{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T16:40:29Z","timestamp":1789144829866,"version":"build-2803163510"},"reference-count":48,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"vor","delay-in-days":16,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Transplantation"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background and Aim<\/jats:title><jats:p>Liver transplant (LT) recipients may succumb to graft\u2010related pathologies, contributing to graft fibrosis (GF). Current methods to diagnose GF are limited, ranging from procedural\u2010related complications to low accuracy. With recent advances in machine learning (ML), we aimed to develop a noninvasive tool using demographic, clinical, laboratory, and B\u2010mode ultrasound (US) features to predict significant fibrosis (METAVIR\u2265F2).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used a nested 10\u2010fold cross\u2010validation approach with grid\u2010search for hyperparameter fine\u2010tuning to train an artificial neural network (ANN) and a support vector machine (SVM) to classify mild fibrosis (F0\u2010F1) and significant fibrosis (F2\u2010F4) on 1131 patients. We calculated Shapley values to identify top\u2010ranked features, determining the contribution of each feature to model predictions. For the imaging\u2010based model, we used 4819 images with 892 studies trained on the residual network 18 (ResNet18) model to classify F0\u2010F1 versus F3\u2010F4.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We determined the ANN performed the best when compared to the SVM and standard biomarkers, with an AUC ranging from 0.77 to 0.81. The ResNet18 model was unable to diagnose advanced GF, leading to the training AUCs ranging from 0.89 to 0.97, while the validation and testing AUCs were 0.43\u20130.63. Shapley analysis highlighted the following top\u2010ranked features associated with significant GF: hepatitis C at transplant, recipient age, recipient sex, and certain blood markers such as creatinine and hemoglobin.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Noninvasive approaches using ML for predicting significant GF perform well when considering demographic, clinical, and laboratory data; however, this performance is not enhanced with the use of US images.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ctr.70148","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T17:56:31Z","timestamp":1744912591000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data"],"prefix":"10.1111","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4820-1867","authenticated-orcid":false,"given":"Madhumitha","family":"Rabindranath","sequence":"first","affiliation":[{"name":"Ajmera Transplant Program University Health Network  Toronto Ontario Canada"},{"name":"Institute of Medical Science University of Toronto  Toronto Ontario Canada"},{"name":"Toronto General Hospital Research Institute, University Health Network  Toronto Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingji","family":"Sun","sequence":"additional","affiliation":[{"name":"Ajmera Transplant Program University Health Network  Toronto Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Korosh","family":"Khalili","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Princess Margaret Hospital  Toronto Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1960-8449","authenticated-orcid":false,"given":"Mamatha","family":"Bhat","sequence":"additional","affiliation":[{"name":"Ajmera Transplant Program University Health Network  Toronto Ontario Canada"},{"name":"Institute of Medical Science University of Toronto  Toronto Ontario Canada"},{"name":"Toronto General Hospital Research Institute, University Health Network  Toronto Ontario Canada"},{"name":"Division of Gastroenterology &amp; Hepatology Department of Medicine University of Toronto  Toronto Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/453875"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/lt.25338"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0185192"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cgh.2010.03.025"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.transproceed.2019.01.197"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.4254\/wjh.v7.i4.638"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.5500\/wjt.v11.i3.37"},{"key":"e_1_2_11_9_1","unstructured":"N. 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