{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T06:40:06Z","timestamp":1763620806239,"version":"3.40.5"},"reference-count":30,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2017BH044"],"award-info":[{"award-number":["ZR2017BH044"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2021,4]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Liver fibrosis is a common process resulting from various etiologies. Sustained progression of liver fibrosis leads to cirrhosis, even hepatocellular carcinoma. Thus, noninvasive staging of liver fibrosis is of clinical importance. Radiomics is an emerging approach for staging liver fibrosis. However, the feature selection methods and classifier models are complicated, and may result in a discrepancy of diagnostic performance owing to different radiomics models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To identify the optimal feature selection and classifier methods for predicting liver fibrosis by using nonenhanced T<jats:sub>1<\/jats:sub>\u2010weighted imaging.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Prospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Animal Model<\/jats:title><jats:p>Wistar rats, total 97.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>3T, 3D T<jats:sub>1<\/jats:sub>\u2010weighted images with fast\u2010spoiled gradient echo (FSPGR).<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>Liver fibrosis rats were induced via subcutaneous injection of a mixture of carbon tetrachloride. Rats in the control group were injected with saline. Segmentation and feature extraction were performed by 3D slicer and the image biomarker explorer (IBEX) software package. Data preprocessing, feature selection, model building, and model comparative evaluation were conducted with Python. The liver fibrosis stage was determined by pathological examination.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Receiver operating characteristic curve, fuzzy comprehensive evaluation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>For discriminating between F0 and F1\u20102, F0 and F3\u20104, F0 and F1\u20104, F0\u20101 and F2\u20104, F0\u20102 and F3\u20104, and F0\u20103 and F4, the accuracies of 12 radiomics models were 77.27\u201390.91%, 73.33\u201386.67%, 80.56\u201391.67%, 74.07\u201388.89%, 76.47\u201388.24%, and 79.49\u201392.31%, respectively. The AUCs of the radiomics models were 0.86\u20130.97, 0.85\u20130.95, 0.89\u20130.97, 0.81\u20130.96, 0.82\u20130.93, and 0.85\u20130.96, respectively. The least absolute shrinkage and selection operator \/ support vector machine (LASSO\u2010SVM) model had high AUCs of 0.93\u20130.97. For discriminating between F0 and F1\u20102, F0 and F3\u20104, F0 and F1\u20104, F0\u20101 and F2\u20104, and F0\u20102 and F3\u20104, the fuzzy comprehensive evaluation showed that the LASSO\u2010SVM model had a high fuzzy score\/order of 0.087\u20130.091\/1.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>LASSO\u2010SVM appears to be the optimal model for predicting liver fibrosis by using nonenhanced T<jats:sub>1<\/jats:sub>\u2010weighted imaging in a rodent model of liver fibrosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>2.<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy Stage<\/jats:title><jats:p>2.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.27391","type":"journal-article","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T18:09:39Z","timestamp":1602526179000},"page":"1080-1089","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced <scp>T<sub>1<\/sub><\/scp>\u2010Weighted Imaging: Comparison of Different Radiomics Models"],"prefix":"10.1002","volume":"53","author":[{"given":"Ming","family":"Ni","sequence":"first","affiliation":[{"name":"Department of Radiology The Affiliated Hospital of Qingdao University  Qingdao China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lili","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Pathology The Affiliated Hospital of Qingdao University  Qingdao 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