-RC 4+DRE. The PCPTRC 2.0 does not recommend any specific breakpoint for its use. Unlike in the 4KsT case, although Parekh et al. have not specifically defined a cut-off point, if we apply a cut-off of 9% (as we suggested at some point) we can identify all HGPCas in this series with a saving of biopsies of 21% of the total.Nevertheless, the desirable situation would be to have a method that would make it possible to make decisions in the sense of knowing the consequences of applying a cut-off and: first, to know how many diagnosed HGPCas would be lost with that cut-off, and then decide or discuss with our patients and their comorbidity factors the risk of making or not a biopsy and the expected benefit. And secondly, to know the number of Bx avoided with that cut-off to estimate in terms of management the saving of biopsies and analysis of cost-effectiveness of implementing these models. That does not go without the added financial cost in the case of 4KsT (cost of logistics, determination, and final count, €230<a class="elsevierStyleCrossRef" href="#bib0180">17</a>) or when performing transrectal ultrasound of the ERSPC-RC4 model (production cost, €37),<a class="elsevierStyleCrossRef" href="#bib0185">18</a> compared to savings in the non-performing Bx itself, with pathologic analysis fees of €42.<a class="elsevierStyleCrossRef" href="#bib0185">18</a> Along with it, we should add the variable cost when the patient is treated as an outpatient, for a short or long stay, has undergone local or general anesthesia or antibiotic regimen, among other variables. Depending on all that, the number of professionals involved should be considered, since it raises the price of the procedure, which could justify the initial outlay. Those objectives are adequately fulfilled with the CUC we have designed generically for these decision-making environments, which we apply to this reality showing the diagnostic losses of HGPCa (<a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a> blue line) and the simultaneous saving of Bx (<a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a> red line) for different cut-off points. The maximum value of the CUC as a helpful decision-making tool is enhanced when these are used in a dynamic way in which if the prediction obtained is changed, it is possible to obtain Bx savings accurately and immediately, as well as unwanted diagnostic losses of HGPCa.<a class="elsevierStyleCrossRef" href="#bib0190">19</a> Therefore, we have a helpful tool to support the decision making on the application of the markers so that, in a continuous range of points of different possible cut-offs, we immediately get clear information on their usefulness in clinical practice.The main limitation of this study is the sample size of 51 patients, so even if they reproduce the detection rate of PCa and HGPCa obtained by Parekh et al., they correspond to a pilot study in which the results, in the external validation of 4KsT, PCPTRC 2.0 and ERSPC-RC 4 as well as compared to the discrimination capacity between models (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>), or analysis to optimize the choice of cut-offs of clinical utility (<a class="elsevierStyleCrossRefs" href="#fig0015">Figs. 3–5</a>), need an external validation with a larger sample size.ConclusionsThe predictive models of pre-Bx multivariate analysis provide a good discrimination capacity when they estimate HGPCa. The 4KsT is a good classificatory model as a whole HGPCa, followed by ERSPC-RC 4 and PCPTRC 2.0. The CUC is a tool easy to implement and to interpret in order to select the breakpoints of clinical decision. This preliminary study should be interpreted with caution, and further research should be carried out with a larger sample size.FundingOPKO Health Europe has provided the logistics, analysis, and calculation of 4KsT in each of the 51 patients.Conflict of interestÁngel Borque Fernando, José Rubio Briones and Luis M. Esteban Escaño participated as speakers and scientific advisory of OPKO Health Europe." "textoCompletoSecciones" => array:1 [ "secciones" => array:12 [ 0 => array:3 [ "identificador" => "xres621203" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec635448" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres621204" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Introducción" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Material y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec635449" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:2 [ "identificador" => "sec0010" "titulo" => "Materials and methods" ] 6 => array:2 [ "identificador" => "sec0015" "titulo" => "Results" ] 7 => array:2 [ "identificador" => "sec0020" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0025" "titulo" => "Conclusions" ] 9 => array:2 [ "identificador" => "sec0030" "titulo" => "Funding" ] 10 => array:2 [ "identificador" => "sec0035" "titulo" => "Conflict of interest" ] 11 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2015-07-15" "fechaAceptado" => "2015-09-04" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec635448" "palabras" => array:10 [ 0 => "Prostate cancer" 1 => "High-grade prostate cancer" 2 => "4Kscore test" 3 => "Prostate Cancer Prevention Trial-Risk Calculator" 4 => "European Research Screening Prostate Cancer-Risk Calculator 4" 5 => "Clinical utility curves" 6 => "Predictive models" 7 => "Prostate biopsy" 8 => "Validation" 9 => "Clinical utility" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec635449" "palabras" => array:10 [ 0 => "Cáncer de próstata" 1 => "Cáncer de próstata de alto grado" 2 => "4Kscore Test" 3 => "Prostate Cancer Prevention Trial-Risk Calculator" 4 => "European Research Screening Prostate Cancer-Risk Calculator 4" 5 => "Curvas de utilidad clínica" 6 => "Modelos predictivos" 7 => "Biopsia de próstata" 8 => "Validación" 9 => "Utilidad clínica" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "IntroductionTo prevent the overdiagnosis and overtreatment of prostate cancer (PCa), therapeutic strategies have been established such as active surveillance and focal therapy, as well as methods for clarifying the diagnosis of high-grade prostate cancer (HGPCa) (defined as a Gleason score ≥7), such as multiparametric magnetic resonance imaging and new markers such as the 4Kscore test (4KsT).By means of a pilot study, we aim to test the ability of the 4KsT to identify HGPCa in prostate biopsies (Bx) and compare the test with other multivariate prognostic models such as the Prostate Cancer Prevention Trial Risk Calculator 2.0 (PCPTRC 2.0) and the European Research Screening Prostate Cancer Risk Calculator 4 (ERSPC-RC 4). Materials and methodsFifty-one patients underwent a prostate Bx according to standard clinical practice, with a minimum of 10 cores. The diagnosis of HGPCa was agreed upon by 4 uropathologists. We compared the predictions from the various models by using the Mann–Whitney U test, area under the ROC curve (AUC) (DeLong test), probability density function (PDF), box plots and clinical utility curves. ResultsForty-three percent of the patients had PC, and 23.5% had HGPCa. The medians of probability for the 4KsT, PCPTRC 2.0 and ERSPC-RC 4 were significantly different between the patients with HGPCa and those without HGPCa (p≤.022) and were more differentiated in the case of 4KsT (51.5% for HGPCa [25–75 percentile: 25–80.5%] vs. 16% [p 25–75: 8–26.5%] for non-HGPCa; p=.002). All models presented AUCs above 0.7, with no significant differences between any of them and 4KsT (p≥.20). The PDF and box plots showed good discriminative ability, especially in the ERSPC-RC 4 and 4KsT models. The utility curves showed how a cutoff of 9% for 4KsT identified all cases of HGPCa and provided a 22% savings in biopsies, which is similar to what occurs with the ERSPC-RC 4 models and a cutoff of 3%. ConclusionsThe assessed predictive models offer good discriminative ability for HGPCas in Bx. The 4KsT is a good classification model as a whole, followed by ERSPC-RC 4 and PCPTRC 2.0. The clinical utility curves help suggest cutoff points for clinical decisions: 9% for 4KsT and 3% for ERSPC-RC 4. This preliminary study should be interpreted with caution due to its limited sample size." "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "IntroducciónFrente al sobrediagnóstico y al sobretratamiento en cáncer de próstata (CaP) se establecen estrategias terapéuticas como la vigilancia activa o la terapia focal, o métodos para precisar el diagnóstico del CaP de alto grado (CaP-AG), Gleason≥7, como la resonancia magnética multiparamétrica o nuevos marcadores como el 4Kscore Test (4KsT).Es nuestro propósito testar mediante un estudio piloto la capacidad del 4KsT como identificador de CaP-AG (suma de Gleason≥7) en biopsia de próstata (Bx) y compararlo con otros modelos pronósticos multivariantes disponibles, como el Prostate Cancer Prevention Trial-Risk Calculator 2.0 (PCPTRC 2.0) y el European Research Screening Prostate Cancer-Risk Calculator 4 (ERSPC-RC 4). Material y métodosCincuenta y un pacientes sometidos a BxP según práctica clínica habitual, con un mínimo de 10 cilindros. Diagnóstico de CaP-AG consensuado por 4 uropatólogos. Comparación de las predicciones ofrecidas por los diferentes modelos mediante prueba U Mann-Whitney, áreas bajo la curva ROC (AUC) (test de DeLong), funciones de densidad de probabilidad, diagramas de caja y curvas de utilidad clínica (CUC). ResultadosUn 43% presentaron CaP y un 23,5% CaP-AG. Las medianas de probabilidad de 4KsT, PCPTRC 2.0 y ERSPC-RC 4 fueron significativamente diferentes entre los pacientes con CaP-AG y no CaP-AG (p≤0,022), siendo más diferenciadas en el caso de 4KsT (mediana en CaP-AG: 51,5% [percentil 25-75: 25-80,5%], frente a 16% [P 25-75: 8-26,5%] en no CaP-AG [p=0,002]). Todos los modelos mostraron AUC por encima de 0,7 sin diferencias significativas entre ninguno de ellos y 4KsT (p≥0,20). Las funciones de densidad de probabilidad y diagramas de caja muestran una buena capacidad discriminativa, especialmente en los modelos de ERSPC-RC 4 y 4KsT. Las CUC muestran como un punto de corte del 9% de 4KsT identifica a todos los CaP-AG y permite un ahorro del 22% de biopsias, similar a lo que ocurre con los modelos de ERSPC-RC 4 y un punto de corte del 3%. ConclusionesLos modelos predictivos evaluados ofrecen una buena capacidad de discriminación del CaP-AG en Bx. 4KsT es un buen modelo clasificatorio en su conjunto, seguido de ERSPC-RC 4 y PCPTRC 2.0. Las CUC permiten sugerir puntos de corte de decisión clínica: 9% para 4KsT y 3% en ERSPC-RC 4. Este estudio preliminar debe ser interpretado con cautela por su limitado tamaño muestral." "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Introducción" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Material y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] ] "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "Please cite this article as: Borque-Fernando Á, Esteban-Escaño LM, Rubio-Briones J, Lou-Mercadé AC, García-Ruiz R, Tejero-Sánchez A, et al. 4Kscore Test, Prostate Cancer Prevention Trial-Risk Calculator y European Research Screening Prostate-Risk Calculator en la predicción del cáncer de próstata de alto grado; estudio preliminar. Actas Urol Esp. 2016;40:155–163." ] ] "apendice" => array:1 [ 0 => array:1 [ "seccion" => array:1 [ 0 => array:4 [ "apendice" => "The following are the supplementary data to this article:<elsevierMultimedia ident="upi0005"></elsevierMultimedia>" "etiqueta" => "Appendix A" "titulo" => "Supplementary data" "identificador" => "sec0045" ] ] ] ] "multimedia" => array:9 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1196 "Ancho" => 3280 "Tamanyo" => 286168 ] ] "descripcion" => array:1 [ "en" => "(A) PDF for the chances of having HGPCa assigned by 4KsT vs. PCPT 2.0 without and with free PSA. (B) PDF for the chances of having HGPCa assigned by 4KsT vs. ERSPC-RC 4 with prostate volumetry with and without DRE." ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1202 "Ancho" => 3146 "Tamanyo" => 183665 ] ] "descripcion" => array:1 [ "en" => "Box-plot diagrams of the probabilities assigned by each model depending on whether they are patients with/without HGPCa (Green: patients with HGPCa; blue: patients without HGPCa)." ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Figure 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1381 "Ancho" => 3192 "Tamanyo" => 287681 ] ] "descripcion" => array:1 [ "en" => "Clinical utility curves for estimating HGPCa by 4Kscore Test. In the line of abscissas, the different cut-off points of clinical decision cut-off are located coinciding with the prediction offered by 4KsT; by drawing a line perpendicular to a given value of the cut-off point, this cuts the blue line (undiagnosed HGPCa) at a certain point, and drawing a horizontal line at the level of that point, when this is meets the y-axis, it indicates the percentage of HGPCa patients not diagnosed for this cut-off point. The same anterior perpendicular line cuts the red line at a point (avoided biopsies), and its translation to the y-axis indicates the number of biopsies avoided for a certain cut-off point. For example: a cut-off point corresponding to a probability of 9% HGPCa offered by 4KsT leads to a loss of 0% of HGPCa diagnosis in our series of 51 patients, and a saving of 22% of biopsies. Similarly, for a 50% chance that it would mean to biopsy anyone who is assigned a probability of HGPCa over 50% by 4KsT, this would mean that 50% of HGPCas in our series would not be diagnosed, and on the other hand 76% of Bx would be avoided." ] ] 3 => array:7 [ "identificador" => "fig0020" "etiqueta" => "Figure 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 1465 "Ancho" => 3248 "Tamanyo" => 372725 ] ] "descripcion" => array:1 [ "en" => "CUC for the models of PCPT 2.0." ] ] 4 => array:7 [ "identificador" => "fig0025" "etiqueta" => "Figure 5" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr5.jpeg" "Alto" => 1376 "Ancho" => 3114 "Tamanyo" => 345152 ] ] "descripcion" => array:1 [ "en" => "CUC for the models of ERSPC-RC 4." ] ] 5 => array:7 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:1 [ "tablatextoimagen" => array:2 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Mean \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Median \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P25 \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P75 \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Minimum \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Maximum \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Age (years) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">67.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">69 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">63 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">73 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">50 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">80 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">PSA (ng/ml) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">18.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.97 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.17 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.15 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.48 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">356.6 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Prostate volume (cc.) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">60.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">53 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">37 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">86 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">20 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">140 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1018605.png" ] ] 1 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Number of cases \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Percentage (%) \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Suspicious DRE \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">15 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">29.4 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Family history, first order \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">17.6 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">First biopsies \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">40 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">78.5 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1018607.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Characteristics of the studied patients." ] ] 6 => array:7 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " colspan="3" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">No HGPCa</th><th class="td" title="table-head " colspan="3" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">HGPCa</th><th class="td" title="table-head " align="" valign="top" scope="col"> \t\t\t\t\t\t\n \t\t\t\t</th></tr><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P-25 \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Median \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P-75 \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P-25 \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Median \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P-75 \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">4K-Score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">16 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">26.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">25 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">51.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">80.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.002 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">PCPT-2.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">15.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">20 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">29.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.008 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">PCTT-2.0/PSA-free \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">19 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">25 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">40.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.011 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">ERSPC-RC 4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">37 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">ERSPC-RC 4+DRE \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">14.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">42 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.022 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1018606.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Distribution of the probabilities of HGPCa assigned for each predictive model depending on whether it is a HGPCa or not." ] ] 7 => array:7 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">PCPT-2.0 \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">PCPT-2.0/PSA-L \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">ERSPC-RC 4 \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">ERSPC-RC 4+DRE \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">4KsT \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">AUC (95% CI) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.751 (0.610–0.861) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.748 (0.607–0.859) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.807 (0.672–0.904) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.717 (0.573–0.834) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.794 (0.657–0.894) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">PCPT-2.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.9627 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.5058 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.7026 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.3864 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">PCTT-2.0/PSA-free \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.5757 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.7924 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.5684 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">ERSPC-RC 4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.0267 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.8298 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">ERSPC-RC 4+DRE \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">p=0.2116 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1018604.png" ] ] ] ] "descripcion" => array:1 [ "en" => "AUC-ROC of the different models and comparison of the differences between them. 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Original article

A preliminary study of the ability of the 4Kscore test, the Prostate Cancer Prevention Trial-Risk Calculator and the European Research Screening Prostate-Risk Calculator for predicting high-grade prostate cancer

4Kscore Test, Prostate Cancer Prevention Trial-Risk Calculator y European Research Screening Prostate-Risk Calculator en la predicción del cáncer de próstata de alto grado; estudio preliminar

Á. Borque-Fernandoa,g,

Corresponding author

aborque@salud.aragon.es

Corresponding author.

, L.M. Esteban-Escañob,g, J. Rubio-Brionesc, A.C. Lou-Mercadéd, R. García-Ruiza, A. Tejero-Sáncheza, M.V. Muñoz-Riveroa, T. Cabañuz-Ploa, J. Alfaro-Torrese, I.M. Marquina-Ibáñeze, S. Hakim-Alonsoe, E. Mejía-Urbáeze, J. Gil-Fabraa, P. Gil-Martíneza, R. Ávarez-Alegrete, G. Sanzf,g, M.J. Gil-Sanza

a Servicio de Urología, Hospital Universitario Miguel Servet, Zaragoza, Spain

b Escuela Universitaria Politécnica La Almunia, Zaragoza, Spain

c Servicio de Urología, Instituto Valenciano de Oncología, Valencia, Spain

d Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain

e Servicio de Anatomía Patológica, Hospital Universitario Miguel Servet, Zaragoza, Spain

f Departamento de Métodos Estadísticos, Facultad de Ciencias, Universidad de Zaragoza, Zaragoza, Spain

g Grupo Consolidado de Investigación “Modelos Estocásticos”, Gobierno de Aragón, European Social Fund, Zaragoza, Spain

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    "titulo" => "A preliminary study of the ability of the 4Kscore test&#44; the Prostate Cancer Prevention Trial-Risk Calculator and the European Research Screening Prostate-Risk Calculator for predicting high-grade prostate cancer"
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        "titulo" => "4Kscore Test&#44; Prostate Cancer Prevention Trial-Risk Calculator y European Research Screening Prostate-Risk Calculator en la predicci&#243;n del c&#225;ncer de pr&#243;stata de alto grado&#59; estudio preliminar"
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          "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Box-plot diagrams of the probabilities assigned by each model depending on whether they are patients with&#47;without HGPCa &#40;Green&#58; patients with HGPCa&#59; blue&#58; patients without HGPCa&#41;&#46;</p>"
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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Overdiagnosis&#44; and consequently overtreatment&#44; is a reality in prostate cancer &#40;PCa&#41;&#46; Strategies such as active surveillance and focal therapy are used to compensate for the side effects from conventional treatments such as surgery or radiotherapy&#46; Such treatments are especially burdensome for patients whose tumor will not result in lethal or even morbid consequences&#46;<a class="elsevierStyleCrossRefs" href="#bib0100"><span class="elsevierStyleSup">1&#8211;3</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">The best strategy to prevent overtreatment is to avoid overdiagnosis&#46; If we were able to select as candidates for a prostate biopsy &#40;Bx&#41; only patients who have high risk tumours&#44; strategies such as active surveillance and even focal therapy may not be necessary&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">Aware of this reality&#44; the urological community is focusing its efforts on pre-biopsy identification of high-grade tumors with a Gleason score Bx<span class="elsevierStyleHsp" style=""></span>&#62;<span class="elsevierStyleHsp" style=""></span>7&#44; either by multiparameter magnetic resonance<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">4</span></a> or by new optimized markers for the diagnosis of high-grade prostate cancer &#40;HGPCa&#41; such as the <span class="elsevierStyleItalic">4Kscore Test</span> &#40;4KsT&#41;&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">The 4KsT is actually a weighted combination of seven variables&#44; 4 kallikrein &#40;total PSA&#44; free PSA&#44; intact PSA and hK2&#41; and 3 clinical variables &#40;age&#44; digital rectal examination and existence or not of previous biopsy&#41; that provides individual probability of harboring HGPCa&#46; Its ability to identify prebiopsy highly aggressive tumors makes it an attractive option&#46; It was recently validated in a US cohort study of more than 1000 patients with satisfactory results&#46;<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">5</span></a> However&#44; there are other multivariate models available&#44; and although they are developed in somewhat different clinical scenarios&#44; they also offer an individualized risk of PCa in general and HGPCa in particular&#46;<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">6</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">Our goal in this project is to conduct a preliminary comparative study of the capacity of 4KsT against <span class="elsevierStyleItalic">Prostate Cancer Prevention Trial Risk Calculator 2&#46;0</span> &#40;PCPTRC 2&#46;0&#41;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">7</span></a> and <span class="elsevierStyleItalic">European Randomized Study of Screening for Prostate Cancer</span>-<span class="elsevierStyleItalic">Risk Calculator 4</span> &#40;ERSPC-RC 4&#41;<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">8</span></a> to identify HGPCa&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Materials and methods</span><p id="par0030" class="elsevierStylePara elsevierViewall">We prospectively evaluated 51 patients undergoing prostate Bx on suspicion of PCa&#44; as indicated according to standard clinical practice&#46; The characteristics thereof are detailed in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0035" class="elsevierStylePara elsevierViewall">The Bx was performed through transrectal procedure with the number of cylinders set according to age and prostate volume&#44; according to the Vienna nomogram<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">9</span></a> modified with a minimum of 10 cylinders&#46; First&#44; the Bx was focused on the peripheral zone and included the transition zone in case of repeated Bx or ultrasound findings&#46; The Bx was practiced under sedation and prophylaxis with 400<span class="elsevierStyleHsp" style=""></span>mg&#47;IV single dose of ciprofloxacin in the run-Bx&#46; The uropathologist from the Department of Pathology of our hospital analyzed the Bx samples&#46; They reached a consensus&#44; and in each case&#44; the Gleason score of samples diagnosed with PCa were in accordance with the post-ISUP 2005<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">10</span></a> criteria&#46; They considered HGPCa to be the one with a Gleason score of Bx<span class="elsevierStyleHsp" style=""></span>&#8805;<span class="elsevierStyleHsp" style=""></span>7&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">Patients signed the informed consent form for the blood sample extraction and calculation of 4KsT and its study&#46; We collected demographic data necessary for the calculation of risk in the different models analyzed&#46;</p><p id="par0045" class="elsevierStylePara elsevierViewall">The risks of HGPCa were calculated according to PCPTRC 2&#46;0 with and without free PSA&#44; ERSPC-RC 4 &#40;as it is not mass screening population&#41; with prostate volume estimate&#44; either by rectal examination or by transrectal ultrasound as well as 4KsT by the reference laboratory&#46; For the computation of predictions of PCPTRC 2&#46;0 and ERSPC-RC 4&#44; we employed PSA and free PSA values obtained and used for calculating the 4KsT&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">We compared the predictions obtained by analyzing their distributions in cases of HGPCa vs&#46; non-HGPCa &#40;Mann&#8211;Whitney <span class="elsevierStyleItalic">U</span> test&#44; since it does not follow normal distribution&#41;&#44; areas under the ROC curve &#40;AUC&#41; &#40;compared with DeLong test&#41;&#44; probability density functions&#44; box-plots&#44; and curves of clinical utility&#46; Those analyses were performed with the statistical programming language Rv3&#46;1&#46;0&#46;</p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Results</span><p id="par0055" class="elsevierStylePara elsevierViewall">In the total of 51 biopsies performed&#44; 24 of them were identified as carriers of PCa &#40;47&#37;&#41;&#44; while 12 cases were HGPCa &#40;23&#46;5&#37;&#41;&#46; It is a very similar proportion to the study carried out by Parekh et al&#46;<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">5</span></a> Those results may be possibly influenced by the fact that it is a preselected population that undergoes routine clinical practice with a high risk of suffering from PCa based on potentially different restrictive indication criteria&#46; But they presumably match between the US multicenter and our center &#40;age&#44; PSA&#44; free PSA&#44; rectal exam&#44; family history&#44; prostate volume&#41;&#46;</p><p id="par0060" class="elsevierStylePara elsevierViewall">The median of probabilities assigned by 4KsT to the 12 patients with HGPCa was 51&#46;5&#37; &#40;25th&#8211;75th percentile&#58; 25&#8211;80&#46;5&#37;&#41; vs&#46; 16&#37; &#40;25th&#8211;75th percentile&#58; 8&#8211;26&#46;5&#37;&#41; in the 39 patients who had no HGPCa &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleHsp" style=""></span>0&#46;002&#41;&#46; Likewise&#44; PCPTRC-2&#46;0 with and without PSA-free and ERSPC-RC 4 with prostate volume estimated by rectal exam or transrectal ultrasonography showed significant differences between the distribution of probabilities of developing HGPCa between patients with and without HGPCa&#46; However&#44; there was not much gap between the two groups &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46;</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0065" class="elsevierStylePara elsevierViewall">Although all models show a good discrimination capacity with above 0&#46;7 AUC&#44; the highest AUC being the most relevant to the ERSPC-RC 4 with 0&#46;807 &#40;95&#37; CI&#58; 0&#46;672&#8211;0&#46;904&#41; followed by 4KsT with 0&#46;794 &#40;95&#37; CI&#58; 0&#46;657&#8211;0&#46;894&#41;&#44; there is no statistically significant differences between the AUCs of the models except in the comparison between the two models of ERCPC-RC 4 &#40;<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>&#41;&#46;</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><p id="par0070" class="elsevierStylePara elsevierViewall">As we did earlier in the analysis of prognostic models of organ-confined tumor&#44;<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">11</span></a> we have implemented the application of probability density functions &#40;PDFs&#41; to the visual comparison of the distribution of the probabilities offered by each model to patients with HGPCa and without HGPCa&#46; In general&#44; we can observe that except for the 4KsT case&#44; and subtly in the case of ERSPC RC4-DRE&#44; the different models assign significantly low probabilities &#40;below 50&#37;&#41; of developing HGPCa to patients that do not really suffer from that&#46; That shows a zero rate of false positives in probabilities&#44; both for PCPTRC as well as for ERSPC above 50&#37; &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>A&#41;&#46; And in the same PDF we can observe how 4KsT offers a growing distribution of HGPCa risk to patients who suffer from it&#46; Unlike other models&#44; except in the PCPTRC 2&#46;0 case that has a peak distribution in high probability&#44; the rest assign low probabilities of HGPCa to patients who really do have HGPCa&#46; Additionally&#44; those assigned probabilities in real HGPCa overlap to those accurate low probabilities of patients without HGPCa &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>B&#41;&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0075" class="elsevierStylePara elsevierViewall">In <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#44; and as a simplification of the density functions&#44; boxplots are created with the probabilities assigned by the models to the 51 patients of the study&#44; and they are represented according to whether they have or not HGPCa&#46; We can observe more clearly how the range of probabilities assigned by 4KsT to patients with HGPCa is broader than in the case of the other models&#46; In fact&#44; a patient who was biopsied with a PSA of 356&#46;6<span class="elsevierStyleHsp" style=""></span>ng&#47;ml &#40;59&#44; caucasian&#44; suspicious DRE&#44; first biopsy&#44; free PSA ratio&#58;18&#37;&#41; and which was confirmed as Gleason score 9 &#40;4<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>5&#41; was labeled as a probability of 95&#37; to have HGPCa by 4KsT&#44; vs&#46; a 76&#37; and 59&#37; of probability by ERSPC-RC 4-DRE and without volumetric estimation by DRE&#44; but by transrectal ultrasound&#46; And a probability of 79&#37; and surprisingly of 9&#37; if the PCPTRC 2&#46;0 is used with or without free-PSA&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0080" class="elsevierStylePara elsevierViewall">In any case&#44; this figure confirms how the probabilities assigned by 4KsT and ERSPC-RC 4 are the least overlapped between patients with HGPCa and without it&#46; On the one hand&#44; we observe in turn how a cut of 9&#37; with 4KsT identifies the total of 12 cases with HGPCa&#44; and excludes beneath it to just over 25&#37; of patients without HGPCa&#46; On the other hand&#44; the figure presents a 4&#37; of chance &#40;according to &#8220;&#62;3&#37;&#8221; suggested in the ERSPC-RC 4&#41;&#46; In this case of ERSPC-RC 4 this cutoff identifies almost all HGPCa with a similar saving of Bx with ERSPC- RC 4-DRE among non-HGPCa and 4KsT&#44; and which is still above with ERSPC-RC 4 without DRE&#46;</p><p id="par0085" class="elsevierStylePara elsevierViewall">As for the clinical utility of the models in a screening process&#44; we have also analyzed the number of biopsies avoided and undiagnosed HGPCa&#59; as well as their characteristics using different predictive models&#46; These models represent biopsy performance savings concretely&#44; as well as diagnostic losses of HGPCa and especially its characteristics according to the different models&#46; In general&#44; the findings reflect that diagnostic losses of HGPCa with 4KsT seem to be less relevant&#44; to a lesser extent than with the other models&#46; It is particularly striking how the diagnostics losses of Gleason 8 &#40;4<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>4&#41; with PCPTRC 2&#46;0 are more remarkable than with other models &#40;Appendix B&#44; supplementary material S1&#41;&#46;</p><p id="par0090" class="elsevierStylePara elsevierViewall">In order to get a clearer interpretation of the applicability of the marker&#44; we have implemented in this study our designed curves of clinical decision that offer the diagnostic performance of each marker according to different cutoffs&#46; The graphic exhibits two distinct curves both measured in percentage in the <span class="elsevierStyleItalic">Y</span> axis&#46; In the first one&#44; for each potential cut-off score&#44; we can see the percentage of biopsies avoided&#44; and in the second curve&#44; the percentage of high-grade cancers underdiagnosed for the same cut-off points&#46; Therefore&#44; the clinical utility curves &#40;CUC&#41; are particularly enlightening to implement the marker in daily clinical practice&#44; showing how&#44; for example&#44; a cut-off of probability of HGPCa of 9&#37; with 4KsT &#40;Bx indication only to 4KsT<span class="elsevierStyleHsp" style=""></span>&#8805;<span class="elsevierStyleHsp" style=""></span>9&#37;&#41; would identify correctly all patients with HGPCa&#46; That could bring about a BxP saving of 22&#37; in case the results of this preliminary study of 51 patients &#40;<a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a>&#41; are confirmed&#46; If we think it is permissible up to 16&#37; of high-grade cancers with delayed diagnosis&#44; the percentage of avoided biopsies would rise to 45&#37;&#46; Those same CUCs are very illustrative&#44; as well as for the other models of PCPTRC 2&#46;0 &#40;<a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a>&#41; and of ERSPC-RC 4 &#40;<a class="elsevierStyleCrossRef" href="#fig0025">Fig&#46; 5</a>&#41;&#46;</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia><elsevierMultimedia ident="fig0025"></elsevierMultimedia></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Discussion</span><p id="par0095" class="elsevierStylePara elsevierViewall">In the list of markers in PCa&#44; 4KsT has burst onto the scene as a specific marker for HGPCa&#46; However&#44; this is actually a weighted combination of 7 variables of which 4 are real biological markers&#44; serum markers in their conventional concept &#40;total PSA&#44; free-PSA&#44; PSA-intact&#44; human kallikrein 2 or hK2&#41; and the other 3 are clinical factors associated with the disease &#40;directly proportional in the case of age and DRE and vice versa in the case of the existence of prior Bx&#41;&#46; Those specific characteristics of 4KsT are an adjusted multivariate analysis model of logistic regression optimized by the existence or inexistence of HGPCa&#46; Other available markers that determine the existence of HGPCa can be compared to these properties&#46; However&#44; their initial analysis is not optimized for predicting the risk of HGPCa but rather for PCa &#40;specifically&#44; PCA3<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">12</span></a> and the <span class="elsevierStyleItalic">Prostate Health Index</span> &#40;PHI&#41;<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">13</span></a>&#41;&#46; The most consistent comparison is to other multivariate risk models having HGPCa&#44; such as those derived from the American study on the possible preventive nature of the finasteride in the appearance of PCa &#40;the PCPTRC 2&#46;0&#44; in versions of free PSA or without it&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">7</span></a> Additionally&#44; those coming from the European study on the impact of mortality of mass population screening in PCa &#40;<span class="elsevierStyleItalic">ERSPC-Risk Calculator</span>&#44; in its model 4 since our sample matches a population not subject to mass screening&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">8</span></a> They are both available <span class="elsevierStyleItalic">on-line</span>&#46;<a class="elsevierStyleCrossRefs" href="#bib0165"><span class="elsevierStyleSup">14&#44;15</span></a></p><p id="par0100" class="elsevierStylePara elsevierViewall">The 4KsT score has burst onto the scene of PCa diagnosis by focusing its prediction in estimating HGPCa&#46; Its clinical development began in the estimation of PCa&#44; where HGPCa was extrapolated as a combination of 4 kallikreins and age&#44; based on the mass screening series and sextant Bx of the ERSPC series and the evaluation criteria of prostate Bx pre-ISUP 2005&#46;<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">10</span></a> However&#44; the current clinical development and its design as 4KsT with 7 variables was developed on contemporary patients based on Bx criteria according to routine clinical practice in 26 US centers&#44; involving Bx of at least 10 cylinders and HGPCa interpretive criteria post-ISUP 2005&#46;<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">5</span></a></p><p id="par0105" class="elsevierStylePara elsevierViewall">According to the specific purpose of 4KsT and in light of current concerns to avoid unnecessary overdiagnosis and overtreatment&#44; we focused our analysis on patients with HGPCa &#40;Gleason score<span class="elsevierStyleHsp" style=""></span>&#8805;<span class="elsevierStyleHsp" style=""></span>7&#41; vs&#46; non-HGPCa &#40;including no PCa as well as PCa with Gleason score<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>7&#41;&#46;</p><p id="par0110" class="elsevierStylePara elsevierViewall">There are some potential limitations on the use of each of the three predictive models&#46; PCPT RC 2&#46;0 is built on patients undergoing sextant biopsy&#59; therefore&#44; we can expect a lower ability to identify PCa and HGPCa against the current Bx of at least 10 cylinders&#46; That can influence the odds offered by this model to be lower than reality since it is a model fitted to the lower identifying capacity of the sextant biopsy&#46; In the case of ERSPC-RC 4&#44; the model does not allow introducing prostate volumes higher than 110<span class="elsevierStyleHsp" style=""></span>cc or a total PSA higher than 50<span class="elsevierStyleHsp" style=""></span>ng&#47;ml&#44; as these are the highest limits that can be entered for computation&#46; And again&#44; the models are built on the identifying capacity of sextant biopsy&#44; which means the probabilities offered by the models may be lower than reality again&#46; In the case of prostate volume values&#44; or extreme PSA&#44; they do not influence enough in predicting real proportion to the true value&#44; but rather to the limit that can be entered in the input interface&#46; On the other hand&#44; the HGPCa consideration includes both Gleason<span class="elsevierStyleHsp" style=""></span>&#8805;<span class="elsevierStyleHsp" style=""></span>7 and clinical stage<span class="elsevierStyleHsp" style=""></span>&#8805;<span class="elsevierStyleHsp" style=""></span>T2b&#44; together or separately&#46; Consequently&#44; it could have exaggerated the estimated HGPCa&#46; In addition&#44; both PCPTRC 2&#46;0 as well as ERSPC-RC 4 in its estimate of HGPCa have not included the post-ISUP 2005 criteria with the usual migration of some Gleason 6 to current Gleason 7&#46; There is no limitation for 4KsT regarding the values of the variables&#44; the prostate Bx is at least 10 cylinders and the histological interpretation criteria are post-ISUP 2005 since there are biopsy cases between 2013 and 2014&#46; In any case&#44; the most contemporary series and indication criteria according to the clinical practice routine are potentially more reproducible and close to our current development&#46; These facts could have conditioned that in these patients the range of probabilities seems to be more comprehensive and individualized in the case of 4KsT than in the other models &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#41;&#46;</p><p id="par0115" class="elsevierStylePara elsevierViewall">However&#44; we must take into account that these forecasting models have to fulfill two purposes&#46; On the one hand&#44; assign each patient the individual probability of event&#44; the closest to the real one&#44; which corresponds to a good calibration&#46;<a class="elsevierStyleCrossRef" href="#bib0175"><span class="elsevierStyleSup">16</span></a> On the other hand&#44; these models must be good discriminatory and classificatory elements because our objective really is to know whether our patient is a candidate for Bx if he has a high enough probability of HGPCa according to each model&#46; In this sense&#44; the models can generate different probabilities of HGPCa to a single subject&#44; as it occurs in reality&#44; which there are some of them even more adjusted to reality than others&#46; But if a specific cut-off point for each model is able to identify optimally patients with or without HGPCa&#44; this would be sufficient&#44; and even a priority in the results&#44; even if the odds are not realistic enough&#46;</p><p id="par0120" class="elsevierStylePara elsevierViewall">We explored the distribution of the probabilities of the various models &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41; in this way&#44; as well as for the classification given to each model according to different cut-offs &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#41;&#44; and especially the characteristics of errors of classification &#40;Appendix B&#44; supplementary material S1&#41;&#46; Hence&#44; a cut of 4&#37; &#40;&#62;3&#37;&#41; as ERSPC-RC 4 suggests to apply in their model&#44; achieves biopsy savings of over 50&#37; when applying the model with volumetric by transrectal ultrasound and over 25&#37; in volume estimation by digital rectal exam &#40;ERSPC-RC 4<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>DRE&#41;&#44; with 2 diagnostic losses in the case of ERSPC-RC 4 and none with ERSPC-RC 4<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>DRE&#46; The PCPTRC 2&#46;0 does not recommend any specific breakpoint for its use&#46; Unlike in the 4KsT case&#44; although Parekh et al&#46; have not specifically defined a cut-off point&#44; if we apply a cut-off of 9&#37; &#40;as we suggested at some point&#41; we can identify all HGPCas in this series with a saving of biopsies of 21&#37; of the total&#46;</p><p id="par0125" class="elsevierStylePara elsevierViewall">Nevertheless&#44; the desirable situation would be to have a method that would make it possible to make decisions in the sense of knowing the consequences of applying a cut-off and&#58; first&#44; <span class="elsevierStyleItalic">to know how many diagnosed HGPCas would be lost</span> with that cut-off&#44; and then decide or discuss with our patients and their comorbidity factors the risk of making or not a biopsy and the expected benefit&#46; And secondly&#44; <span class="elsevierStyleItalic">to know the number of Bx avoided</span> with that cut-off to estimate in terms of management the saving of biopsies and analysis of cost-effectiveness of implementing these models&#46; That does not go without the added financial cost in the case of 4KsT &#40;cost of logistics&#44; determination&#44; and final count&#44; &#8364;230<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">17</span></a>&#41; or when performing transrectal ultrasound of the ERSPC-RC4 model &#40;production cost&#44; &#8364;37&#41;&#44;<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">18</span></a> compared to savings in the non-performing Bx itself&#44; with pathologic analysis fees of &#8364;42&#46;<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">18</span></a> Along with it&#44; we should add the variable cost when the patient is treated as an outpatient&#44; for a short or long stay&#44; has undergone local or general anesthesia or antibiotic regimen&#44; among other variables&#46; Depending on all that&#44; the number of professionals involved should be considered&#44; since it raises the price of the procedure&#44; which could justify the initial outlay&#46; Those objectives are adequately fulfilled with the CUC we have designed generically for these decision-making environments&#44; which we apply to this reality showing the diagnostic losses of HGPCa &#40;<a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a> blue line&#41; and the simultaneous saving of Bx &#40;<a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a> red line&#41; for different cut-off points&#46; The maximum value of the CUC as a helpful decision-making tool is enhanced when these are used in a dynamic way in which if the prediction obtained is changed&#44; it is possible to obtain Bx savings accurately and immediately&#44; as well as unwanted diagnostic losses of HGPCa&#46;<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">19</span></a> Therefore&#44; we have a helpful tool to support the decision making on the application of the markers so that&#44; in a continuous range of points of different possible cut-offs&#44; we immediately get clear information on their usefulness in clinical practice&#46;</p><p id="par0130" class="elsevierStylePara elsevierViewall">The main limitation of this study is the sample size of 51 patients&#44; so even if they reproduce the detection rate of PCa and HGPCa obtained by Parekh et al&#46;&#44; they correspond to a pilot study in which the results&#44; in the external validation of 4KsT&#44; PCPTRC 2&#46;0 and ERSPC-RC 4 as well as compared to the discrimination capacity between models &#40;<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>&#41;&#44; or analysis to optimize the choice of cut-offs of clinical utility &#40;<a class="elsevierStyleCrossRefs" href="#fig0015">Figs&#46; 3&#8211;5</a>&#41;&#44; need an external validation with a larger sample size&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Conclusions</span><p id="par0135" class="elsevierStylePara elsevierViewall">The predictive models of pre-Bx multivariate analysis provide a good discrimination capacity when they estimate HGPCa&#46; The 4KsT is a good classificatory model as a whole HGPCa&#44; followed by ERSPC-RC 4 and PCPTRC 2&#46;0&#46; The CUC is a tool easy to implement and to interpret in order to select the breakpoints of clinical decision&#46; This preliminary study should be interpreted with caution&#44; and further research should be carried out with a larger sample size&#46;</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Funding</span><p id="par0140" class="elsevierStylePara elsevierViewall">OPKO Health Europe has provided the logistics&#44; analysis&#44; and calculation of 4KsT in each of the 51 patients&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Conflict of interest</span><p id="par0145" class="elsevierStylePara elsevierViewall">&#193;ngel Borque Fernando&#44; Jos&#233; Rubio Briones and Luis M&#46; Esteban Esca&#241;o participated as speakers and scientific advisory of OPKO Health Europe&#46;</p></span></span>"
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            0 => "Prostate cancer"
            1 => "High-grade prostate cancer"
            2 => "4Kscore test"
            3 => "Prostate Cancer Prevention Trial-Risk Calculator"
            4 => "European Research Screening Prostate Cancer-Risk Calculator 4"
            5 => "Clinical utility curves"
            6 => "Predictive models"
            7 => "Prostate biopsy"
            8 => "Validation"
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            1 => "C&#225;ncer de pr&#243;stata de alto grado"
            2 => "4Kscore Test"
            3 => "Prostate Cancer Prevention Trial-Risk Calculator"
            4 => "European Research Screening Prostate Cancer-Risk Calculator 4"
            5 => "Curvas de utilidad cl&#237;nica"
            6 => "Modelos predictivos"
            7 => "Biopsia de pr&#243;stata"
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        "titulo" => "Abstract"
        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">To prevent the overdiagnosis and overtreatment of prostate cancer &#40;PCa&#41;&#44; therapeutic strategies have been established such as active surveillance and focal therapy&#44; as well as methods for clarifying the diagnosis of high-grade prostate cancer &#40;HGPCa&#41; &#40;defined as a Gleason score &#8805;7&#41;&#44; such as multiparametric magnetic resonance imaging and new markers such as the 4Kscore test &#40;4KsT&#41;&#46;</p><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">By means of a pilot study&#44; we aim to test the ability of the 4KsT to identify HGPCa in prostate biopsies &#40;Bx&#41; and compare the test with other multivariate prognostic models such as the Prostate Cancer Prevention Trial Risk Calculator 2&#46;0 &#40;PCPTRC 2&#46;0&#41; and the European Research Screening Prostate Cancer Risk Calculator 4 &#40;ERSPC-RC 4&#41;&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Materials and methods</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Fifty-one patients underwent a prostate Bx according to standard clinical practice&#44; with a minimum of 10 cores&#46; The diagnosis of HGPCa was agreed upon by 4 uropathologists&#46; We compared the predictions from the various models by using the Mann&#8211;Whitney <span class="elsevierStyleItalic">U</span> test&#44; area under the ROC curve &#40;AUC&#41; &#40;DeLong test&#41;&#44; probability density function &#40;PDF&#41;&#44; box plots and clinical utility curves&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Forty-three percent of the patients had PC&#44; and 23&#46;5&#37; had HGPCa&#46; The medians of probability for the 4KsT&#44; PCPTRC 2&#46;0 and ERSPC-RC 4 were significantly different between the patients with HGPCa and those without HGPCa &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#8804;<span class="elsevierStyleHsp" style=""></span>&#46;022&#41; and were more differentiated in the case of 4KsT &#40;51&#46;5&#37; for HGPCa &#91;25&#8211;75 percentile&#58; 25&#8211;80&#46;5&#37;&#93; vs&#46; 16&#37; &#91;<span class="elsevierStyleItalic">p</span> 25&#8211;75&#58; 8&#8211;26&#46;5&#37;&#93; for non-HGPCa&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#46;002&#41;&#46; All models presented AUCs above 0&#46;7&#44; with no significant differences between any of them and 4KsT &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#8805;<span class="elsevierStyleHsp" style=""></span>&#46;20&#41;&#46; The PDF and box plots showed good discriminative ability&#44; especially in the ERSPC-RC 4 and 4KsT models&#46; The utility curves showed how a cutoff of 9&#37; for 4KsT identified all cases of HGPCa and provided a 22&#37; savings in biopsies&#44; which is similar to what occurs with the ERSPC-RC 4 models and a cutoff of 3&#37;&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">The assessed predictive models offer good discriminative ability for HGPCas in Bx&#46; The 4KsT is a good classification model as a whole&#44; followed by ERSPC-RC 4 and PCPTRC 2&#46;0&#46; The clinical utility curves help suggest cutoff points for clinical decisions&#58; 9&#37; for 4KsT and 3&#37; for ERSPC-RC 4&#46; This preliminary study should be interpreted with caution due to its limited sample size&#46;</p></span>"
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      "es" => array:3 [
        "titulo" => "Resumen"
        "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Introducci&#243;n</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Frente al sobrediagn&#243;stico y al sobretratamiento en c&#225;ncer de pr&#243;stata &#40;CaP&#41; se establecen estrategias terap&#233;uticas como la vigilancia activa o la terapia focal&#44; o m&#233;todos para precisar el diagn&#243;stico del CaP de alto grado &#40;CaP-AG&#41;&#44; Gleason<span class="elsevierStyleHsp" style=""></span>&#8805;<span class="elsevierStyleHsp" style=""></span>7&#44; como la resonancia magn&#233;tica multiparam&#233;trica o nuevos marcadores como el <span class="elsevierStyleItalic">4Kscore Test</span> &#40;4KsT&#41;&#46;</p><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Es nuestro prop&#243;sito testar mediante un estudio piloto la capacidad del 4KsT como identificador de CaP-AG &#40;suma de Gleason<span class="elsevierStyleHsp" style=""></span>&#8805;<span class="elsevierStyleHsp" style=""></span>7&#41; en biopsia de pr&#243;stata &#40;Bx&#41; y compararlo con otros modelos pron&#243;sticos multivariantes disponibles&#44; como el <span class="elsevierStyleItalic">Prostate Cancer Prevention Trial-Risk Calculator 2&#46;0</span> &#40;PCPTRC 2&#46;0&#41; y el <span class="elsevierStyleItalic">European Research Screening Prostate Cancer-Risk Calculator 4</span> &#40;ERSPC-RC 4&#41;&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Material y m&#233;todos</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Cincuenta y un pacientes sometidos a BxP seg&#250;n pr&#225;ctica cl&#237;nica habitual&#44; con un m&#237;nimo de 10 cilindros&#46; Diagn&#243;stico de CaP-AG consensuado por 4 uropat&#243;logos&#46; Comparaci&#243;n de las predicciones ofrecidas por los diferentes modelos mediante prueba U Mann-Whitney&#44; &#225;reas bajo la curva ROC &#40;AUC&#41; &#40;test de DeLong&#41;&#44; funciones de densidad de probabilidad&#44; diagramas de caja y curvas de utilidad cl&#237;nica &#40;CUC&#41;&#46;</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Un 43&#37; presentaron CaP y un 23&#44;5&#37; CaP-AG&#46; Las medianas de probabilidad de 4KsT&#44; PCPTRC 2&#46;0 y ERSPC-RC 4 fueron significativamente diferentes entre los pacientes con CaP-AG y no CaP-AG &#40;p<span class="elsevierStyleHsp" style=""></span>&#8804;<span class="elsevierStyleHsp" style=""></span>0&#44;022&#41;&#44; siendo m&#225;s diferenciadas en el caso de 4KsT &#40;mediana en CaP-AG&#58; 51&#44;5&#37; &#91;percentil 25-75&#58; 25-80&#44;5&#37;&#93;&#44; frente a 16&#37; &#91;P 25-75&#58; 8-26&#44;5&#37;&#93; en no CaP-AG &#91;p<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#44;002&#93;&#41;&#46; Todos los modelos mostraron AUC por encima de 0&#44;7 sin diferencias significativas entre ninguno de ellos y 4KsT &#40;p<span class="elsevierStyleHsp" style=""></span>&#8805;<span class="elsevierStyleHsp" style=""></span>0&#44;20&#41;&#46; Las funciones de densidad de probabilidad y diagramas de caja muestran una buena capacidad discriminativa&#44; especialmente en los modelos de ERSPC-RC 4 y 4KsT&#46; Las CUC muestran como un punto de corte del 9&#37; de 4KsT identifica a todos los CaP-AG y permite un ahorro del 22&#37; de biopsias&#44; similar a lo que ocurre con los modelos de ERSPC-RC 4 y un punto de corte del 3&#37;&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Los modelos predictivos evaluados ofrecen una buena capacidad de discriminaci&#243;n del CaP-AG en Bx&#46; 4KsT es un buen modelo clasificatorio en su conjunto&#44; seguido de ERSPC-RC 4 y PCPTRC 2&#46;0&#46; Las CUC permiten sugerir puntos de corte de decisi&#243;n cl&#237;nica&#58; 9&#37; para 4KsT y 3&#37; en ERSPC-RC 4&#46; Este estudio preliminar debe ser interpretado con cautela por su limitado tama&#241;o muestral&#46;</p></span>"
        "secciones" => array:4 [
          0 => array:2 [
            "identificador" => "abst0025"
            "titulo" => "Introducci&#243;n"
          ]
          1 => array:2 [
            "identificador" => "abst0030"
            "titulo" => "Material y m&#233;todos"
          ]
          2 => array:2 [
            "identificador" => "abst0035"
            "titulo" => "Resultados"
          ]
          3 => array:2 [
            "identificador" => "abst0040"
            "titulo" => "Conclusiones"
          ]
        ]
      ]
    ]
    "NotaPie" => array:1 [
      0 => array:2 [
        "etiqueta" => "&#9734;"
        "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Please cite this article as&#58; Borque-Fernando &#193;&#44; Esteban-Esca&#241;o LM&#44; Rubio-Briones J&#44; Lou-Mercad&#233; AC&#44; Garc&#237;a-Ruiz R&#44; Tejero-S&#225;nchez A&#44; et al&#46; 4Kscore Test&#44; Prostate Cancer Prevention Trial-Risk Calculator y European Research Screening Prostate-Risk Calculator en la predicci&#243;n del c&#225;ncer de pr&#243;stata de alto grado&#59; estudio preliminar&#46; Actas Urol Esp&#46; 2016&#59;40&#58;155&#8211;163&#46;</p>"
      ]
    ]
    "apendice" => array:1 [
      0 => array:1 [
        "seccion" => array:1 [
          0 => array:4 [
            "apendice" => "<p id="par0155" class="elsevierStylePara elsevierViewall">The following are the supplementary data to this article&#58;<elsevierMultimedia ident="upi0005"></elsevierMultimedia></p>"
            "etiqueta" => "Appendix A"
            "titulo" => "Supplementary data"
            "identificador" => "sec0045"
          ]
        ]
      ]
    ]
    "multimedia" => array:9 [
      0 => array:7 [
        "identificador" => "fig0005"
        "etiqueta" => "Figure 1"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr1.jpeg"
            "Alto" => 1196
            "Ancho" => 3280
            "Tamanyo" => 286168
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">&#40;A&#41; PDF for the chances of having HGPCa assigned by 4KsT vs&#46; PCPT 2&#46;0 without and with free PSA&#46; &#40;B&#41; PDF for the chances of having HGPCa assigned by 4KsT vs&#46; ERSPC-RC 4 with prostate volumetry with and without DRE&#46;</p>"
        ]
      ]
      1 => array:7 [
        "identificador" => "fig0010"
        "etiqueta" => "Figure 2"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr2.jpeg"
            "Alto" => 1202
            "Ancho" => 3146
            "Tamanyo" => 183665
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Box-plot diagrams of the probabilities assigned by each model depending on whether they are patients with&#47;without HGPCa &#40;Green&#58; patients with HGPCa&#59; blue&#58; patients without HGPCa&#41;&#46;</p>"
        ]
      ]
      2 => array:7 [
        "identificador" => "fig0015"
        "etiqueta" => "Figure 3"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr3.jpeg"
            "Alto" => 1381
            "Ancho" => 3192
            "Tamanyo" => 287681
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Clinical utility curves for estimating HGPCa by 4Kscore Test&#46; In the line of abscissas&#44; the different cut-off points of clinical decision cut-off are located coinciding with the prediction offered by 4KsT&#59; by drawing a line perpendicular to a given value of the cut-off point&#44; this cuts the blue line &#40;undiagnosed HGPCa&#41; at a certain point&#44; and drawing a horizontal line at the level of that point&#44; when this is meets the <span class="elsevierStyleItalic">y</span>-axis&#44; it indicates the percentage of HGPCa patients not diagnosed for this cut-off point&#46; The same anterior perpendicular line cuts the red line at a point &#40;avoided biopsies&#41;&#44; and its translation to the <span class="elsevierStyleItalic">y</span>-axis indicates the number of biopsies avoided for a certain cut-off point&#46; For example&#58; a cut-off point corresponding to a probability of 9&#37; HGPCa offered by 4KsT leads to a loss of 0&#37; of HGPCa diagnosis in our series of 51 patients&#44; and a saving of 22&#37; of biopsies&#46; Similarly&#44; for a 50&#37; chance that it would mean to biopsy anyone who is assigned a probability of HGPCa over 50&#37; by 4KsT&#44; this would mean that 50&#37; of HGPCas in our series would not be diagnosed&#44; and on the other hand 76&#37; of Bx would be avoided&#46;</p>"
        ]
      ]
      3 => array:7 [
        "identificador" => "fig0020"
        "etiqueta" => "Figure 4"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr4.jpeg"
            "Alto" => 1465
            "Ancho" => 3248
            "Tamanyo" => 372725
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">CUC for the models of PCPT 2&#46;0&#46;</p>"
        ]
      ]
      4 => array:7 [
        "identificador" => "fig0025"
        "etiqueta" => "Figure 5"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr5.jpeg"
            "Alto" => 1376
            "Ancho" => 3114
            "Tamanyo" => 345152
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">CUC for the models of ERSPC-RC 4&#46;</p>"
        ]
      ]
      5 => array:7 [
        "identificador" => "tbl0005"
        "etiqueta" => "Table 1"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "tabla" => array:1 [
          "tablatextoimagen" => array:2 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head  " align="" valign="top" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Median&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P25&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P75&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Minimum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Age &#40;years&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">67&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">69&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">63&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">73&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">50&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">80&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">PSA &#40;ng&#47;ml&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">18&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;97&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;17&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">9&#46;15&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;48&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">356&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Prostate volume &#40;cc&#46;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">60&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">53&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">37&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">86&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">20&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">140&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab1018605.png"
              ]
            ]
            1 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head  " align="" valign="top" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Number of cases&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Percentage &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Suspicious DRE&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">15&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">29&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Family history&#44; first order&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">17&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">First biopsies&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">40&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">78&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab1018607.png"
              ]
            ]
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">Characteristics of the studied patients&#46;</p>"
        ]
      ]
      6 => array:7 [
        "identificador" => "tbl0010"
        "etiqueta" => "Table 2"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "tabla" => array:1 [
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head  " align="" valign="top" scope="col">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " colspan="3" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">No HGPCa</th><th class="td" title="table-head  " colspan="3" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">HGPCa</th><th class="td" title="table-head  " align="" valign="top" scope="col">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th></tr><tr title="table-row"><th class="td" title="table-head  " align="" valign="top" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P-25&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Median&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P-75&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P-25&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Median&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P-75&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="" valign="top" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">4K-Score&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">16&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">26&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">25&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">51&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">80&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;002&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">PCPT-2&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">15&#46;5&nbsp;\t\t\t\t\t\t\n
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