{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T12:10:30Z","timestamp":1780402230896,"version":"3.54.1"},"reference-count":28,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T00:00:00Z","timestamp":1695254400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61003175"],"award-info":[{"award-number":["61003175"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["201602228"],"award-info":[{"award-number":["201602228"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["20170520153"],"award-info":[{"award-number":["20170520153"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Precise staging of hepatic fibrosis with MRI is necessary as it can assist precision medicine. Computer aided diagnosis (CAD) system with distinguishing radiomics features and radiologists domain knowledge is expected to obtain 5\u2010grade meta\u2010analysis of histological data in viral hepatitis (METAVIR)\u00a0staging.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study aims to obtain the precise staging of hepatic fibrosis based on MRI as it predicts the risk of future liver\u2010related morbidity and the need for treatment, monitoring and surveillance. Based on METAVIR score, fibrosis can be divided into five stages. Most previous researches focus on binary classification, such as cirrhosis versus non\u2010cirrhosis, early versus advanced fibrosis, and substantial fibrosis or not. In this paper, a comprehensive CAD system TMM is proposed to precisely class hepatic fibrosis into five stages for precision medicine instead of the common binary\u00a0classification.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We propose a novel hepatic fibrosis staging CAD system TMM which includes three modules, Two\u2010level Image Statistical Radiomics Feature (TISRF), Monotonic Error Correcting Output Codes (MECOC) and Monotone Multiclassification with Deep Forest (MMDF). TISRF extracts radiomics features for distinguishing different hepatic fibrosis stages. MECOC is proposed to encode monotonic multiclass by making full use of the progressive severity of hepatic fibrosis and increase the fault tolerance and error correction ability. MMDF combines multiple Deep Forest network to ensure the final five\u2010class classification, which can achieve more precise classification than the common binary classification. The performance of the proposed hepatic fibrosis CAD system is tested on the hepatic data collected from our rabbits models of\u00a0fibrosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 140 regions of interest (ROI) are selected from MRI T1W of liver fibrosis models in 35 rabbits with F0(<jats:italic>n<\/jats:italic>\u00a0=\u00a016), F1(<jats:italic>n<\/jats:italic>\u00a0=\u00a028), F2(<jats:italic>n<\/jats:italic>\u00a0=\u00a029), F3(<jats:italic>n<\/jats:italic>\u00a0=\u00a044) and F4(<jats:italic>n<\/jats:italic>\u00a0=\u00a023). The performance is evaluated by five\u2010fold cross\u2010validation. TMM can achieve the highest total accuracy of 72.14% for five fibrosis stages compared with other popular classifications. To make a comprehensive comparison, a binary classification experiment have been carried out.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>T1WI can obtain precise staging of hepatic fibrosis with the help of comprehensive CAD including radiomics features extraction inspired by radiologists, monotonic multiclass according to the severity of hepatic fibrosis, and deep learning\u00a0classification.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16700","type":"journal-article","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T20:55:01Z","timestamp":1695329701000},"page":"2032-2043","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["TMM: A comprehensive CAD system for hepatic fibrosis 5\u2010grade METAVIR staging based on liver MRI"],"prefix":"10.1002","volume":"51","author":[{"given":"Hui","family":"Liu","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering Dalian University of Technology &amp; Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaqing","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Dalian University of Technology &amp; Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongmei","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Radiology Second Affiliated Hospital Dalian Medical University Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology Second Affiliated Hospital Dalian Medical University Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilin","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Dalian University of Technology &amp; Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aoran","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Dalian University of Technology &amp; Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengjun","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Dalian University of Technology &amp; Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,9,21]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.joim.2020.03.001"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.296095512"},{"key":"e_1_2_9_4_1","first-page":"1","article-title":"Liver fibrosis: Difficulties in diagnostic and treatment: a review","volume":"1","author":"Bert F","year":"2017","journal-title":"Gastro Med Res"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11901-014-0237-3"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1515\/rjim-2015-0039"},{"key":"e_1_2_9_7_1","unstructured":"MaRX.Studies of the liver fibrosis staging based on ultrasound images. Harbin Institute of Technology;2017."},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.18.20284"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-06831-8"},{"key":"e_1_2_9_10_1","first-page":"899","article-title":"Investigation on diagnosing hepatic fibrosis with X\u2010ray diffraction\u2010enhanced imaging","volume":"50","author":"Wang M","year":"2018","journal-title":"Beijing Da Xue Xue Bao Yi Xue Ban"},{"key":"e_1_2_9_11_1","first-page":"228","article-title":"Multi\u2010stage optimization support vector machine by analysis of non\u2010linear normalization texture for early evaluation staging liver fibrosis","volume":"13","author":"Faghihi R","year":"2019","journal-title":"PJMHS"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/1\/4\/045012"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2015.09.003"},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0118297","article-title":"Hepatitis C related chronic liver cirrhosis: feasibility of texture analysis of MR images for classification of fibrosis stage and necroinflammatory activity grade","volume":"10","author":"Zhuo W","year":"2015","journal-title":"PLoS One"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12112737"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.23759"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2942774"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3240216"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-34300-2"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2015.12.009"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.10.006"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13521"},{"key":"e_1_2_9_23_1","first-page":"421","article-title":"Machine learning model based on MRI plain scan for evaluation on hepatic fibrosis staging of rabbits","volume":"7","author":"Li S","year":"2021","journal-title":"Chinese J Interv Imaging Ther"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/hep.510240201"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.fct.2009.05.038"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.04.038"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.105"},{"key":"e_1_2_9_28_1","doi-asserted-by":"crossref","unstructured":"ZhouZ\u2010H FengJ.Deep forest: Towards an alternative to deep neural networks. In:Proceedings of the Twenty\u2010Sixth International Joint Conference on Artificial Intelligence. IJCAI\u201017;2017;3553\u20103559.","DOI":"10.24963\/ijcai.2017\/497"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2021.666138"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.16700","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:08:54Z","timestamp":1710256134000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.16700"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,21]]},"references-count":28,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["10.1002\/mp.16700"],"URL":"https:\/\/doi.org\/10.1002\/mp.16700","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,21]]},"assertion":[{"value":"2023-02-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-26","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}