Computer-aided analysis of transrectal ultrasound images of the prostate | Actas Urológicas Españolas (English Edition)

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Gómez-Ferrer, S. Arlandis" "autores" => array:2 [ 0 => array:4 [ "nombre" => "Á." "apellidos" => "Gómez-Ferrer" "email" => array:1 [ 0 => "dr.alvaro@gomez-ferrer.net" ] "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "¿" "identificador" => "cor0005" ] ] ] 1 => array:2 [ "nombre" => "S." "apellidos" => "Arlandis" ] ] "afiliaciones" => array:1 [ 0 => array:1 [ "entidad" => "Servicio de Urología, Hospital Universitario La Fe, Valencia, Spain" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Análisis de imagen asistido por ordenador en ecografía transrectal de próstata" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0015" "etiqueta" => "Figure 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1417 "Ancho" => 2583 "Tamanyo" => 213525 ] ] "descripcion" => array:1 [ "en" => "ROC curve obtained in computer lab from experiments with SGLDM rated by K-neighbors and hidden Markov's models." ] ] ] "textoCompleto" => "IntroductionDespite the gradual progress in image quality of the current transrectal ultrasound devices and the incorporation of new technologies, Doppler study, use of ultrasound contrast, and three-dimensional reconstruction, prostate cancer remains difficult to detect by ultrasound, especially in its earliest stages.Most authors find a high percentage of hypoechoic, very rarely hyperechoic cancers, and they all recognize the high prevalence of isoechoic tumors, undetectable by ultrasound, which can be between 20 and 50%.<a class="elsevierStyleCrossRefs" href="#bib0005">1–3</a> In order to minimize this diagnostic difficulty, the most frequent distribution of the disease has been studied to direct the punctures to the areas where it is most present, and different schemes have been proposed in terms of number and location of punctures, which are not to be exposed in this work. On the other hand, we could potentially improve the ability of ultrasound to provide images more suspicious of cancer in order to lead the punctures to them. With this aim, the vascular behavior of tumors has been studied by Doppler technology with all its variants,<a class="elsevierStyleCrossRef" href="#bib0020">4</a> including the use of ultrasound contrast,<a class="elsevierStyleCrossRefs" href="#bib0025">5,6</a> with and without Doppler, and more recently, three-dimensional ultrasound.<a class="elsevierStyleCrossRefs" href="#bib0035">7,8</a> In parallel with the above, it is also reasonable to think that there may be characteristic ultrasound patterns of prostate cancer and hidden from the visual capacity of the human eye, which can be identified by artificial intelligence processes. Under this work hypothesis, we present our study of transrectal ultrasound images of prostate cancer through image analysis techniques aided by computer in the department of Systems at the Polytechnic University of Valencia. Another possible approach of the seemingly hidden information provided by the ultrasound scanner is the study of the signal obtained by the different absorption of the emitted ultrasound wave, which is known as raw data, attenuation signal (backscattered), or radio frequency (RF). Later, we will present the most important work done with our same purpose by other groups, both through imaging study itself and through the study of RF signal.Material and methodBiopsies of 288 patients with clinical and/or analytical suspicion of prostate cancer were recorded digitally: stages cT1c and CT2, mean PSA 11.6 (median: 9.25, range: 1.2–66)ng/ml. Classical sextant biopsy was performed in most patients, with additional transition zone biopsy in voluminous prostates (>60cc) and second biopsies. The usual ultrasound data, prostate volume and transition zone, presence of suspicious nodules and description of general appearance and echogenicity were registered. In addition, each puncture alone described its echogenicity (hypo, hyper or isoechogenic), necessarily assigning one category or another, setting the hypoechogenicity as a benchmark for suspected cancer.In order to perform all the registered explorations, we had a Bruel & Kjaer model 3535 ultrasound scanner, which was coupled with a CD recorder and an external hard drive, at our disposal. We used a 7.5MHz transrectal probe model 8551. All registered biopsies were performed under the same scanning conditions, without changing frequency, speed, level and gain curve, or scale (scale: abscissa from 0 to 5.2c; Power: Low; Gain: 85% Res: 6; Rate: 10f/s). In all cases, we used the automatic punch biopsy model Microvasive® (Boston Scientific) 20mm 18 Gauge: we operated the digital capture of the images using a pedal device and for 10s the procedure was recorded at a speed of 5 frames per second. In the computer lab, three still images previous to the introduction of the needle were isolated from the recording of each puncture. In each of these images, a rectangular area corresponding to the biopsied prostate zone, identified by the introduction of the needle (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>), was identified as a region of interest (ROI). These images were divided into two working groups (sets), one used for learning (training set) of the system, and the other one to compare the diagnostic ability of the same (test set) with an equal ratio of tumor imaging in each group. There were two types of imaging study: construction of simple mapping vectors of gray levels in each of the pixels in the region of interest (simple gray map) and second-order statistical descriptors called Haralick's co-occurrence matrices or spatial gray level dependent matrices (SGLDM). These descriptors are widely used in pattern recognition and in the study of all kinds of images.<elsevierMultimedia ident="fig0005"></elsevierMultimedia>Different types of relationship of each pixel with its neighbors, at different angles and distances are described: immediately adjacent (distance=1) and intermediate (distance=2), and so on. Of the 16 originally described, in our imaging, we studied 11 descriptors that analyze different characteristics in the image: uniformity, homogeneity, contrast, variance, cumulative variance, differential variance, entropy, cumulative entropy, differential entropy, correlation, and cumulative average. The 256 gray values of each pixel were reduced to 20 by vector quantization and principal component analysis (PCA) to reduce the computational cost in the study of co-occurrence matrices. In each of these descriptors, 16 matrices were obtained, studying every pixel with its neighbors in one to four distances and in four different directions. Different window sizes were studied: from 8×8 to 25×25 pixels. The simple mapping study was performed in windows of 16×16 to 50×50 with the 256 original gray levels.<a class="elsevierStyleCrossRefs" href="#bib0045">9,10</a> With the result of these studies and in order to verify the correct identification of images of prostate cancer in the test set, two classification methods were used: the technique of “k-neighbors” or “nearest neighbors” (nearest neighbor [k-NN]) and the hidden Markov's models, which are also commonly used techniques in the identification of images. The diagnostic yield of all these experiments was assessed by determining the sensitivity (SE) and specificity (S), and the construction of ROC curves (Receiver Operator Characteristic). Finally, we conducted a simulation experiment using the results of our study on 408 recorded images of biopsies already performed, not in real time, on which the software developed by the team of the Polytechnic University of Valencia colored the most suspicious areas of cancer in the optimal ratio of sensitivity/specificity (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>). The images obtained this way were compared with the level of suspicion in the original image valued on a scale of 0–10, as interpreted by 4 sonographers. The calculation of the sensitivity and specificity of both interpretations was made with different level of suspicion of prostate cancer thresholds.<elsevierMultimedia ident="fig0010"></elsevierMultimedia>ResultsA total of 66 cases were diagnosed with prostate cancer, identifying cancer in 205 cylinders. The total of benign cylinders available for the study was 1370, both of patients with and without cancer. Of each individual puncture, three quality still images immediately prior to the introduction of the needle were isolated, so in the end, 4725 images were available for analysis; 4110 were benign and 615 malignant.The punctures described as hypoechoic or suspicious for cancer were 199 (12.6%) among a total of 1575 cylinders. Of the 199 hypoechoic images, 137 (68%) were benign cylinders, representing 10% of the 1370 benign cylinders; and 62 (32%) were malignant cylinders, being 30% of the 205 malignant cylinders. If we consider only this description of echogenicity as suspicious for malignancy, we obtained 30% sensitivity, 90% specificity, 37% positive predictive value, an 89% negative predictive value, and area under the curve (ROC curve) of 0.601.The diagnostic capacity of the system by means of co-occurrence matrices study and classification with k-neighbors was 60.1%, and 60% with classification using hidden Markov's models (<a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>). The study of simple gray vectors and classification with k-neighbors obtained an area under the ROC curve of 59.7% and classification by means of hidden Markov's models of 61.6% (<a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a>). The diagnostic capacity of each of the 4 sonographers who valued transrectal ultrasound images on an original gray scale is reflected in <a class="elsevierStyleCrossRefs" href="#tbl0005">Tables 1–4</a>, according to how different thresholds are assessed in the above-mentioned scale of 0–10 proposed to grade the level of suspicion of prostate cancer.<elsevierMultimedia ident="fig0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="tbl0020"></elsevierMultimedia>The calculation of area under the ROC curve in the interpretation of the original image on gray scale was 61, 60, 66 and 60%, respectively. His interpretation of probability of cancer with colored images by our system gave an area under the ROC curve of 63, 67, 64 and 63% in each of the 4 involved, respectively, as we can observe in <a class="elsevierStyleCrossRef" href="#fig0025">Fig. 5</a>.<elsevierMultimedia ident="fig0025"></elsevierMultimedia>DiscussionClassically, the observation of a less echogenic nodule in the peripheral area was defined as suspected prostate cancer; however, that description does not appear in most cases and even watching it, it is not pathognomonic. In our work, we found only 12.6% hypoechoic lesions, housing in the majority (68%) benign tissue. These data confirm the need to try to improve the diagnostic yield of prostate cancer by means of imaging techniques.As we will see, such promising initial results of other working groups have not been reflected in a subsequent clinical application of relevant benefit, and the vast majority of groups have not moved their studies to the development of software for image recognition for use in real time.In light of our results, we have not developed any software either. The computational cost that would be necessary to do so is great because the image would have to be analyzed and suspected areas identified in real time. In addition, our modest results would not justify this development, since we have not achieved a significant improvement regarding conventional ultrasound. We believe that this may be due to several factors: methodological and/or inherent to the disease. It might be that prostate cancer and its histological variants do not share an echotexture that may differ from the normal gland. Another problem we face when approaching this type of study is imperfect monitoring, as it is practically impossible to accurately identify the precise location of the cancer in the resulting image with the current technology. This monitoring can be carried out with the study of the prostatectomy piece and/or that of the punctures. In our case, we performed it through the histological study of the obtained cylinder. However, this may be affected to a greater or lesser extent, and the entire length of the cylinder rarely corresponds to carcinoma. Finally, we believe, and so seems to be observed in other works, that the study of the signal attenuation would provide more reliable and independent information of the ultrasound scanner used, the signal not having been processed to image, and that this line of work is the one that in the future may give better results.When we analyzed the experience of other centers, we appreciated that the image recognition system that initially had most impact was the one made by the Nijmegen group in Holland, called Automated Urologic Diagnostic Expert System (AUDEX). In their early work, they obtained an 80 and 88% sensitivity and specificity, respectively, after studying the image analyzing 5 co-occurrence matrices.<a class="elsevierStyleCrossRef" href="#bib0055">11</a> Subsequently, they presented a validation of results with the findings on prostatectomy, obtaining a 78% sensitivity and a 50% specificity, considering the existence of a tumor volume greater than 10% of the gland as a cut-off.<a class="elsevierStyleCrossRefs" href="#bib0060">12,13</a>In a more recent publication, the poor relationship of their AUDEX system with the histological confirmation on prostatectomy pieces in a larger number of patients was objectified, finding an 85% sensitivity, an 18% specificity, and a 58% diagnostic accuracy.<a class="elsevierStyleCrossRef" href="#bib0070">14</a> The University of Waterloo, in Ontario, Canada, conducted a study with similar methodology to ours and the AUDEX group, studying 4 matrices and simple mapping on gray scale and classifying the images using the k-neighbors technique, with a maximum diagnostic efficacy close to 90%.<a class="elsevierStyleCrossRef" href="#bib0075">15</a> However, they do not communicate how the supervision of their images is performed and there are no results of clinical application.Fair et al., from the MSKCC, together with the Riverside Research Institute of New York, combine the analysis of the RF signal with clinical parameters such as PSA, and get 80% correct classification of their images.<a class="elsevierStyleCrossRefs" href="#bib0080">16–19</a> At the University of Kiel, an applied technology called C-TRUS (Computer Transrectal Ultrasound) was developed through image analysis, reporting very good results in prostate cancer detection in 132 patients with previous negative biopsies.<a class="elsevierStyleCrossRefs" href="#bib0100">20,21</a> However, in the literature, we did not find any evidence of this technology being applied in recent years.The Seoul National University, in Korea, has worked with descriptors of textures similar to those used in our work, but adding clinical parameters such as location, morphology and contour of the prostate cancer. It is hardly surprising that they get 90% sensitivity and 96% specificity,<a class="elsevierStyleCrossRef" href="#bib0110">22</a> since they use the same images for the training and the test, which would invalidate their system for extrapolating results to other patients. Besides, the supervision of a region of interest is not performed by histological verification whatsoever. Despite the success of many of these working groups, none has managed to develop a software that helps real-time decision making for biopsy, and virtually all researchers have abandoned the development of a device for clinical application. The only exception to this is the technology called HistoScanning, created by Braeckman et al. with the Belgian company Advanced Medical Diagnostics. Based on studies of RF signal, they segment the ultrasound information in regions of interest of 0.04ml (r=0.89, p<0.001)<a class="elsevierStyleCrossRef" href="#bib0115">23</a> and, in a later publication, they demonstrate how their system can show small tumors, around 0.5ml.<a class="elsevierStyleCrossRef" href="#bib0120">24</a> This technology is now commercially available coupled with ultrasound devices Bruel & Kjaer.In summary, conventional ultrasound has its limitations in the early diagnosis of the disease, and it would be useful to identify apparently hidden cancer image patterns. In our work, we found no significant benefit to justify the development of software for image recognition. Currently, only the HistoScanning software is available for commercial use, although we are unaware of its benefit in daily clinical practice.FundingWork funded by grant to the cabinet Foundation for Health Research (FHR).Conflict of interestThe authors have no conflicts of interest to declare." "textoCompletoSecciones" => array:1 [ "secciones" => array:11 [ 0 => array:2 [ "identificador" => "xres98386" "titulo" => array:5 [ 0 => "Abstract" 1 => "Introduction" 2 => "Material and methods" 3 => "Results" 4 => "Conclusions" ] ] 1 => array:2 [ "identificador" => "xpalclavsec85546" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "xres98385" "titulo" => array:5 [ 0 => "Resumen" 1 => "Introducción" 2 => "Materiales y método" 3 => "Resultados" 4 => "Conclusiones" ] ] 3 => array:2 [ "identificador" => "xpalclavsec85545" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:2 [ "identificador" => "sec0010" "titulo" => "Material and method" ] 6 => array:2 [ "identificador" => "sec0015" "titulo" => "Results" ] 7 => array:2 [ "identificador" => "sec0020" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0025" "titulo" => "Funding" ] 9 => array:2 [ "identificador" => "sec0030" "titulo" => "Conflict of interest" ] 10 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2010-09-28" "fechaAceptado" => "2011-02-13" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec85546" "palabras" => array:3 [ 0 => "Prostate cancer" 1 => "Computer-Aided Diagnosis" 2 => "Transrectal ultrasound" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec85545" "palabras" => array:3 [ 0 => "Cáncer de próstata" 1 => "Diagnóstico" 2 => "Ecografía transrectal" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:2 [ "titulo" => "Abstract" "resumen" => "IntroductionProstate cancer is usually diagnosed by transrectal ultrasound (TRUS) biopsy. Nevertheless, suspicious images are frequently not found. Imaging analysis studies aim to identify ultrasound patterns characteristic of apparently hidden conditions. Material and methodsWe digitally recorded 288 TRUS ultrasound guided transrectal biopsies and extracted 3 static images from the puncture-biopsy area. The extraction of the texture characteristics were obtained by “simple mapping” on a gray scale and spatial gray level dependence matrices (SGLDM), also known as Haralick's co-occurrence matrices, which study the relationship of each pixel and its neighbors. A pattern recognition software system was developed with two different classification methods: nearest neighbor (k-NN) and Markov's hidden models. Finally, a virtual experiment was carried out in which four urologists compared their diagnostic accuracy for prostate cancer with our system in 408 TRUS images, not in real time. ResultsThe diagnostic capacity (ROC curve) with the simple gray map study was 59.7% with nearest-neighbor classification and 61.6% with Markov's hidden models classification. The co-occurrence matrices showed an area under ROC curve of 60.1% and 60.0% with k-NN and Markov's hidden models classification, respectively. The virtual experiment was conducted with a simple gray map study and k-NN classification. The images processed by our system showed the following diagnostic accuracy: 63.3, 67, 64.3 and 63.7% compared to 61.7, 60.5, 66.2 and 60.7% with the original image. ConclusionsOur pattern recognition system for prostate cancer TRUS images has a limited, yet stable, accuracy." ] "es" => array:2 [ "titulo" => "Resumen" "resumen" => "IntroducciónEl método de diagnóstico de elección del cáncer de próstata (CP) es la biopsia transrectal guiada ecográficamente. Sin embargo, es frecuente no objetivar imágenes sospechosas. Los estudios de análisis de imagen pretenden identificar patrones ecográficos propios de una patología aparentemente ocultos. Materiales y métodoRegistramos digitalmente 288 biopsias transrectales ecoguiadas, de las que se aislaron imágenes estáticas de cada punción-biopsia para su análisis computarizado. Para ello se procedió a la extracción de características de textura mediante «mapeo simple» en escala de gris y «matrices espaciales dependientes del nivel de gris» o «matrices de coaparición», que estudian la relación de cada píxel con sus vecinos. Se desarrolló un sistema de «reconocimiento de formas» con dos métodos de clasificación: «técnica de k-vecinos» y «modelos ocultos de Markov». Finalmente realizamos una simulación del sistema con 4 ecografistas, comparando su capacidad diagnóstica en escala de gris con imágenes procesadas con nuestro sistema en 408 punciones grabadas, no en tiempo real. ResultadosLa capacidad diagnóstica (curva ROC) con mapeo simple fue de 59,7 y 61,6% con clasificación mediante k-vecinos y modelos ocultos de Markov, respectivamente. Las matrices de coaparición ofrecieron un área bajo la curva ROC de 60,1 y 60,0%. El experimento virtual se llevó a cabo mediante «mapeo simple» y clasificación con «k-vecinos», otorgando una capacidad diagnóstica en cada urólogo de 63,3, 67,0, 64,3 y 63,7% frente a 61,7, 60,5, 66,2 y 60,7% conseguidas con la imagen original. ConclusionesLa utilización de nuestro método de análisis de imagen tiene una capacidad limitada, aunque estable, en la detección de áreas prostáticas cancerígenas." ] ] "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "Please cite this article as: Gómez-Ferrer A, Arlandis S. Análisis de imagen asistido por ordenador en ecografía transrectal de próstata. Actas Urol Esp. 2011;35:404–13." ] ] "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" => 1583 "Ancho" => 2833 "Tamanyo" => 266666 ] ] "descripcion" => array:1 [ "en" => "Identification of the region of interest of the ultrasound image corresponding to the prostate tissue removed in each puncture." ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1729 "Ancho" => 2160 "Tamanyo" => 203642 ] ] "descripcion" => array:1 [ "en" => "Suspicious areas identified by the system in red." ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Figure 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1417 "Ancho" => 2583 "Tamanyo" => 213525 ] ] "descripcion" => array:1 [ "en" => "ROC curve obtained in computer lab from experiments with SGLDM rated by K-neighbors and hidden Markov's models." ] ] 3 => array:7 [ "identificador" => "fig0020" "etiqueta" => "Figure 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 1694 "Ancho" => 2896 "Tamanyo" => 256208 ] ] "descripcion" => array:1 [ "en" => "ROC curve obtained in computer lab from the experiments with simple mapping with classification by K-neighbors and hidden Markov's models." ] ] 4 => array:7 [ "identificador" => "fig0025" "etiqueta" => "Figure 5" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:2 [ 0 => array:1 [ "imagen" => "gr5a.jpeg" ] 1 => array:1 [ "imagen" => "gr5b.jpeg" ] ] "descripcion" => array:1 [ "en" => "ROC curve of Urologists’ virtual experiment 1–4." ] ] 5 => array:7 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "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"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Threshold \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Hits \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">PV \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">NV \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">PF \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">NF \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Sensibility \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Specificity \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Classification obtained on gray scale</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">53.4% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">162 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">148 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">42 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n 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valign="\n \t\t\t\t\ttop\n \t\t\t\t">37 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">192 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">167 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18.1% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">94.1% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">51.5% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">202 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">196 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.9% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">99.0% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Classification obtained with the computer system</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">57.6% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">176 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">59 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">145 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">28 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">86.3% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">28.9% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">59.3% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">130 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">112 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">92 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">74 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">63.7% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">54.9% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58.1% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">97 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">140 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">64 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">107 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">47.5% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">68.6% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58.8% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">61 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">179 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">143 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">29.9% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">87.7% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">52.9% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">195 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">183 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10.3% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">95.6% \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab183656.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Results of urologist 1." ] ] 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"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Threshold \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Hits \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">PV \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">NV \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">PF \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">NF \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Sensibility \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Specificity \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Classification obtained on gray scale</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">51.5% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">181 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">29 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">175 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">88.7% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14.2% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">60.3% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">92 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">154 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">50 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">112 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">45.1% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">75.5% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">55.6% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">44 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">183 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">160 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21.6% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">89.7% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">52.7% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">201 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">190 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.9% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">98.5% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">50.5% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">204 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">202 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.0% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">100% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Classification obtained with the computer system</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">52.5% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">203 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">193 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">99.5% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.4% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">64.2% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">134 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">128 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">76 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">70 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">65.7% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">62.7% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58.8% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">52 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">188 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">16 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">152 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25.5% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">92.2% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">51.7% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">197 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">190 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.9% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">96.6% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">49.8% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">203 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">204 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">99.5% \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab183658.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Results of urologist 2." ] ] 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"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Threshold \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Hits \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">PV \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">NV \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">PF \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">NF \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Sensibility \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Specificity \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Classification obtained on gray scale</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">51.7% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">191 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">20 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">184 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">93.6% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9.8% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">61.8% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">153 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">99 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">105 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">51 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">75.0% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">48.5% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">62.0% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">132 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">121 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">83 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">72 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">64.7% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">59.3% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">61.0% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">87 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">162 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">42 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">117 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">42.6% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">79.4% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">57.6% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">35 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">200 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">169 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">17.2% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">98.0% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Classification obtained with the computer system</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">51.5% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">201 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">195 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">98.5% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.4% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58.3% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">156 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">82 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">122 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">48 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">76.5% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">40.2% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">57.4% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">149 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">85 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">119 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">55 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">73.0% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">41.7% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">64.0% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">110 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">151 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">53 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">94 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">53.9% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">74.0% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">51.5% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">198 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">192 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.9% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">97.1% \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab183653.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Results of urologist 3." ] ] 8 => array:7 [ "identificador" => "tbl0020" "etiqueta" => "Table 4" "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"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Threshold \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Hits \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">PV \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">NV \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">PF \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">NF \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Sensibility \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Specificity \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Classification obtained on gray scale</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">53.4% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">188 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">30 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">174 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">16 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">92.2% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14.7% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58.1% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">119 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">118 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">86 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">85 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58.3% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">57.8% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58.8% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">92 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">148 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">112 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">45.1% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">72.5% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">52.7% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">20 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">195 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">184 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9.8% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">95.6% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">50.0% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">204 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">204 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">100% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Classification obtained with the computer system</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">55.6% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">184 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">43 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">161 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">20 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">90.2% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21.1% \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">61.5% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">138 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">113 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">91 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n 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Computer-aided analysis of transrectal ultrasound images of the prostate

Análisis de imagen asistido por ordenador en ecografía transrectal de próstata

Á. Gómez-Ferrer

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dr.alvaro@gomez-ferrer.net

Corresponding author.

, S. Arlandis

Servicio de Urología, Hospital Universitario La Fe, Valencia, Spain

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        "titulo" => "An&#225;lisis de imagen asistido por ordenador en ecograf&#237;a transrectal de pr&#243;stata"
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          "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">ROC curve obtained in computer lab from experiments with SGLDM rated by K-neighbors and hidden Markov&#39;s models&#46;</p>"
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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Despite the gradual progress in image quality of the current transrectal ultrasound devices and the incorporation of new technologies&#44; Doppler study&#44; use of ultrasound contrast&#44; and three-dimensional reconstruction&#44; prostate cancer remains difficult to detect by ultrasound&#44; especially in its earliest stages&#46;</p><p id="par0010" class="elsevierStylePara elsevierViewall">Most authors find a high percentage of hypoechoic&#44; very rarely hyperechoic cancers&#44; and they all recognize the high prevalence of isoechoic tumors&#44; undetectable by ultrasound&#44; which can be between 20 and 50&#37;&#46;<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#8211;3</span></a> In order to minimize this diagnostic difficulty&#44; the most frequent distribution of the disease has been studied to direct the punctures to the areas where it is most present&#44; and different schemes have been proposed in terms of number and location of punctures&#44; which are not to be exposed in this work&#46; On the other hand&#44; we could potentially improve the ability of ultrasound to provide images more suspicious of cancer in order to lead the punctures to them&#46; With this aim&#44; the vascular behavior of tumors has been studied by Doppler technology with all its variants&#44;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> including the use of ultrasound contrast&#44;<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#44;6</span></a> with and without Doppler&#44; and more recently&#44; three-dimensional ultrasound&#46;<a class="elsevierStyleCrossRefs" href="#bib0035"><span class="elsevierStyleSup">7&#44;8</span></a> In parallel with the above&#44; it is also reasonable to think that there may be characteristic ultrasound patterns of prostate cancer and hidden from the visual capacity of the human eye&#44; which can be identified by artificial intelligence processes&#46; Under this work hypothesis&#44; we present our study of transrectal ultrasound images of prostate cancer through image analysis techniques aided by computer in the department of Systems at the Polytechnic University of Valencia&#46; Another possible approach of the seemingly hidden information provided by the ultrasound scanner is the study of the signal obtained by the different absorption of the emitted ultrasound wave&#44; which is known as raw data&#44; attenuation signal &#40;backscattered&#41;&#44; or radio frequency &#40;RF&#41;&#46; Later&#44; we will present the most important work done with our same purpose by other groups&#44; both through imaging study itself and through the study of RF signal&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Material and method</span><p id="par0015" class="elsevierStylePara elsevierViewall">Biopsies of 288 patients with clinical and&#47;or analytical suspicion of prostate cancer were recorded digitally&#58; stages cT1c and CT2&#44; mean PSA 11&#46;6 &#40;median&#58; 9&#46;25&#44; range&#58; 1&#46;2&#8211;66&#41;<span class="elsevierStyleHsp" style=""></span>ng&#47;ml&#46; Classical sextant biopsy was performed in most patients&#44; with additional transition zone biopsy in voluminous prostates &#40;&#62;60<span class="elsevierStyleHsp" style=""></span>cc&#41; and second biopsies&#46; The usual ultrasound data&#44; prostate volume and transition zone&#44; presence of suspicious nodules and description of general appearance and echogenicity were registered&#46; In addition&#44; each puncture alone described its echogenicity &#40;hypo&#44; hyper or isoechogenic&#41;&#44; necessarily assigning one category or another&#44; setting the hypoechogenicity as a benchmark for suspected cancer&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">In order to perform all the registered explorations&#44; we had a Bruel &#38; Kjaer model 3535 ultrasound scanner&#44; which was coupled with a CD recorder and an external hard drive&#44; at our disposal&#46; We used a 7&#46;5<span class="elsevierStyleHsp" style=""></span>MHz transrectal probe model 8551&#46; All registered biopsies were performed under the same scanning conditions&#44; without changing frequency&#44; speed&#44; level and gain curve&#44; or scale &#40;scale&#58; abscissa from 0 to 5&#46;2<span class="elsevierStyleHsp" style=""></span>c&#59; Power&#58; Low&#59; Gain&#58; 85&#37; Res&#58; 6&#59; Rate&#58; 10<span class="elsevierStyleHsp" style=""></span>f&#47;s&#41;&#46; In all cases&#44; we used the automatic punch biopsy model Microvasive<span class="elsevierStyleSup">&#174;</span> &#40;Boston Scientific&#41; 20<span class="elsevierStyleHsp" style=""></span>mm 18 Gauge&#58; we operated the digital capture of the images using a pedal device and for 10<span class="elsevierStyleHsp" style=""></span>s the procedure was recorded at a speed of 5 frames per second&#46; In the computer lab&#44; three still images previous to the introduction of the needle were isolated from the recording of each puncture&#46; In each of these images&#44; a rectangular area corresponding to the biopsied prostate zone&#44; identified by the introduction of the needle &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41;&#44; was identified as a region of interest &#40;ROI&#41;&#46; These images were divided into two working groups &#40;sets&#41;&#44; one used for learning &#40;training set&#41; of the system&#44; and the other one to compare the diagnostic ability of the same &#40;test set&#41; with an equal ratio of tumor imaging in each group&#46; There were two types of imaging study&#58; construction of simple mapping vectors of gray levels in each of the pixels in the region of interest &#40;simple gray map&#41; and second-order statistical descriptors called Haralick&#39;s co-occurrence matrices or spatial gray level dependent matrices &#40;SGLDM&#41;&#46; These descriptors are widely used in pattern recognition and in the study of all kinds of images&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0025" class="elsevierStylePara elsevierViewall">Different types of relationship of each pixel with its neighbors&#44; at different angles and distances are described&#58; immediately adjacent &#40;distance<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>1&#41; and intermediate &#40;distance<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>2&#41;&#44; and so on&#46; Of the 16 originally described&#44; in our imaging&#44; we studied 11 descriptors that analyze different characteristics in the image&#58; uniformity&#44; homogeneity&#44; contrast&#44; variance&#44; cumulative variance&#44; differential variance&#44; entropy&#44; cumulative entropy&#44; differential entropy&#44; correlation&#44; and cumulative average&#46; The 256 gray values of each pixel were reduced to 20 by vector quantization and principal component analysis &#40;PCA&#41; to reduce the computational cost in the study of co-occurrence matrices&#46; In each of these descriptors&#44; 16 matrices were obtained&#44; studying every pixel with its neighbors in one to four distances and in four different directions&#46; Different window sizes were studied&#58; from 8<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>8 to 25<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>25 pixels&#46; The simple mapping study was performed in windows of 16<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>16 to 50<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>50 with the 256 original gray levels&#46;<a class="elsevierStyleCrossRefs" href="#bib0045"><span class="elsevierStyleSup">9&#44;10</span></a> With the result of these studies and in order to verify the correct identification of images of prostate cancer in the test set&#44; two classification methods were used&#58; the technique of &#8220;k-neighbors&#8221; or &#8220;nearest neighbors&#8221; &#40;nearest neighbor &#91;k-NN&#93;&#41; and the hidden Markov&#39;s models&#44; which are also commonly used techniques in the identification of images&#46; The diagnostic yield of all these experiments was assessed by determining the sensitivity &#40;SE&#41; and specificity &#40;S&#41;&#44; and the construction of ROC curves &#40;Receiver Operator Characteristic&#41;&#46; Finally&#44; we conducted a simulation experiment using the results of our study on 408 recorded images of biopsies already performed&#44; not in real time&#44; on which the software developed by the team of the Polytechnic University of Valencia colored the most suspicious areas of cancer in the optimal ratio of sensitivity&#47;specificity &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#41;&#46; The images obtained this way were compared with the level of suspicion in the original image valued on a scale of 0&#8211;10&#44; as interpreted by 4 sonographers&#46; The calculation of the sensitivity and specificity of both interpretations was made with different level of suspicion of prostate cancer thresholds&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Results</span><p id="par0030" class="elsevierStylePara elsevierViewall">A total of 66 cases were diagnosed with prostate cancer&#44; identifying cancer in 205 cylinders&#46; The total of benign cylinders available for the study was 1370&#44; both of patients with and without cancer&#46; Of each individual puncture&#44; three quality still images immediately prior to the introduction of the needle were isolated&#44; so in the end&#44; 4725 images were available for analysis&#59; 4110 were benign and 615 malignant&#46;</p><p id="par0035" class="elsevierStylePara elsevierViewall">The punctures described as hypoechoic or suspicious for cancer were 199 &#40;12&#46;6&#37;&#41; among a total of 1575 cylinders&#46; Of the 199 hypoechoic images&#44; 137 &#40;68&#37;&#41; were benign cylinders&#44; representing 10&#37; of the 1370 benign cylinders&#59; and 62 &#40;32&#37;&#41; were malignant cylinders&#44; being 30&#37; of the 205 malignant cylinders&#46; If we consider only this description of echogenicity as suspicious for malignancy&#44; we obtained 30&#37; sensitivity&#44; 90&#37; specificity&#44; 37&#37; positive predictive value&#44; an 89&#37; negative predictive value&#44; and area under the curve &#40;ROC curve&#41; of 0&#46;601&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">The diagnostic capacity of the system by means of co-occurrence matrices study and classification with k-neighbors was 60&#46;1&#37;&#44; and 60&#37; with classification using hidden Markov&#39;s models &#40;<a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a>&#41;&#46; The study of simple gray vectors and classification with k-neighbors obtained an area under the ROC curve of 59&#46;7&#37; and classification by means of hidden Markov&#39;s models of 61&#46;6&#37; &#40;<a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a>&#41;&#46; The diagnostic capacity of each of the 4 sonographers who valued transrectal ultrasound images on an original gray scale is reflected in <a class="elsevierStyleCrossRefs" href="#tbl0005">Tables 1&#8211;4</a>&#44; according to how different thresholds are assessed in the above-mentioned scale of 0&#8211;10 proposed to grade the level of suspicion of prostate cancer&#46;</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="tbl0020"></elsevierMultimedia><p id="par0045" class="elsevierStylePara elsevierViewall">The calculation of area under the ROC curve in the interpretation of the original image on gray scale was 61&#44; 60&#44; 66 and 60&#37;&#44; respectively&#46; His interpretation of probability of cancer with colored images by our system gave an area under the ROC curve of 63&#44; 67&#44; 64 and 63&#37; in each of the 4 involved&#44; respectively&#44; as we can observe in <a class="elsevierStyleCrossRef" href="#fig0025">Fig&#46; 5</a>&#46;</p><elsevierMultimedia ident="fig0025"></elsevierMultimedia></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Discussion</span><p id="par0050" class="elsevierStylePara elsevierViewall">Classically&#44; the observation of a less echogenic nodule in the peripheral area was defined as suspected prostate cancer&#59; however&#44; that description does not appear in most cases and even watching it&#44; it is not pathognomonic&#46; In our work&#44; we found only 12&#46;6&#37; hypoechoic lesions&#44; housing in the majority &#40;68&#37;&#41; benign tissue&#46; These data confirm the need to try to improve the diagnostic yield of prostate cancer by means of imaging techniques&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">As we will see&#44; such promising initial results of other working groups have not been reflected in a subsequent clinical application of relevant benefit&#44; and the vast majority of groups have not moved their studies to the development of software for image recognition for use in real time&#46;</p><p id="par0060" class="elsevierStylePara elsevierViewall">In light of our results&#44; we have not developed any software either&#46; The computational cost that would be necessary to do so is great because the image would have to be analyzed and suspected areas identified in real time&#46; In addition&#44; our modest results would not justify this development&#44; since we have not achieved a significant improvement regarding conventional ultrasound&#46; We believe that this may be due to several factors&#58; methodological and&#47;or inherent to the disease&#46; It might be that prostate cancer and its histological variants do not share an echotexture that may differ from the normal gland&#46; Another problem we face when approaching this type of study is imperfect monitoring&#44; as it is practically impossible to accurately identify the precise location of the cancer in the resulting image with the current technology&#46; This monitoring can be carried out with the study of the prostatectomy piece and&#47;or that of the punctures&#46; In our case&#44; we performed it through the histological study of the obtained cylinder&#46; However&#44; this may be affected to a greater or lesser extent&#44; and the entire length of the cylinder rarely corresponds to carcinoma&#46; Finally&#44; we believe&#44; and so seems to be observed in other works&#44; that the study of the signal attenuation would provide more reliable and independent information of the ultrasound scanner used&#44; the signal not having been processed to image&#44; and that this line of work is the one that in the future may give better results&#46;</p><p id="par0065" class="elsevierStylePara elsevierViewall">When we analyzed the experience of other centers&#44; we appreciated that the image recognition system that initially had most impact was the one made by the Nijmegen group in Holland&#44; called Automated Urologic Diagnostic Expert System &#40;AUDEX&#41;&#46; In their early work&#44; they obtained an 80 and 88&#37; sensitivity and specificity&#44; respectively&#44; after studying the image analyzing 5 co-occurrence matrices&#46;<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a> Subsequently&#44; they presented a validation of results with the findings on prostatectomy&#44; obtaining a 78&#37; sensitivity and a 50&#37; specificity&#44; considering the existence of a tumor volume greater than 10&#37; of the gland as a cut-off&#46;<a class="elsevierStyleCrossRefs" href="#bib0060"><span class="elsevierStyleSup">12&#44;13</span></a></p><p id="par0070" class="elsevierStylePara elsevierViewall">In a more recent publication&#44; the poor relationship of their AUDEX system with the histological confirmation on prostatectomy pieces in a larger number of patients was objectified&#44; finding an 85&#37; sensitivity&#44; an 18&#37; specificity&#44; and a 58&#37; diagnostic accuracy&#46;<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a> The University of Waterloo&#44; in Ontario&#44; Canada&#44; conducted a study with similar methodology to ours and the AUDEX group&#44; studying 4 matrices and simple mapping on gray scale and classifying the images using the k-neighbors technique&#44; with a maximum diagnostic efficacy close to 90&#37;&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> However&#44; they do not communicate how the supervision of their images is performed and there are no results of clinical application&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall">Fair et al&#46;&#44; from the MSKCC&#44; together with the Riverside Research Institute of New York&#44; combine the analysis of the RF signal with clinical parameters such as PSA&#44; and get 80&#37; correct classification of their images&#46;<a class="elsevierStyleCrossRefs" href="#bib0080"><span class="elsevierStyleSup">16&#8211;19</span></a> At the University of Kiel&#44; an applied technology called C-TRUS &#40;Computer Transrectal Ultrasound&#41; was developed through image analysis&#44; reporting very good results in prostate cancer detection in 132 patients with previous negative biopsies&#46;<a class="elsevierStyleCrossRefs" href="#bib0100"><span class="elsevierStyleSup">20&#44;21</span></a> However&#44; in the literature&#44; we did not find any evidence of this technology being applied in recent years&#46;</p><p id="par0080" class="elsevierStylePara elsevierViewall">The Seoul National University&#44; in Korea&#44; has worked with descriptors of textures similar to those used in our work&#44; but adding clinical parameters such as location&#44; morphology and contour of the prostate cancer&#46; It is hardly surprising that they get 90&#37; sensitivity and 96&#37; specificity&#44;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">22</span></a> since they use the same images for the training and the test&#44; which would invalidate their system for extrapolating results to other patients&#46; Besides&#44; the supervision of a region of interest is not performed by histological verification whatsoever&#46; Despite the success of many of these working groups&#44; none has managed to develop a software that helps real-time decision making for biopsy&#44; and virtually all researchers have abandoned the development of a device for clinical application&#46; The only exception to this is the technology called HistoScanning&#44; created by Braeckman et al&#46; with the Belgian company Advanced Medical Diagnostics&#46; Based on studies of RF signal&#44; they segment the ultrasound information in regions of interest of 0&#46;04<span class="elsevierStyleHsp" style=""></span>ml &#40;<span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;89&#44; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41;<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">23</span></a> and&#44; in a later publication&#44; they demonstrate how their system can show small tumors&#44; around 0&#46;5<span class="elsevierStyleHsp" style=""></span>ml&#46;<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">24</span></a> This technology is now commercially available coupled with ultrasound devices Bruel &#38; Kjaer&#46;</p><p id="par0085" class="elsevierStylePara elsevierViewall">In summary&#44; conventional ultrasound has its limitations in the early diagnosis of the disease&#44; and it would be useful to identify apparently hidden cancer image patterns&#46; In our work&#44; we found no significant benefit to justify the development of software for image recognition&#46; Currently&#44; only the HistoScanning software is available for commercial use&#44; although we are unaware of its benefit in daily clinical practice&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Funding</span><p id="par0090" class="elsevierStylePara elsevierViewall">Work funded by grant to the <span class="elsevierStyleGrantSponsor">cabinet Foundation for Health Research &#40;FHR&#41;</span>&#46;</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Conflict of interest</span><p id="par0095" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare&#46;</p></span></span>"
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    "fechaRecibido" => "2010-09-28"
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            0 => "Prostate cancer"
            1 => "Computer-Aided Diagnosis"
            2 => "Transrectal ultrasound"
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        "resumen" => "<span class="elsevierStyleSectionTitle">Introduction</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Prostate cancer is usually diagnosed by transrectal ultrasound &#40;TRUS&#41; biopsy&#46; Nevertheless&#44; suspicious images are frequently not found&#46; Imaging analysis studies aim to identify ultrasound patterns characteristic of apparently hidden conditions&#46;</p> <span class="elsevierStyleSectionTitle">Material and methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">We digitally recorded 288 TRUS ultrasound guided transrectal biopsies and extracted 3 static images from the puncture-biopsy area&#46; The extraction of the texture characteristics were obtained by &#8220;simple mapping&#8221; on a gray scale and spatial gray level dependence matrices &#40;SGLDM&#41;&#44; also known as Haralick&#39;s co-occurrence matrices&#44; which study the relationship of each pixel and its neighbors&#46; A pattern recognition software system was developed with two different classification methods&#58; nearest neighbor &#40;k-NN&#41; and Markov&#39;s hidden models&#46; Finally&#44; a virtual experiment was carried out in which four urologists compared their diagnostic accuracy for prostate cancer with our system in 408 TRUS images&#44; not in real time&#46;</p> <span class="elsevierStyleSectionTitle">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">The diagnostic capacity &#40;ROC curve&#41; with the simple gray map study was 59&#46;7&#37; with nearest-neighbor classification and 61&#46;6&#37; with Markov&#39;s hidden models classification&#46; The co-occurrence matrices showed an area under ROC curve of 60&#46;1&#37; and 60&#46;0&#37; with k-NN and Markov&#39;s hidden models classification&#44; respectively&#46; The virtual experiment was conducted with a simple gray map study and k-NN classification&#46; The images processed by our system showed the following diagnostic accuracy&#58; 63&#46;3&#44; 67&#44; 64&#46;3 and 63&#46;7&#37; compared to 61&#46;7&#44; 60&#46;5&#44; 66&#46;2 and 60&#46;7&#37; with the original image&#46;</p> <span class="elsevierStyleSectionTitle">Conclusions</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Our pattern recognition system for prostate cancer TRUS images has a limited&#44; yet stable&#44; accuracy&#46;</p>"
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        "resumen" => "<span class="elsevierStyleSectionTitle">Introducci&#243;n</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">El m&#233;todo de diagn&#243;stico de elecci&#243;n del c&#225;ncer de pr&#243;stata &#40;CP&#41; es la biopsia transrectal guiada ecogr&#225;ficamente&#46; Sin embargo&#44; es frecuente no objetivar im&#225;genes sospechosas&#46; Los estudios de an&#225;lisis de imagen pretenden identificar patrones ecogr&#225;ficos propios de una patolog&#237;a aparentemente ocultos&#46;</p> <span class="elsevierStyleSectionTitle">Materiales y m&#233;todo</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Registramos digitalmente 288 biopsias transrectales ecoguiadas&#44; de las que se aislaron im&#225;genes est&#225;ticas de cada punci&#243;n-biopsia para su an&#225;lisis computarizado&#46; Para ello se procedi&#243; a la extracci&#243;n de caracter&#237;sticas de textura mediante &#171;mapeo simple&#187; en escala de gris y &#171;matrices espaciales dependientes del nivel de gris&#187; o &#171;matrices de coaparici&#243;n&#187;&#44; que estudian la relaci&#243;n de cada p&#237;xel con sus vecinos&#46; Se desarroll&#243; un sistema de &#171;reconocimiento de formas&#187; con dos m&#233;todos de clasificaci&#243;n&#58; &#171;t&#233;cnica de k-vecinos&#187; y &#171;modelos ocultos de Markov&#187;&#46; Finalmente realizamos una simulaci&#243;n del sistema con 4 ecografistas&#44; comparando su capacidad diagn&#243;stica en escala de gris con im&#225;genes procesadas con nuestro sistema en 408 punciones grabadas&#44; no en tiempo real&#46;</p> <span class="elsevierStyleSectionTitle">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">La capacidad diagn&#243;stica &#40;curva ROC&#41; con mapeo simple fue de 59&#44;7 y 61&#44;6&#37; con clasificaci&#243;n mediante k-vecinos y modelos ocultos de Markov&#44; respectivamente&#46; Las matrices de coaparici&#243;n ofrecieron un &#225;rea bajo la curva ROC de 60&#44;1 y 60&#44;0&#37;&#46; El experimento virtual se llev&#243; a cabo mediante &#171;mapeo simple&#187; y clasificaci&#243;n con &#171;k-vecinos&#187;&#44; otorgando una capacidad diagn&#243;stica en cada ur&#243;logo de 63&#44;3&#44; 67&#44;0&#44; 64&#44;3 y 63&#44;7&#37; frente a 61&#44;7&#44; 60&#44;5&#44; 66&#44;2 y 60&#44;7&#37; conseguidas con la imagen original&#46;</p> <span class="elsevierStyleSectionTitle">Conclusiones</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">La utilizaci&#243;n de nuestro m&#233;todo de an&#225;lisis de imagen tiene una capacidad limitada&#44; aunque estable&#44; en la detecci&#243;n de &#225;reas prost&#225;ticas cancer&#237;genas&#46;</p>"
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        "nota" => "<p class="elsevierStyleNotepara">Please cite this article as&#58; G&#243;mez-Ferrer A&#44; Arlandis S&#46; An&#225;lisis de imagen asistido por ordenador en ecograf&#237;a transrectal de pr&#243;stata&#46; Actas Urol Esp&#46; 2011&#59;35&#58;404&#8211;13&#46;</p>"
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                  """
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                  """
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        "tipo" => "MULTIMEDIATABLA"
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                  \t\t\t\t" style="border-bottom: 2px solid black">Threshold&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">PV&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">NV&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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Article information

ISSN: 21735786
Original language: English

DOI:10.1016/j.acuroe.2011.02.004

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Year/Month	Html	Pdf	Total

2023 March	1	0	1
2018 February	18	0	18
2018 January	10	0	10
2017 December	15	4	19
2017 November	10	1	11
2017 October	23	3	26
2017 September	16	1	17
2017 August	11	8	19
2017 July	13	1	14
2017 June	12	4	16
2017 May	8	1	9
2017 April	8	28	36
2017 March	8	14	22
2017 February	17	3	20
2017 January	18	2	20
2016 December	18	8	26
2016 November	22	6	28
2016 October	34	4	38
2016 September	24	6	30
2016 August	16	4	20
2016 July	14	2	16
2016 June	19	3	22
2016 May	19	5	24
2016 April	25	15	40
2016 March	30	12	42
2016 February	25	16	41
2016 January	27	19	46
2015 December	20	10	30
2015 November	21	8	29
2015 October	16	16	32
2015 September	20	6	26
2015 August	18	5	23
2015 July	16	2	18
2015 June	4	2	6
2015 May	13	2	15
2015 April	29	6	35
2015 March	21	13	34
2015 February	29	8	37
2015 January	30	5	35
2014 December	38	10	48
2014 November	35	2	37
2014 October	50	4	54
2014 September	54	6	60
2014 August	39	6	45
2014 July	17	3	20
2014 June	25	3	28
2014 May	19	5	24
2014 April	13	3	16
2014 March	20	0	20
2014 February	12	3	15
2014 January	8	1	9
2013 December	14	1	15
2013 November	20	2	22
2013 October	16	7	23
2013 September	11	1	12
2013 August	11	1	12
2013 July	4	0	4

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