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"documento" => "article" "crossmark" => 1 "subdocumento" => "rev" "cita" => "Gastroenterol Hepatol. 2020;43:222-32" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "es" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Revisión</span>" "titulo" => "Revisión de métodos computacionales de detección y clasificación de pólipos en imagen de colonoscopia" "tienePdf" => "es" "tieneTextoCompleto" => "es" "tieneResumen" => array:2 [ 0 => "es" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "222" "paginaFinal" => "232" ] ] "titulosAlternativos" => array:1 [ "en" => array:1 [ "titulo" => "Review of computational methods for the detection and classification of polyps in colonoscopy imaging" ] ] "contieneResumen" => array:2 [ "es" => true "en" => true ] "contieneTextoCompleto" => array:1 [ "es" => true ] "contienePdf" => array:1 [ "es" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0015" "etiqueta" => "Figura 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 677 "Ancho" => 1740 "Tamanyo" => 100075 ] ] "descripcion" => array:1 [ "es" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Comparación de los mapas de energía proporcionados por cada método participante en el <span class="elsevierStyleItalic">challenge</span> de detección de pólipos celebrado en el congreso MICCAI de 2015: (A) Imagen original; (B) CUMED; (C) CVC-CLINIC; (D) PLS; (E) UNS-UCLAN; (F) ETIS-LARIB; (G) OUS, y (H) SNU. La línea verde de cada imagen representa la máscara del pólipo de referencia.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Cristina Sánchez-Montes, Jorge Bernal, Ana García-Rodríguez, Henry Córdova, Gloria Fernández-Esparrach" "autores" => array:5 [ 0 => array:2 [ "nombre" => "Cristina" "apellidos" => "Sánchez-Montes" ] 1 => array:2 [ "nombre" => "Jorge" "apellidos" => "Bernal" ] 2 => array:2 [ "nombre" => "Ana" "apellidos" => "García-Rodríguez" ] 3 => array:2 [ "nombre" => "Henry" "apellidos" => "Córdova" ] 4 => array:2 [ "nombre" => "Gloria" "apellidos" => "Fernández-Esparrach" ] ] ] ] ] "idiomaDefecto" => "es" "Traduccion" => array:1 [ "en" => array:9 [ "pii" => "S2444382420300390" "doi" => "10.1016/j.gastre.2019.11.003" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2444382420300390?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0210570520300145?idApp=UINPBA00004N" "url" => "/02105705/0000004300000004/v1_202003250614/S0210570520300145/v1_202003250614/es/main.assets" ] "itemAnterior" => array:18 [ "pii" => "S0210570519302031" "issn" => "02105705" "doi" => "10.1016/j.gastrohep.2019.08.008" "estado" => "S300" "fechaPublicacion" => "2020-04-01" "aid" => "1443" "copyright" => "Elsevier España, S.L.U." "documento" => "article" "crossmark" => 1 "subdocumento" => "sco" "cita" => "Gastroenterol Hepatol. 2020;43:209-10" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 9 "PDF" => 9 ] "en" => array:11 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Image of the month</span>" "titulo" => "Rectal neuroendocrine tumor – Case report" "tienePdf" => "en" "tieneTextoCompleto" => "en" "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "209" "paginaFinal" => "210" ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Tumor neuroendocrino rectal: un caso clínico" ] ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 690 "Ancho" => 1674 "Tamanyo" => 210363 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Rectum endoscopic ultrasound: hypoechoic lesion with 12<span class="elsevierStyleHsp" style=""></span>mm (arrow), homogeneous, well delimited, originating in the muscularis of the mucosa with no lymph nodes or muscularis propria involvement.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Carolina Simões, Sofia Carvalhana, Leonor Xavier de Brito, Luís Carrilho-Ribeiro, Rui Tato Marinho" "autores" => array:5 [ 0 => array:2 [ "nombre" => "Carolina" "apellidos" => "Simões" ] 1 => array:2 [ "nombre" => "Sofia" "apellidos" => "Carvalhana" ] 2 => array:2 [ "nombre" => "Leonor" "apellidos" => "Xavier de Brito" ] 3 => array:2 [ "nombre" => "Luís" "apellidos" => "Carrilho-Ribeiro" ] 4 => array:2 [ "nombre" => "Rui" "apellidos" => "Tato Marinho" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0210570519302031?idApp=UINPBA00004N" "url" => "/02105705/0000004300000004/v1_202003250614/S0210570519302031/v1_202003250614/en/main.assets" ] "en" => array:19 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Review</span>" "titulo" => "Non-invasive diagnosis of liver fibrosis: A review of current imaging modalities" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "211" "paginaFinal" => "221" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Ling Wu, Yue Shen, Feng Li" "autores" => array:3 [ 0 => array:2 [ "nombre" => "Ling" "apellidos" => "Wu" ] 1 => array:2 [ "nombre" => "Yue" "apellidos" => "Shen" ] 2 => array:4 [ "nombre" => "Feng" "apellidos" => "Li" "email" => array:1 [ 0 => "13661654285@163.com" ] "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] ] "afiliaciones" => array:1 [ 0 => array:2 [ "entidad" => "Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, PR China" "identificador" => "aff0005" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Diagnóstico no invasivo de la fibrosis hepática: una revisión de las técnicas de diagnóstico por imagen actuales" ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Liver fibrosis comes into being as a reversible result of a sustained or recurrent wound healing response to hepatic injury created by viral, toxic and/or metabolic insult, and represents an imbalance between the synthesis and degeneration of extracellular matrix (ECM).<a class="elsevierStyleCrossRef" href="#bib0305"><span class="elsevierStyleSup">1</span></a> Accompanied by the distortion of hepatic structure and function, cirrhosis is the result of the progression of liver fibrosis. Regardless of the etiology, activated hepatic stellate cells secrete ECM molecules and release matrix metalloproteinases (MMPs) and tissue inhibitors of MMPs in the space of Disse,<a class="elsevierStyleCrossRef" href="#bib0310"><span class="elsevierStyleSup">2</span></a> which can result in excessive matrix deposition and hepatic fibrogenesis.</p><p id="par0010" class="elsevierStylePara elsevierViewall">According to the latest Global Burden of Disease Study, the global incidence of cirrhosis and other chronic liver diseases in 2017 was 5,154,900.<a class="elsevierStyleCrossRef" href="#bib0315"><span class="elsevierStyleSup">3</span></a> Moreover, from 2007 to 2017, the years lived with disability (or ‘YLDs’) and all-age deaths from cirrhosis have increased by 34.8% and 15.0%,<a class="elsevierStyleCrossRefs" href="#bib0315"><span class="elsevierStyleSup">3,4</span></a> respectively. Hence, the clinical burden of cirrhosis is substantial. It has been validated that liver fibrosis can be reversed after proper treatment of the underlying etiologies, especially at the early stage of fibrosis.<a class="elsevierStyleCrossRef" href="#bib0325"><span class="elsevierStyleSup">5</span></a> Therefore, early diagnosis and staging of liver fibrosis will benefit the treatment of patients, serving as a determinant in the prognosis of chronic liver disease, and acquiring the dynamic changes of liver fibrosis in a timely manner will be a boost for the clinical treatment approach.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Imaging modalities</span><p id="par0015" class="elsevierStylePara elsevierViewall">The medical imaging is a non-invasive tool with robust diagnostic function. Basic imaging methods, including computed tomography (CT), ultrasound (US) and magnetic resonance imaging (MRI), can provide dependable information about decompensated cirrhosis, while the performance for diagnosing early fibrosis is not so good.<a class="elsevierStyleCrossRef" href="#bib0330"><span class="elsevierStyleSup">6</span></a> Over the last few decades, quite a number of more advanced imaging modalities have been developed, enabling a prompt evaluation of liver fibrosis and cirrhosis. And, combining different imaging modalities or combining them with serum biomarkers can be a realistic substitution for liver biopsy. Advantages and flaws of different imaging modalities are summarized briefly in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">CT</span><p id="par0020" class="elsevierStylePara elsevierViewall">Traditionally, CT has often been used in the diagnosis of advanced liver fibrosis and cirrhosis, especially for assessment of liver cirrhosis-associated complications, such as portal hypertension. Based on simplified indices for liver remodeling and attenuation, quantitative CT scores have been shown to have a good performance in predicting significant liver fibrosis, with high areas (0.96–0.97) under the ROC curves.<a class="elsevierStyleCrossRef" href="#bib0335"><span class="elsevierStyleSup">7</span></a> However, considering the reliance on radiation and inherent lower accuracy compared to other diagnostic methods such as fibroScan and MRE, CT remains an inferior choice for assessing early stages of liver fibrosis. Countless efforts have been taken to improve its accuracy and diagnostic value, for example: employing iodine density measurement in 8-cm detector dual-energy CT, clinicians could assess liver parenchyma hemodynamic changes and evaluate the severity through quantitative indices which correlate positively with Child-Paugh Score.<a class="elsevierStyleCrossRef" href="#bib0340"><span class="elsevierStyleSup">8</span></a></p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Contrast-enhanced CT (CECT)</span><p id="par0025" class="elsevierStylePara elsevierViewall">Many researchers have found that CECT may be a pretty good choice in the evaluation of liver fibrosis. Choi et al. developed a deep learning system (based upon a data set including portal venous phase CT images from 7461 patients with pathologically-confirmed liver fibrosis) for staging liver fibrosis with CECT images of the liver. With a high accuracy as it diagnosed significant or more severe fibrosis patients, it did not assess moderate fibrosis.<a class="elsevierStyleCrossRef" href="#bib0345"><span class="elsevierStyleSup">9</span></a> While, from the study on the rat model through micro-CT, the performance of CETC in assessing early and intermediate fibrosis is satisfying with strong correlations to both the Ishak fibrosis score (<span class="elsevierStyleItalic">R</span><span class="elsevierStyleSup">2</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.751, <span class="elsevierStyleItalic">P</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.01) and the fibrotic area (<span class="elsevierStyleItalic">R</span><span class="elsevierStyleSup">2</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.801, <span class="elsevierStyleItalic">P</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.01).<a class="elsevierStyleCrossRef" href="#bib0350"><span class="elsevierStyleSup">10</span></a> Additionally, monitoring pathological angiogenesis and microvasculature alterations could be realized by contrast-enhanced micro-CT.<a class="elsevierStyleCrossRef" href="#bib0355"><span class="elsevierStyleSup">11</span></a> Since a sharp increase in sinusoid angiogenesis has been observed during early-stage fibrosis and the vascular reconstruction would happen due to the portal hypertension,<a class="elsevierStyleCrossRef" href="#bib0360"><span class="elsevierStyleSup">12</span></a> CECT might be of potential use in assessing liver fibrosis and the complications.</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">CT perfusion (CTP)</span><p id="par0030" class="elsevierStylePara elsevierViewall">Based on the changes of substantial microcirculation occurring in liver fibrosis: an increase in the arterial perfusion and a drop in portal and total liver perfusion, CTP allows for a quantitative assessment of the hepatic perfusion.<a class="elsevierStyleCrossRef" href="#bib0365"><span class="elsevierStyleSup">13</span></a> And according to the transient time (a parameter of CTP) increased significantly between minimal fibrosis and intermediate fibrosis (<span class="elsevierStyleItalic">P</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.025), discriminating mild fibrosis from intermediate fibrosis could be realized by CTP.<a class="elsevierStyleCrossRef" href="#bib0365"><span class="elsevierStyleSup">13</span></a> Combining the measurements of liver and spleen might improve the accuracy of the assessment for liver fibrosis, and the splenic mean transient time (a parameter of splenic perfusion) also changes significantly between different fibrosis stages revealed under the Kruskal–Wallis test (<span class="elsevierStyleItalic">P</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001).<a class="elsevierStyleCrossRef" href="#bib0370"><span class="elsevierStyleSup">14</span></a></p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">US</span><p id="par0035" class="elsevierStylePara elsevierViewall">On the basis of gray-scale findings such as surface nodularity, altered parenchymal echogenicity and heterogeneous echotexture, which reflect the presence of regenerative nodules and fibrous septa, US could help diagnosis liver cirrhosis.<a class="elsevierStyleCrossRef" href="#bib0375"><span class="elsevierStyleSup">15</span></a> And combing the spleen longitudinal diameter, doctors could evaluate portal hypertension in patients with viral hepatitis.<a class="elsevierStyleCrossRef" href="#bib0380"><span class="elsevierStyleSup">16</span></a> It is noteworthy that splenomegaly dose not equal portal hypertension, other mechanisms such as reduced lysosomal lipase in patients with non-alcoholic fatty liver disease (NAFLD) could result in splenomegaly.<a class="elsevierStyleCrossRef" href="#bib0385"><span class="elsevierStyleSup">17</span></a> So in case of the splenomegaly detected by US, combining other examinations is needed for the right estimation of portal hypertension, especially in patients with NAFLD. The advantages of low cost, non-invasiveness, reproducibility, simple application, and non-reliance on ionizing radiation make US an attractive alternative to liver biopsy.<a class="elsevierStyleCrossRef" href="#bib0390"><span class="elsevierStyleSup">18</span></a> However, like traditional CT, its accuracy is not reliable for the diagnosis of early fibrosis, which continues to limit the utility of US.</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Transient elastography (TE)</span><p id="par0040" class="elsevierStylePara elsevierViewall">Based on the rationale that the collagen deposits and imparts parenchymal rigidity in livers during fibrogenesis, TE converts this rigidity into a stiffness value to evaluate the fibrosis degree,<a class="elsevierStyleCrossRef" href="#bib0395"><span class="elsevierStyleSup">19</span></a> and it has to be noted that different from other imaging methods, TE presents us with the stiffness measurement of the liver tissue instead of an intuitive picture of the liver, therefore, it is not per se an imaging method.</p><p id="par0045" class="elsevierStylePara elsevierViewall">Numerous researches have validated its accuracy in diagnosing and staging liver fibrosis under a recognized pre-determined set of cut-off values,<a class="elsevierStyleCrossRef" href="#bib0395"><span class="elsevierStyleSup">19</span></a> as well as in identifying the improvement of liver fibrosis under effective treatment of different etiologies.<a class="elsevierStyleCrossRef" href="#bib0400"><span class="elsevierStyleSup">20</span></a> Furthermore, the value of TE is a good reference for identifying patients at the risk of possible negative outcomes of liver cirrhosis such as liver decompensation, liver failure, hepatocellular carcinoma,<a class="elsevierStyleCrossRef" href="#bib0405"><span class="elsevierStyleSup">21</span></a> and at worst, death. Moreover, no more than 5<span class="elsevierStyleHsp" style=""></span>min is needed to perform TE at patient bedside or in an outpatient clinic, with results obtained immediately,<a class="elsevierStyleCrossRef" href="#bib0410"><span class="elsevierStyleSup">22</span></a> doctors often choose TE to monitor patients longitudinally for evaluating therapeutic effectiveness and modulating treatment plan promptly.<a class="elsevierStyleCrossRef" href="#bib0415"><span class="elsevierStyleSup">23</span></a> Meanwhile, it is important to note that a non-cirrhotic liver may also have a high TE value. Non-cirrhotic portal hypertension conditions such as portal vein thrombosis and nodular regenerative hyperplasia might result in an elevated TE value.<a class="elsevierStyleCrossRef" href="#bib0420"><span class="elsevierStyleSup">24</span></a> Recent food intake, abdominal fat and elevated liver enzymes have all been reported as factors with potential for impairing the accuracy of TE.<a class="elsevierStyleCrossRef" href="#bib0425"><span class="elsevierStyleSup">25</span></a></p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Acoustic radiation force impulse (ARFI)</span><p id="par0050" class="elsevierStylePara elsevierViewall">Akin to TE, ARFI is shear wave-based technique, and it is based on an acoustic internal push, known as liver share wave velocity, that assesses liver stiffness using focused US beams. Converted by Young's modulus: 3<span class="elsevierStyleItalic">ρv</span><span class="elsevierStyleSup">2</span> (<span class="elsevierStyleItalic">ρ</span> and <span class="elsevierStyleItalic">v</span> represent the tissue density and the speed of shear wave respectively), the measured shear wave speed could be represented by the value in kilopascals (kPa) that positively correlated with tissue stiffness.<a class="elsevierStyleCrossRef" href="#bib0390"><span class="elsevierStyleSup">18</span></a> Several researches have thoroughly compared the diagnostic performance of ARFI and TE for the evaluation of liver fibrosis with the conclusion that ARFI is more reliable than TE, especially in patients with ascites,<a class="elsevierStyleCrossRef" href="#bib0430"><span class="elsevierStyleSup">26</span></a> the AUROC values and sensitivity of ARFI in diagnosing significant fibrosis could reach over 85%.<a class="elsevierStyleCrossRef" href="#bib0435"><span class="elsevierStyleSup">27</span></a> Overall, there are 2 types of ARFI: point share wave elastography (pSWE) and two-dimensional share wave elastography (2D-SWE),<a class="elsevierStyleCrossRef" href="#bib0390"><span class="elsevierStyleSup">18</span></a> sampling area and focused energy are the main differences, and the 2D-SWE is the latest elastography used in clinic.</p><p id="par0055" class="elsevierStylePara elsevierViewall">The 2D-SWE creates a real-time, 2D quantitative map of liver stiffness superimposed on a B-mode image, characterized by a higher frame rate to record the shear wave than the conventional US device.<a class="elsevierStyleCrossRef" href="#bib0440"><span class="elsevierStyleSup">28</span></a> It integrates the information from the anatomy and stiffness: the shear wave velocities distribution reflects liver elastic properties, and the B-mode imaging represents the liver morphology, presenting us the anatomy specific elastograms of the liver.<a class="elsevierStyleCrossRef" href="#bib0445"><span class="elsevierStyleSup">29</span></a> The reliability of it to assess the liver fibrosis is now being researched widely. It has a better performance than TE, probably due to the larger area of interest in 2D-SWE, which is more reflective of the whole picture of the liver; moreover, it could localize to the area of interest exactly. Because the liver stiffness could be influenced by the inflammation due to different etiologies,<a class="elsevierStyleCrossRef" href="#bib0450"><span class="elsevierStyleSup">30</span></a> the threshold values that defines should be defined according to etiologies, and different cut-off values according to the etiologies are shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>. However, the area of interest which could be representative of the whole liver is depended on the operator, and it might cause deviations. It is noteworthy that the fibrosis is not reflected by the stiffness entirely, as the cofounding factors such as the inflammation and fluctuations of liver enzymes might influence the stiffness value,<a class="elsevierStyleCrossRef" href="#bib0450"><span class="elsevierStyleSup">30</span></a> so it is necessary to combine other examinations such as medical history, clinical symptoms, serum biomarkers and so on.</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Contrast-enhanced ultrasound (CEUS)</span><p id="par0060" class="elsevierStylePara elsevierViewall">Akin to CTP, it evaluates liver fibrosis and cirrhosis is based on the liver hemodynamics alteration. Using microbubble as contrast agents, CEUS has intrinsic advantages: On the one hand, it is safety for patients with renal failure for whom conventional CT or MRI contrast agents are contraindicated<a class="elsevierStyleCrossRef" href="#bib0455"><span class="elsevierStyleSup">31</span></a>; on the other hand, free of interstitial or equilibrium phase, it has a better temporal and in-plane spatial resolution than contrast-enhanced CT and MRI. On the basis of its wash-in and washout patterns, focal liver lesions such as hepatocellular carcinoma can be detected – hypervascularity in the arterial phase and washout in the portal venous or delayed phase.<a class="elsevierStyleCrossRef" href="#bib0460"><span class="elsevierStyleSup">32</span></a> For cirrhotic patients that are at a high risk for hepatocellular carcinoma, CEUS could be used during the follow up for the detection of new lesions. Based on portal vein maximum signal intenstiy, CEUS could diagnosis both and early fibrosis with high specificities in rabbits.<a class="elsevierStyleCrossRef" href="#bib0465"><span class="elsevierStyleSup">33</span></a> CEUS also has limitations: based on US, the value of it depends on skills of operators, breath-hold of the patients and so on. Moreover, factors such as continuous imaging and inappropriate frame rate could result in excessive disruption of microbubble agent.<a class="elsevierStyleCrossRef" href="#bib0470"><span class="elsevierStyleSup">34</span></a></p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">MRI</span><p id="par0065" class="elsevierStylePara elsevierViewall">Like US, MRI is another radiation-free imaging modality, and its specificity, sensitivity and accuracy are similar to CT in the diagnosis of liver cirrhosis. In the experimental fibrosis animal model (tetrachloride-induced), T1 and T2 mapping in MRI were found to associated with the severity of liver fibrosis.<a class="elsevierStyleCrossRef" href="#bib0475"><span class="elsevierStyleSup">35</span></a> Moreover, a prospective study showed that, by calculating the hepatocyte fraction, the AUROC values of T1 mapping for diagnosis of any (≥F1), significant (≥F2), advanced (≥F3) and cirrhosis (≥F4) were 0.837, 0.890, 0.957, 0.957, respectively.<a class="elsevierStyleCrossRef" href="#bib0480"><span class="elsevierStyleSup">36</span></a></p><p id="par0070" class="elsevierStylePara elsevierViewall">As a part of the body unit, liver with pathological changes could impact other visceral organs such as spleen, kidney and cardiac. Measuring the volume, blood flow, perfusion of critical organs could help clinicians evaluate liver fibrosis and cirrhosis in a holistic fashion.<a class="elsevierStyleCrossRef" href="#bib0485"><span class="elsevierStyleSup">37</span></a> The hepatic venous pressure gradient (HVPG) is an important index for evaluating the patients with fibrosis or cirrhosis, while invasiveness, high cost and poor reproducibility limit its implement. However, from the research by Bradley et al., apart from the structural changes reflected by prolonged T1 values and hemodynamic changes within the liver reflected by increased total hepatic blood flow and decreased liver perfusion, the HVPG could be estimated by the combination of T1 relaxation time and splenic artery velocity.<a class="elsevierStyleCrossRef" href="#bib0490"><span class="elsevierStyleSup">38</span></a> Moreover, renal cortex T1 significantly reduced with disease severity (<span class="elsevierStyleItalic">P</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001).<a class="elsevierStyleCrossRef" href="#bib0495"><span class="elsevierStyleSup">39</span></a> Therefore, MRI could help clinicians make an overall evaluation of cirrhotic patients. However, the accuracy of MRI for diagnosing liver fibrosis at early stages is still not satisfying. Thanks to great efforts of countless researchers, advanced imaging modalities based on MRI with improved performance in the evaluation of liver fibrosis are now being proposed and even developed, overcoming the limitations of diagnosis on the basis of morphological features alone.</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Magnetic resonance elastography (MRE)</span><p id="par0075" class="elsevierStylePara elsevierViewall">MRE is a novel non-invasive tool for mapping of tissue elasticity based upon phase contrast, something like the palpation of the liver which can assess its tissue stiffness physically. It can directly visualize and quantitatively measure propagating acoustic strain waves.<a class="elsevierStyleCrossRef" href="#bib0480"><span class="elsevierStyleSup">36</span></a> The diagnostic performance of MRE remains promising in clinical practice, not only for diagnosing liver fibrosis, but also for evaluating the patients’ survival by clinical end-points such as hepatocellular carcinoma and hepatic decompensation.<a class="elsevierStyleCrossRef" href="#bib0500"><span class="elsevierStyleSup">40</span></a> Considering the less robust features of traditional MRI for detecting early fibrosis and the better performance of MRE in detecting morphological features in cirrhosis,<a class="elsevierStyleCrossRef" href="#bib0455"><span class="elsevierStyleSup">31</span></a> MRE is superior for diagnosing patients with suspected liver fibrosis or for evaluating the effectiveness of a treatment. Furthermore, recent studies have reported that MRE is superior to ARFI and TE,<a class="elsevierStyleCrossRef" href="#bib0500"><span class="elsevierStyleSup">40</span></a> having a higher AUROC than either for identifying liver fibrosis (<span class="elsevierStyleItalic">P</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.01), especially in patients with non-alcoholic fatty liver disease.</p><p id="par0080" class="elsevierStylePara elsevierViewall">Preceding studies and analyses have indicated satisfactory sensitivity, specificity, and accuracy rates of MRE in diagnosing and staging of liver fibrosis; moreover, just like TE, the high rates of repeatability and painlessness<a class="elsevierStyleCrossRef" href="#bib0505"><span class="elsevierStyleSup">41</span></a> of MRE make it useful for longitudinal follow-up of patients with chronic liver disease. However, as every coin has two sides, MRE has some limitations too. Firstly, compared to other imaging modalities, such as TE, it is more expensive, though still cheaper than liver biopsy.<a class="elsevierStyleCrossRef" href="#bib0505"><span class="elsevierStyleSup">41</span></a> Secondly, to obtain an accurate diagnosis through MRE, the clinical practitioner should possess specialized knowledge about MRE, and appropriate judgments should be made under different circumstances, such as severe obesity, massive ascites, liver iron deposition and the use of 3.0-Tesla (T), which can lead to the failure of MRE (<span class="elsevierStyleItalic">P</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.004).<a class="elsevierStyleCrossRef" href="#bib0510"><span class="elsevierStyleSup">42</span></a> Additionally, metal implants or psychological illness, such as claustrophobia, preclude a patient's ability to be examined by MRE. Because the value from the elastography could be influenced by different etiologies, according to the highest Youden's index (sensitivity<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>specificity<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>1), the optimal cut-off values for different fibrosis stages in different etiologies are shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>.</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Diffusion-weighted imaging (DWI)</span><p id="par0085" class="elsevierStylePara elsevierViewall">DWI is a kind of imaging modality based on the assumption of the free (unrestricted) diffusion of water protons, which measures the apparent diffusion coefficient (ADC). Since excessive ECM deposition hinders the free movement of water molecules,<a class="elsevierStyleCrossRef" href="#bib0515"><span class="elsevierStyleSup">43</span></a> the ADC value is inversely correlated with fibrosis stages as reflected by differing quantifiable measures of the random microscopic motion of water molecules in biological tissue. Conflicting results have been published regarding the accuracy of DWI. Furthermore, findings from the meta-analysis by Wang et al.<a class="elsevierStyleCrossRef" href="#bib0520"><span class="elsevierStyleSup">44</span></a> challenged the value of DWI, with its accuracy being inferior to MRE when a 1.5-T MRI scanner is used (<span class="elsevierStyleItalic">z</span> test, <span class="elsevierStyleItalic">P</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05).</p><p id="par0090" class="elsevierStylePara elsevierViewall">Use of a 3.0-T MRI scanner with consecutive <span class="elsevierStyleItalic">b</span> values of 0, 50, 100, 200, 400, 800<span class="elsevierStyleHsp" style=""></span>s/mm<span class="elsevierStyleSup">2</span> has shown DWI to perform well in diagnosing and staging of liver fibrosis for primary sclerosing cholangitis cohorts,<a class="elsevierStyleCrossRef" href="#bib0525"><span class="elsevierStyleSup">45</span></a> to be superior to Gd-EOB-DTPA-enhanced MRI, and to be capable of sufficiently discriminating mild or no fibrosis from moderate fibrosis and cirrhosis (<span class="elsevierStyleItalic">P</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001, sensitivity of 0.917 and 0.8, respectively). However, a contradictory conclusion was made in another study,<a class="elsevierStyleCrossRef" href="#bib0530"><span class="elsevierStyleSup">46</span></a> in which use of the same 3.0-T magnetic field at a <span class="elsevierStyleItalic">b</span> value of 500<span class="elsevierStyleHsp" style=""></span>ns/mm<span class="elsevierStyleSup">2</span> showed the ADC to be only weakly corrlated with fibrosis stages. It has also been reported that when a 3.0-T MRI scanner was used, fibrosis stages were poorly associated with ADC at a <span class="elsevierStyleItalic">b</span> value of 500<span class="elsevierStyleHsp" style=""></span>s/mm<span class="elsevierStyleSup">2</span> (<span class="elsevierStyleItalic">P</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.27), while were significantly associated with ADC at a <span class="elsevierStyleItalic">b</span> value of 1000<span class="elsevierStyleHsp" style=""></span>s/mm<span class="elsevierStyleSup">2</span> (<span class="elsevierStyleItalic">P</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.01).<a class="elsevierStyleCrossRef" href="#bib0535"><span class="elsevierStyleSup">47</span></a></p><p id="par0095" class="elsevierStylePara elsevierViewall">Thus, the collective findings reported in the literature to date suggested that use of a 3.0-T MRI scanner with a higher <span class="elsevierStyleItalic">b</span> value may benefit the ability of DWI to assess liver fibrosis more reliably. Disappointingly, it can be influenced by several common biologic, pathologic and physiologic factors, such as liver inflammation, steatosis and perfusion effects, and it is not sensitive enough in differentiating mild fibrosis from moderate fibrosis.<a class="elsevierStyleCrossRef" href="#bib0530"><span class="elsevierStyleSup">46</span></a></p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">T1 ρ mapping</span><p id="par0100" class="elsevierStylePara elsevierViewall">T1 ρ is the spin-lattice relaxation time constant in the rotating frame in MRI, and is sufficiently sensitive for identifying the motion related to tissue macromolecular composition, such as interaction among water molecules.<a class="elsevierStyleCrossRef" href="#bib0540"><span class="elsevierStyleSup">48</span></a> In recently years, its application has been extended to detecting and staging liver fibrosis with 1.5-T or 3.0-T MRI scanner. Several preliminary studies have indicated the capabilities of T1 ρ mapping to diagnose and stage fibrosis<a class="elsevierStyleCrossRef" href="#bib0540"><span class="elsevierStyleSup">48</span></a>: T1 ρ values in fibrotic livers were significantly higher compared to healthy livers (<span class="elsevierStyleItalic">P</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05); at a threshold of 49.5<span class="elsevierStyleHsp" style=""></span>ms, the sensitivity and specificity of a 3T scanner in predicting liver fibrosis could reach to 77.8% and 100%, respectively, as well as with a good interobserver agreement (intraclass correlation coefficient<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.975). Importantly, its detection may not be affected by the presence of fatty liver.<a class="elsevierStyleCrossRef" href="#bib0545"><span class="elsevierStyleSup">49</span></a> However, the lack of robust clinical data cannot be ignored and optimization remains an open need, particularly since it has not shown any obvious superiority over the other imaging modalities, such as 2D-SWE.<a class="elsevierStyleCrossRef" href="#bib0540"><span class="elsevierStyleSup">48</span></a></p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Molecular imaging techniques</span><p id="par0105" class="elsevierStylePara elsevierViewall">Liver fibrosis is widely recognized as a dynamic process, involving the turnover of ECM. Collagen overload is one of its hallmarks. Low molecular weight (<1<span class="elsevierStyleHsp" style=""></span>kDa)-based and peptide (<10<span class="elsevierStyleHsp" style=""></span>kDa)-based agents contributing to the process of ECM turnover have attracted the attention of molecular imaging researchers for their potential to provide better visualization, characterization and measurement of fibrosis process, and thereby to improve the technological approaches for diagnosing and staging of liver fibrosis. The techniques that utilized such agents include, but are not limited to, radiotracer imaging, MRI, MR spectroscopy and optical imaging.<a class="elsevierStyleCrossRef" href="#bib0550"><span class="elsevierStyleSup">50</span></a></p><p id="par0110" class="elsevierStylePara elsevierViewall">Among these, MRI has been studied extensively in recent years. Studies using a rat model of liver fibrosis have validated the hypothesis that hepatic expression of integrin αγβ3 reflects the activation of hepatic stellate cells and allowed for characterization of the radioiodinated cyclic RGDyk peptide high-affinity binding to both purified and membrane-bound integrin αγβ3.<a class="elsevierStyleCrossRef" href="#bib0555"><span class="elsevierStyleSup">51</span></a> These findings make it possible for subsequent researchers to use integrin αγβ3 as an imaging tracer to visualize and stage liver fibrosis,<a class="elsevierStyleCrossRef" href="#bib0560"><span class="elsevierStyleSup">52</span></a> and to monitor the progression of liver fibrosis and therapeutic response. Another fascinating probe is EP-3533, compromising a 10 amino acid cyclic peptide conjugated to three gadolinium moieties.<a class="elsevierStyleCrossRef" href="#bib0565"><span class="elsevierStyleSup">53</span></a> It has been clearly shown, through use of a rat model, that MRI-based molecular imaging is capable of distinguishing liver fibrosis stages and monitoring therapeutic effectiveness,<a class="elsevierStyleCrossRef" href="#bib0570"><span class="elsevierStyleSup">54</span></a> suggesting its potential for clinical utility.</p><p id="par0115" class="elsevierStylePara elsevierViewall">Ultimately, this experimental imaging modality is attracting more and more attention of researchers, particularly in consideration of its cost-effectiveness, accuracy, convenience, and feasibility for human application. Yet, there's still a long way to go before the application of molecular imaging modalities in clinical diagnosis of liver fibrosis can be fully realized.</p></span><span id="sec0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Conclusion</span><p id="par0120" class="elsevierStylePara elsevierViewall">Admittedly, imaging modalities of today cannot rival liver biopsy in accuracy of diagnosing and staging liver fibrosis, and the ultimately definitive diagnosis of liver fibrosis still depends on liver biopsy. Despite myriad studies that have demonstrated superiorities of non-invasive imaging modalities for diagnosing liver fibrosis, there remain some problems. For example, almost all of the studies have been performed by skilled operators, which does not reflect the real-life heterogeneity in clinical practices, and it impacts the generalizability of the findings published and underlies the uncertainty as to whether a similarly good performance of complex imaging modalities, such as DWI, could be realized in other clinical departments.</p><p id="par0125" class="elsevierStylePara elsevierViewall">However, on the other hand, as compared with liver biopsy, the characteristics of higher cost-effectiveness, better compliance by patients, easier operation, better repeatability and so on, make non-invasive imaging modalities popular for use in diagnosing and evaluating liver fibrosis. The emergence of novel non-invasive techniques will provide more choices to both treating clinicians and patients for the diagnosis of liver fibrosis, particularly as they are being continually improved for better accuracy and greater practical value. Indexes of different techniques assess different fibrosis degrees are concisely described in <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>.</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><p id="par0130" class="elsevierStylePara elsevierViewall">Almost some of the newest (and promising) alternative techniques, such as molecular imaging techniques, have not yet been established in clinical practice, and their continued development may ultimately provide a foundation upon which even more techniques could be devised. In general, imaging techniques seem to have a great potential as a reliable alternative to liver biopsy. Tremendous efforts are being made to overcome their confounding factors and improve their accuracy in diagnosing liver fibrosis. Certainly, non-invasive imaging modalities represent a tangible hope for more convenient and accurate surveillance of patients with liver fibrosis and cirrhosis in the future.</p></span><span id="sec0080" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Funding</span><p id="par0135" class="elsevierStylePara elsevierViewall">This work was supported by the <span class="elsevierStyleGrantSponsor" id="gs1">National Natural Science Foundation of China</span> (Nos. <span class="elsevierStyleGrantNumber" refid="gs1">81270007 to Li F.</span> and <span class="elsevierStyleGrantNumber" refid="gs1">81670513 to Li F.</span>); the <span class="elsevierStyleGrantSponsor" id="gs2">Shanghai Talent Development Funds</span> (Grant No. <span class="elsevierStyleGrantNumber" refid="gs2">201304 to Li F.</span>); the <span class="elsevierStyleGrantSponsor" id="gs3">Shanghai Rising-Star Program</span> (Grant No. <span class="elsevierStyleGrantNumber" refid="gs3">13QA1400700 to Li F.</span>); and the <span class="elsevierStyleGrantSponsor" id="gs4">Shanghai Outstanding Young Talent Plan in Health System</span> (Grant No. <span class="elsevierStyleGrantNumber" refid="gs4">13 YO56 to Li F.</span>).</p></span><span id="sec0085" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Conflict of interest</span><p id="par0140" class="elsevierStylePara elsevierViewall">None.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:24 [ 0 => array:3 [ "identificador" => "xres1321104" "titulo" => "Abstract" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0005" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1218694" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "xpalclavsec1218693" "titulo" => "Abbreviations" ] 3 => array:3 [ "identificador" => "xres1321105" "titulo" => "Resumen" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0010" ] ] ] 4 => array:2 [ "identificador" => "xpalclavsec1218692" "titulo" => "Palabras clave" ] 5 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 6 => array:2 [ "identificador" => "sec0010" "titulo" => "Imaging modalities" ] 7 => array:2 [ "identificador" => "sec0015" "titulo" => "CT" ] 8 => array:2 [ "identificador" => "sec0020" "titulo" => "Contrast-enhanced CT (CECT)" ] 9 => array:2 [ "identificador" => "sec0025" "titulo" => "CT perfusion (CTP)" ] 10 => array:2 [ "identificador" => "sec0030" "titulo" => "US" ] 11 => array:2 [ "identificador" => "sec0035" "titulo" => "Transient elastography (TE)" ] 12 => array:2 [ "identificador" => "sec0040" "titulo" => "Acoustic radiation force impulse (ARFI)" ] 13 => array:2 [ "identificador" => "sec0045" "titulo" => "Contrast-enhanced ultrasound (CEUS)" ] 14 => array:2 [ "identificador" => "sec0050" "titulo" => "MRI" ] 15 => array:2 [ "identificador" => "sec0055" "titulo" => "Magnetic resonance elastography (MRE)" ] 16 => array:2 [ "identificador" => "sec0060" "titulo" => "Diffusion-weighted imaging (DWI)" ] 17 => array:2 [ "identificador" => "sec0065" "titulo" => "T1 ρ mapping" ] 18 => array:2 [ "identificador" => "sec0070" "titulo" => "Molecular imaging techniques" ] 19 => array:2 [ "identificador" => "sec0075" "titulo" => "Conclusion" ] 20 => array:2 [ "identificador" => "sec0080" "titulo" => "Funding" ] 21 => array:2 [ "identificador" => "sec0085" "titulo" => "Conflict of interest" ] 22 => array:2 [ "identificador" => "xack455536" "titulo" => "Acknowledgements" ] 23 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2019-06-20" "fechaAceptado" => "2019-11-20" "PalabrasClave" => array:2 [ "en" => array:2 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1218694" "palabras" => array:3 [ 0 => "Hepatic fibrosis" 1 => "Imageology diagnosis" 2 => "Noninvasive" ] ] 1 => array:4 [ "clase" => "abr" "titulo" => "Abbreviations" "identificador" => "xpalclavsec1218693" "palabras" => array:13 [ 0 => "ADC" 1 => "ARFI" 2 => "CT" 3 => "CTP" 4 => "DWI" 5 => "ECM" 6 => "MMPs" 7 => "MRE" 8 => "MRI" 9 => "NAFLD" 10 => "TE" 11 => "US" 12 => "2D-SWE" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1218692" "palabras" => array:3 [ 0 => "Fibrosis hepática" 1 => "Técnicas de diagnóstico por imagen" 2 => "No invasivo" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:2 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Hundreds of millions of patients are suffering from cirrhosis and other chronic liver diseases worldwide, and this public health problem continues to grow. It has been proven that liver fibrosis is reversible after the elimination of the etiology, especially in the early stage. Thus, early diagnosis of liver fibrosis is of vital importance for clinical treatment. Liver biopsy remains the gold standard for both diagnosis and staging of fibrosis, but is suboptimal, due in large parts to its invasive nature and sundry associated complications. To overcome this, a number of non-invasive diagnosis based on serum biomarkers or imaging modalities have been developed. While diagnosis based on serum biomarkers is cheaper and more acceptable to patients, almost none developed to date are liver-specific, and may engender a false positive error. The imaging modalities have evolved rapidly and are taking on more and more important roles in the diagnosis of liver fibrosis.</p></span>" ] "es" => array:2 [ "titulo" => "Resumen" "resumen" => "<span id="abst0010" class="elsevierStyleSection elsevierViewall"><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Cientos de millones de pacientes sufren cirrosis y otras enfermedades hepáticas crónicas en todo el mundo, y este problema de salud pública no cesa de crecer. Se ha demostrado que la fibrosis hepática es reversible tras la eliminación de su etiología, especialmente en una fase temprana. De este modo, el diagnóstico precoz de la fibrosis hepática resulta de crucial importancia para el tratamiento clínico. La biopsia de hígado sigue siendo el método de referencia tanto para el diagnóstico como para la estadificación de la fibrosis, pero se trata de un enfoque mejorable, debido en gran medida a su naturaleza invasiva y a las diversas complicaciones asociadas. Para superar estas limitaciones se han desarrollado diversas técnicas diagnósticas no invasivas basadas en biomarcadores séricos o técnicas de diagnóstico por imagen. A pesar de que el diagnóstico basado en biomarcadores séricos es menos costoso y resulta más aceptable para los pacientes, hasta la fecha prácticamente no se ha desarrollado ningún método que sea específico para el hígado, y esto puede dar lugar a falsos positivos. Las técnicas de diagnóstico por imagen han evolucionado rápidamente y están adoptando un papel cada vez más importante en el diagnóstico de la fibrosis hepática.</p></span>" ] ] "multimedia" => array:3 [ 0 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\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" scope="col" style="border-bottom: 2px solid black">Imaging modalities \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Advantages \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Flaws \t\t\t\t\t\t\n \t\t\t\t\t\t</th></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="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">CT</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Contrast-enhanced CT<a class="elsevierStyleCrossRef" href="#bib0350"><span class="elsevierStyleSup">10</span></a> \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Enormous potential in the early diagnosis of liver fibrosis, monitoring pathological angiogenesis. \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 " rowspan="2" align="left" valign="middle">Lower repeatability compared to US, radiation</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CTP<a class="elsevierStyleCrossRef" href="#bib0365"><span class="elsevierStyleSup">13</span></a> \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Short scanning time, high recognition rate, high accuracy in advanced fibrosis and cirrhosis. \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="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">US</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>TE<a class="elsevierStyleCrossRefs" href="#bib0410"><span class="elsevierStyleSup">22,24</span></a> \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">User-friendly, time-saving, widely-used, low cost, evaluating steatosis by CAP. \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Expensive equipment requirements, influenced by many factors such as obesity and food intake. \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>ARFI<a class="elsevierStyleCrossRef" href="#bib0390"><span class="elsevierStyleSup">18</span></a> \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">User-friendly, more reliable for severe liver fibrosis or cirrhosis than TE \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Expensive equipment requirements, influenced by obesity, sex and age. \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CEUS<a class="elsevierStyleCrossRefs" href="#bib0455"><span class="elsevierStyleSup">31,34</span></a> \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Safe for patients with renal failure, real-time evaluation, high temporal and in-plane spatial resolution \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">false positive HCC diagnosis, microbubble disruption \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="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">MRI</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>MRE<a class="elsevierStyleCrossRef" href="#bib0500"><span class="elsevierStyleSup">40</span></a> \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">High applicability and repeatability, evaluates the whole liver, more accurate than US for significant fibrosis. \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Expensive, time-consuming, specialized knowledge requirement, patients with metal implants and psychological illness are precluded. \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>DWI<a class="elsevierStyleCrossRefs" href="#bib0515"><span class="elsevierStyleSup">43,44</span></a> \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Good performance in patients with sclerosing cholangitis \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Influenced by common biologic factors such as inflammation, time-consuming controversial. \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>T1 ρ mapping<a class="elsevierStyleCrossRef" href="#bib0545"><span class="elsevierStyleSup">49</span></a> \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Resistant to the interference of fatty liver \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Novel, lack of robust clinical data. \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2264201.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Summary characteristics of imaging modalities.</p>" ] ] 1 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">US: ultrasonography; TE: transient elastography; 2D-SWE: two-dimensional share wave elastography; MRE: magnetic resonance elastography; CHB: chronic hepatitis B; CHC: chronic hepatitis C; NAFLD: non-alcoholic fatty liver disease; AIH: auto-immune hepatitis; PSC: primary sclerosing cholangitis.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td-with-role" title="\n \t\t\t\t\ttable-head\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">Diagnostic modalities \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="10" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">US</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="6" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">TE<a class="elsevierStyleCrossRefs" href="#bib0395"><span class="elsevierStyleSup">19,55–58</span></a></th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="4" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">2D-SWE<a class="elsevierStyleCrossRef" href="#bib0440"><span class="elsevierStyleSup">28</span></a></th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="5" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">MRE<a class="elsevierStyleCrossRefs" href="#bib0515"><span class="elsevierStyleSup">43,59,60</span></a></th></tr><tr title="table-row"><th 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" scope="col" style="border-bottom: 2px solid black">Etiologies \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">All \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">CHB \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">CHC \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">NAFLD \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">ALD \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">AIH<a class="elsevierStyleCrossRef" href="#bib0395"><span class="elsevierStyleSup">19</span></a> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">All \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">CHB \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">CHC \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">NAFLD \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">All \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">CHB \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">CHC \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">NAFLD \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">PSC \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Moderate fibrosis (≥F1) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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">7.0 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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">2.46 \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">2.48 \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">2.47 \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">3.45 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.41 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Significant fibrosis (≥F2) \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">8.5 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.85 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7.1 \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">11.0 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.8 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.25 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.95 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7.095 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7.15 \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">2.80 \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">2.73 \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">2.73 \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">3.66 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.26 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Advanced fibrosis (≥F3) \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">8.5 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10.80 \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="left" 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">11.4 \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="left" 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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10.4 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9.15 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.15 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9.15 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9.15 \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">3.77 \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">3.76 \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">3.71 \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">4.11 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Cirrhosis (F4) \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">14.6 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">17.05 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12.2 \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">14.0 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">16.0 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9.89 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10.90 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13.3 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11.0 \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">4.09 \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">4.16 \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">3.83 \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">4.71 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.93 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2264202.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Optimal cut-off values of TE, 2D-SWE and MRE in classifying liver fibrosis according to different etiologies.</p>" ] ] 2 => array:8 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at3" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Sensitivity, specificity, accuracy and AUROC of contrast-enhanced CT detecting fibrosis in different stages. AUROC: receiver operating characteristic curve; CLD: chronic liver dsease; N/A: not applicable.</p><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Sensitivity, specificity and AUROC of TE and ARFI detecting fibrosis in different stages under different cut-off values. AUROC: receiver operating characteristic curve; NAFLD: non-alcoholic fatty liver disease; CHC: chronic hepatitis C; N/A: not applicable.</p><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Sensitivity, specificity and AUROC of MRE, T1 ρ mapping and DWI detecting fibrosis in different stages under different cut-off values. AUROC: receiver operating characteristic curve; NAFLD: non-alcoholic fatty liver disease; PSC: primary sclerosing cholangitis; N/A: not applicable.</p>" "tablatextoimagen" => array:3 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td-with-role" title="\n \t\t\t\t\ttable-head\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">(A) Diagnostic modalities \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="4" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">CT</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="4" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Contrast-enhanced CT<a class="elsevierStyleCrossRef" href="#bib0345"><span class="elsevierStyleSup">9</span></a></th></tr><tr title="table-row"><th class="td-with-role" title="\n \t\t\t\t\ttable-head\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">Index \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Sensitivity (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Specificity (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Accuracy (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">AUROC \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr><tr title="table-row"><th 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" scope="col" style="border-bottom: 2px solid black">Disease \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="4" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Any CLD related liver fibrosis</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Moderate fibrosis (≥F1) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Significant fibrosis (≥F2) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">95.5 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">89.9 \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="left" 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><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.96 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Advanced fibrosis (≥F3) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">94.6 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">95.4 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">95 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.97 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Cirrhosis (F4) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">84.6 \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="left" 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><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">92.1 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.95 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2264203.png" ] ] 1 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td-with-role" title="\n \t\t\t\t\ttable-head\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">(B) Diagnostic modalities \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="14" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">US</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="4" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">TE<a class="elsevierStyleCrossRef" href="#bib0420"><span class="elsevierStyleSup">24</span></a></th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="5" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">pSWE<a class="elsevierStyleCrossRef" href="#bib0435"><span class="elsevierStyleSup">27</span></a></th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="5" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">2D-SWE<a class="elsevierStyleCrossRef" href="#bib0495"><span class="elsevierStyleSup">39</span></a></th></tr><tr title="table-row"><th class="td-with-role" title="\n \t\t\t\t\ttable-head\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">Index \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Cut-off (kPa) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Sensitivity (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Specificity (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">AUROC \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Cut-off (m/s) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Sensitivity (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Specificity (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Accuracy (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">AUROC \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Cut-off (kPa) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Sensitivity (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Specificity (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Accuracy (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">AUROC \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr><tr title="table-row"><th 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" scope="col" style="border-bottom: 2px solid black">Disease \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="4" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">NAFLD</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="5" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">CHC</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="5" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">CHB</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Moderate fibrosis (≥F1) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Significant fibrosis (≥F2) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.1 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">90 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">38 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.77 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.36 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">80.6 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">87.5 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">84.1 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.89 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7.6 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">92.0 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">90.0 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">92.1 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.97 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Advanced fibrosis (≥F3) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7.1 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">90 \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="left" 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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.8 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.45 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">90.3 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">87.5 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">88.5 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.94 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9.2 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">91.6 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">96.7 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">93.1 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.96 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Cirrhosis (F4) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10.9 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">90 \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="left" 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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.89 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.7 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">90.9 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">90.3 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">90.4 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.95 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10.4 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">94.6 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">94.9 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">94.7 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.98 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2264204.png" ] ] 2 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td-with-role" title="\n \t\t\t\t\ttable-head\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">(C) Diagnostic modalities \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="13" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">MRI</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="4" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">MRE<a class="elsevierStyleCrossRef" href="#bib0500"><span class="elsevierStyleSup">40</span></a></th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="4" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">T1 ρ mapping<a class="elsevierStyleCrossRef" href="#bib0540"><span class="elsevierStyleSup">48</span></a></th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="5" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">DWI<a class="elsevierStyleCrossRef" href="#bib0525"><span class="elsevierStyleSup">45</span></a></th></tr><tr title="table-row"><th class="td-with-role" title="\n \t\t\t\t\ttable-head\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">Index \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Cut-off (kPa) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Sensitivity (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Specificity (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">AUROC \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Cut-off (ms) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Sensitivity (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Specificity (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">AUROC \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Discrimination \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Cut-off (mm<span class="elsevierStyleSup">2</span>/s<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>10<span class="elsevierStyleSup">–3</span>) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Sensitivity (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Specificity (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">AUROC \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr><tr title="table-row"><th 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" scope="col" style="border-bottom: 2px solid black">Disease \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="4" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">NAFLD</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="4" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">CCl4-induced liver fibrosis rabbit models</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="5" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">PSC</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Moderate fibrosis (≥F1) \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">2.99 \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">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">90.6 \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">0.799 \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">62.1 \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">83.33 \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">83.33 \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">0.856 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">F1/0 from F2/3 \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">1.14 \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">91.7 \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">82.1 \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">0.926 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Significant fibrosis (≥F2) \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">3.62 \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">66.7 \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">95.7 \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">0.885 \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">79.45 \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">69.57 \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">92.31 \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">0.849 \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">F1/0 from F4 \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">1.09 \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">80 \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">92.9 \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">0.914 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Advanced fibrosis (≥F3) \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">3.62 \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">90.5 \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">93.3 \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">0.934 \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">79.44 \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">82.35 \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">84.21 \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">0.799 \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="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \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="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \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="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \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="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \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="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Cirrhosis (F4) \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">4.15 \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">88.9 \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">91.4 \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">0.882 \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">92.43 \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">80 \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">69.23 \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">0.692 \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="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \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="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \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="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \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="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \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="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2264200.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Indexes of different techniques assess different fibrosis degrees.</p>" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:60 [ 0 => array:3 [ "identificador" => "bib0305" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "The origin of fibrogenic myofibroblasts in fibrotic liver" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "T. 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Wu" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1371/journal.pone.0140068" "Revista" => array:5 [ "tituloSerie" => "PLoS ONE" "fecha" => "2015" "volumen" => "10" "paginaInicial" => "e0140068" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/26469342" "web" => "Medline" ] ] ] ] ] ] ] ] ] ] ] ] "agradecimientos" => array:1 [ 0 => array:4 [ "identificador" => "xack455536" "titulo" => "Acknowledgements" "texto" => "<p id="par0145" class="elsevierStylePara elsevierViewall">We are indebted to the National Natural Science Foundation of China (Nos. 81270007 to Li F. and 81670513 to Li F.); the Shanghai Talent Development Funds (Grant No. 201304 to Li F.); the Shanghai Rising-Star Program (Grant No. 13QA14007000 to Li F.); and the Shanghai Outstanding Young Talent Plan in Health System (Grant No. 13 YO56 to Li F.) for financial support. We also wish to thank Filipodia Publishing, LLC, for language editing service in English, making the expression in this manuscript more normative.</p>" "vista" => "all" ] ] ] "idiomaDefecto" => "en" "url" => "/02105705/0000004300000004/v1_202003250614/S0210570520300194/v1_202003250614/en/main.assets" "Apartado" => array:4 [ "identificador" => "48620" "tipo" => "SECCION" "es" => array:2 [ "titulo" => "Revisiones" "idiomaDefecto" => true ] "idiomaDefecto" => "es" ] "PDF" => "https://static.elsevier.es/multimedia/02105705/0000004300000004/v1_202003250614/S0210570520300194/v1_202003250614/en/main.pdf?idApp=UINPBA00004N&text.app=https://www.elsevier.es/" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0210570520300194?idApp=UINPBA00004N" ]
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Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, PR China