{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T04:25:47Z","timestamp":1785039947096,"version":"3.55.0"},"reference-count":65,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T00:00:00Z","timestamp":1613606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 498 81971583"],"award-info":[{"award-number":["No. 498 81971583"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No. 2018YFC1312900"],"award-info":[{"award-number":["No. 2018YFC1312900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["No. 2017SHZDZX01"],"award-info":[{"award-number":["No. 2017SHZDZX01"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["No. 2018SHZDZX01"],"award-info":[{"award-number":["No. 2018SHZDZX01"]}]},{"DOI":"10.13039\/501100008750","name":"Shanghai Hospital Development Center","doi-asserted-by":"publisher","award":["No. SHDC12019128"],"award-info":[{"award-number":["No. SHDC12019128"]}],"id":[{"id":"10.13039\/501100008750","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomolecules"],"abstract":"<jats:p>Accurate grading of liver fibrosis can effectively assess the severity of liver disease and help doctors make an appropriate diagnosis. This study aimed to perform the automatic staging of hepatic fibrosis on patients with hepatitis B, who underwent gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging with dynamic radiomics analysis. The proposed dynamic radiomics model combined imaging features from multi-phase dynamic contrast-enhanced (DCE) images and time-domain information. Imaging features were extracted from the deep learning-based segmented liver volume, and time-domain features were further explored to analyze the variation in features during contrast enhancement. Model construction and evaluation were based on a 132-case data set. The proposed model achieved remarkable performance in significant fibrosis (fibrosis stage S1 vs. S2\u2013S4; accuracy (ACC) = 0.875, area under the curve (AUC) = 0.867), advanced fibrosis (S1\u2013S2 vs. S3\u2013S4; ACC = 0.825, AUC = 0.874), and cirrhosis (S1\u2013S3 vs. S4; ACC = 0.850, AUC = 0.900) classifications in the test set. It was more dominant compared with the conventional single-phase or multi-phase DCE-based radiomics models, normalized liver enhancement, and some serological indicators. Time-domain features were found to play an important role in the classification models. The dynamic radiomics model can be applied for highly accurate automatic hepatic fibrosis staging.<\/jats:p>","DOI":"10.3390\/biom11020307","type":"journal-article","created":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T20:56:21Z","timestamp":1613681781000},"page":"307","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Imaging-Based Staging of Hepatic Fibrosis in Patients with Hepatitis B: A Dynamic Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6397-0192","authenticated-orcid":false,"given":"Rencheng","family":"Zheng","sequence":"first","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China"},{"name":"Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8034-406X","authenticated-orcid":false,"given":"Chunzi","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8890-4973","authenticated-orcid":false,"given":"Chengyan","family":"Wang","sequence":"additional","affiliation":[{"name":"Human Phenome Institute, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nannan","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weibo","family":"Chen","sequence":"additional","affiliation":[{"name":"Market Solutions Center, Philips Healthcare, Shanghai 200072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2053-9439","authenticated-orcid":false,"given":"He","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China"},{"name":"Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China"},{"name":"Human Phenome Institute, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1172\/JCI24282","article-title":"Liver fibrosis","volume":"115","author":"Bataller","year":"2005","journal-title":"J. Clin. Investig."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"European Association for the Study of the Liver (2015). EASL-ALEH Clinical Practice Guidelines: Non-invasive tests for evaluation of liver disease severity and prognosis. J. Hepatol., 63, 237\u2013264.","DOI":"10.1016\/j.jhep.2015.04.006"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1053\/jhep.2000.16602","article-title":"Practices of liver biopsy in France: Results of a prospective nationwide survey. For the Group of Epidemiology of the French Association for the Study of the Liver (AFEF)","volume":"32","author":"Cadranel","year":"2000","journal-title":"Hepatology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12072-016-9760-3","article-title":"Asian-Pacific Association for the Study of the Liver (APASL) consensus guidelines on invasive and non-invasive assessment of hepatic fibrosis: A 2016 update","volume":"11","author":"Shiha","year":"2017","journal-title":"Hepatol. Int."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"91","DOI":"10.4292\/wjgpt.v7.i1.91","article-title":"Clinical applications, limitations and future role of transient elastography in the management of liver disease","volume":"7","author":"Chang","year":"2016","journal-title":"World J. Gastrointest. Pharmacol. Ther."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1007\/s00330-015-3949-z","article-title":"Magnetic resonance elastography for staging liver fibrosis in non-alcoholic fatty liver disease: A diagnostic accuracy systematic review and individual participant data pooled analysis","volume":"26","author":"Singh","year":"2016","journal-title":"Eur. Radiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1080\/23808993.2016.1164013","article-title":"Radiomics: A new application from established techniques","volume":"1","author":"Parekh","year":"2016","journal-title":"Expert Rev. Precis. Med. Drug Dev."},{"key":"ref_8","first-page":"A96","article-title":"IDDF2018-ABS-0098 Preoperative prediction of microvascular invasion in hepatocellular cancer: A radiomics model using GD-EOB-DTPA enhanced MRI","volume":"67","author":"Tang","year":"2018","journal-title":"Gut"},{"key":"ref_9","first-page":"1","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1136\/gutjnl-2018-316407","article-title":"Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab","volume":"69","author":"Dohan","year":"2020","journal-title":"Gut"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1109\/TBME.2012.2235438","article-title":"Classification and staging of chronic liver disease from multimodal data","volume":"60","author":"Ribeiro","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Duan, J., Hu, C., Luo, S., Zhao, X., and Wang, T. (2013). Microcomputed tomography with diffraction-enhanced imaging for morphologic characterization and quantitative evaluation of microvessel of hepatic fibrosis in rats. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0078176"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.compmedimag.2015.09.003","article-title":"Effective staging of fibrosis by the selected texture features of liver: Which one is better, CT or MR imaging?","volume":"46","author":"Zhang","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"117","DOI":"10.2214\/AJR.07.2070","article-title":"Computer-aided diagnosis of hepatic fibrosis: Preliminary evaluation of MRI texture analysis using the finite difference method and an artificial neural network","volume":"189","author":"Kato","year":"2007","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1002\/jmri.24536","article-title":"Texture-based classification of liver fibrosis using MRI","volume":"41","author":"House","year":"2015","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1816","DOI":"10.1007\/s00261-019-01931-6","article-title":"Diagnostic value of MR-based texture analysis for the assessment of hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD)","volume":"44","author":"Cannella","year":"2019","journal-title":"Abdom. Radiol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, Z., Matsui, O., Kitao, A., Kozaka, K., Koda, W., Kobayashi, S., Ryu, Y., Minami, T., Sanada, J., and Gabata, T. (2015). Hepatitis C related chronic liver cirrhosis: Feasibility of texture analysis of MR images for classification of fibrosis stage and necroinflammatory activity grade. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0118297"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1148\/radiol.2018181197","article-title":"Radiomics analysis of gadoxetic acid\u2013enhanced MRI for staging liver fibrosis","volume":"290","author":"Park","year":"2019","journal-title":"Radiology"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1532-429X-12-69","article-title":"Shortened Modified Look-Locker Inversion recovery (ShMOLLI) for clinical myocardial T1-mapping at 1.5 and 3 T within a 9 heartbeat breathhold","volume":"12","author":"Piechnik","year":"2010","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1148\/radiol.2371031842","article-title":"Efficacy and safety of MR imaging with liver-specific contrast agent: U.S. multicenter phase III study","volume":"237","author":"Bluemke","year":"2005","journal-title":"Radiology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3002","DOI":"10.1016\/j.ejrad.2012.03.029","article-title":"Relationship between signal intensity on hepatobiliary phase of gadolinium ethoxybenzyl diethylenetriaminepentaacetic acid (Gd-EOB-DTPA)-enhanced MR imaging and prognosis of borderline lesions of hepatocellular carcinoma","volume":"81","author":"Kobayashi","year":"2012","journal-title":"Eur. J. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep15408","article-title":"Liver fibrosis and Gd-EOB-DTPA-enhanced MRI: A histopathologic correlation","volume":"5","author":"Verloh","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1097\/RLI.0b013e318289ff8f","article-title":"Comparison of magnetic resonance elastography and gadoxetate disodium\u2013enhanced magnetic resonance imaging for the evaluation of hepatic fibrosis","volume":"48","author":"Choi","year":"2013","journal-title":"Invest. Radiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1148\/radiol.13122482","article-title":"Liver fibrosis: Histopathologic and biochemical influences on diagnostic efficacy of hepatobiliary contrast-enhanced MR imaging in staging","volume":"269","author":"Feier","year":"2013","journal-title":"Radiology"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e0215752","DOI":"10.1371\/journal.pone.0215752","article-title":"Influence of hepatic fibrosis and inflammation: Correlation between histopathological changes and Gd-EOB-DTPA-enhanced MR imaging","volume":"14","author":"Verloh","year":"2019","journal-title":"PLoS ONE"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep43347","article-title":"Gd-EOB-DTPA-enhanced MRI for evaluation of liver function: Comparison between signal-intensity-based indices and T1 relaxometry","volume":"7","author":"Haimerl","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1007\/s00330-015-3919-5","article-title":"Volume-assisted estimation of liver function based on Gd-EOB-DTPA-enhanced MR relaxometry","volume":"26","author":"Haimerl","year":"2016","journal-title":"Eur. Radiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1148\/radiol.2015150619","article-title":"Elastography assessment of liver fibrosis: Society of radiologists in ultrasound consensus conference statement","volume":"276","author":"Barr","year":"2015","journal-title":"Radiology"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1148\/radiol.13122208","article-title":"Liver fibrosis: Noninvasive assessment with acoustic radiation force impulse elastography\u2014Comparison with FibroScan M and XL probes and FibroTest in patients with chronic liver disease","volume":"269","author":"Cassinotto","year":"2013","journal-title":"Radiology"},{"key":"ref_30","first-page":"e212","article-title":"Performance of Acoustic Radiation Force Impulse imaging for the staging of liver fibrosis: A pooled meta-analysis","volume":"19","author":"Nierhoff","year":"2012","journal-title":"J. Viral Hepat."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1016\/j.ultrasmedbio.2012.02.032","article-title":"Intra- and interoperator reproducibility of acoustic radiation force impulse (ARFI) elastography\u2014Preliminary results","volume":"38","author":"Bota","year":"2012","journal-title":"Ultrasound Med. Biol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1002\/jmri.23731","article-title":"Magnetic resonance elastography of liver: Technique, analysis, and clinical applications","volume":"37","author":"Venkatesh","year":"2013","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1148\/radiol.2402050606","article-title":"MR elastography of the liver: Preliminary results","volume":"240","author":"Rouviere","year":"2006","journal-title":"Radiology"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1053\/j.gastro.2008.03.076","article-title":"Magnetic resonance elastography for the noninvasive staging of liver fibrosis","volume":"135","author":"Huwart","year":"2008","journal-title":"Gastroenterology"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1002\/mrm.21636","article-title":"Assessment of liver viscoelasticity using multifrequency MR elastography","volume":"60","author":"Asbach","year":"2008","journal-title":"Magn. Reson. Med."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"59","DOI":"10.4329\/wjr.v8.i1.59","article-title":"General review of magnetic resonance elastography","volume":"8","author":"Low","year":"2016","journal-title":"World J. Radiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1136\/gutjnl-2018-316204","article-title":"Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: A prospective multicentre study","volume":"68","author":"Wang","year":"2019","journal-title":"Gut"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4578","DOI":"10.1007\/s00330-018-5499-7","article-title":"Deep learning for staging liver fibrosis on CT: A pilot study","volume":"28","author":"Yasaka","year":"2018","journal-title":"Eur. Radiol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1148\/radiol.2017171928","article-title":"Liver fibrosis: Deep convolutional neural network for staging by using gadoxetic acid\u2013enhanced hepatobiliary phase MR images","volume":"287","author":"Yasaka","year":"2018","journal-title":"Radiology"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1148\/radiol.2018180763","article-title":"Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent\u2013enhanced CT Images in the Liver","volume":"289","author":"Choi","year":"2018","journal-title":"Radiology"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/0168-8278(91)90084-O","article-title":"Classification of chronic viral hepatitis: A need for reassessment","volume":"13","author":"Scheuer","year":"1991","journal-title":"J. Hepatol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1002\/hep.27382","article-title":"Comparison of diagnostic accuracy of aspartate aminotransferase to platelet ratio index and fibrosis-4 index for detecting liver fibrosis in adult patients with chronic hepatitis B virus infection: A systemic review and meta-analysis","volume":"61","author":"Xiao","year":"2015","journal-title":"Hepatology"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1053\/jhep.2003.50346","article-title":"A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C","volume":"38","author":"Wai","year":"2003","journal-title":"Hepatology"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1002\/hep.21178","article-title":"Development of a simple noninvasive index to predict significant fibrosis in patients with HIV\/HCV coinfection","volume":"43","author":"Sterling","year":"2006","journal-title":"Hepatology"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","article-title":"Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain","volume":"12","author":"Avants","year":"2008","journal-title":"Med. Image Anal."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., and Ronneberger, O. (2016, January 17\u201321). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2016, Athens, Greece.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref_47","first-page":"1573","article-title":"Image thresholding techniques: A survey over categories","volume":"34","author":"Sankur","year":"2001","journal-title":"Pattern Recognit."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"Fedorov","year":"2017","journal-title":"Cancer Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"10312","DOI":"10.1038\/srep10312","article-title":"Application of high-dimensional feature selection: Evaluation for genomic prediction in man","volume":"5","author":"Bermingham","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_52","first-page":"321","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Int. Res."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Blagus, R., and Lusa, L. (2013). SMOTE for high-dimensional class-imbalanced data. BMC Bioinformatics, 14.","DOI":"10.1186\/1471-2105-14-106"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Avanzo, M., Pirrone, G., Vinante, L., Caroli, A., Stancanello, J., Drigo, A., Massarut, S., Mileto, M., Urbani, M., and Trovo, M. (2020). Electron density and Biologically Effective Dose (BED) radiomics-based machine learning models to predict late radiation-induced subcutaneous fibrosis. Front. Oncol., 10.","DOI":"10.3389\/fonc.2020.00490"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"S331","DOI":"10.1016\/S1590-8658(11)60589-6","article-title":"Chronic viral hepatitis: The histology report","volume":"43","author":"Guido","year":"2011","journal-title":"Dig. Liver Dis."},{"key":"ref_56","first-page":"110","article-title":"Liver biopsy interpretation in chronic hepatitis","volume":"33","author":"Rahn","year":"2001","journal-title":"J. Insur. Med."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1148\/radiol.10100621","article-title":"Staging hepatic fibrosis: Comparison of gadoxetate disodium\u2013enhanced and diffusion-weighted MR imaging\u2014preliminary observations","volume":"259","author":"Watanabe","year":"2011","journal-title":"Radiology"},{"key":"ref_58","unstructured":"Walker, H.K., Hall, W.D., and Hurst, J.W. (1990). Evaluation of the Size, Shape, and Consistency of the Liver. Clinical Methods: The History, Physical, and Laboratory Examinations, Butterworths. [3rd ed.]."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.mri.2003.09.001","article-title":"Influence of MRI acquisition protocols and image intensity normalization methods on texture classification","volume":"22","author":"Collewet","year":"2004","journal-title":"Magn. Reson. Imaging"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.ejmp.2018.05.017","article-title":"Evaluation of radiomic texture feature error due to MRI acquisition and reconstruction: A simulation study utilizing ground truth","volume":"50","author":"Yang","year":"2018","journal-title":"Phys. Med."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3054","DOI":"10.1002\/mp.14173","article-title":"Multicenter evaluation of MRI-based radiomic features: A phantom study","volume":"47","author":"Rai","year":"2020","journal-title":"Med. Phys."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ford, J., Dogan, N., Young, L., and Yang, F. (2018). Quantitative radiomics: Impact of pulse sequence parameter selection on MRI-based textural features of the brain. Contrast Media Mol. Imaging, 2018.","DOI":"10.1155\/2018\/1729071"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.3109\/0284186X.2013.812798","article-title":"Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability","volume":"52","author":"Leijenaar","year":"2013","journal-title":"Acta Oncol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1593\/tlo.13844","article-title":"Reproducibility and prognosis of quantitative features extracted from CT images","volume":"7","author":"Balagurunathan","year":"2014","journal-title":"Transl. Oncol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.1109\/TMI.2019.2894692","article-title":"A universal intensity standardization method based on a many-to-one weak-paired cycle generative adversarial network for magnetic resonance images","volume":"38","author":"Gao","year":"2019","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Biomolecules"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-273X\/11\/2\/307\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:25:40Z","timestamp":1760160340000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-273X\/11\/2\/307"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,18]]},"references-count":65,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["biom11020307"],"URL":"https:\/\/doi.org\/10.3390\/biom11020307","relation":{},"ISSN":["2218-273X"],"issn-type":[{"value":"2218-273X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,18]]}}}