{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T18:23:32Z","timestamp":1791483812928,"version":"4.3.4"},"reference-count":31,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"6","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["Hepatology"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:sec>\n            <jats:title>Background and Aims<\/jats:title>\n            <jats:p>The hepatic venous pressure gradient (HVPG) is the standard for estimating portal pressure but requires expertise for interpretation. We hypothesized that HVPG could be extrapolated from liver histology using a machine learning (ML) algorithm.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Approach and Results<\/jats:title>\n            <jats:p>Patients with NASH with compensated cirrhosis from a phase 2b trial were included. HVPG and biopsies from baseline and weeks 48 and 96 were reviewed centrally, and biopsies evaluated with a convolutional neural network (PathAI, Boston, MA). Using trichrome\u2010stained biopsies in the training set (n\u00a0=\u00a0130), an ML model was developed to recognize fibrosis patterns associated with HVPG, and the resultant ML HVPG score was validated in a held\u2010out test set (n\u00a0=\u00a088). Associations between the ML HVPG score with measured HVPG and liver\u2010related events, and performance of the ML HVPG score for clinically significant portal hypertension (CSPH) (HVPG\u00a0\u2265\u00a010\u00a0mm Hg), were determined. The ML\u2010HVPG score was more strongly correlated with HVPG than hepatic collagen by morphometry (\u03c1\u00a0=\u00a00.47 vs. \u03c1\u00a0=\u00a00.28; <jats:italic toggle=\"yes\">P<\/jats:italic>\u00a0&lt;\u00a00.001). The ML HVPG score differentiated patients with normal (0\u20105\u00a0mm Hg) and elevated (5.5\u20109.5\u00a0mm Hg) HVPG and CSPH (median: 1.51 vs. 1.93 vs. 2.60; all <jats:italic toggle=\"yes\">P<\/jats:italic>\u00a0&lt;\u00a00.05). The areas under receiver operating characteristic curve (AUROCs) (95% CI) of the ML\u2010HVPG score for CSPH were 0.85 (0.80, 0.90) and 0.76 (0.68, 0.85) in the training and test sets, respectively. Discrimination of the ML\u2010HVPG score for CSPH improved with the addition of a ML parameter for nodularity, Enhanced Liver Fibrosis, platelets, aspartate aminotransferase (AST), and bilirubin (AUROC in test set: 0.85; 95% CI: 0.78, 0.92). Although baseline ML\u2010HVPG score was not prognostic, changes were predictive of clinical events (HR: 2.13; 95% CI: 1.26, 3.59) and associated with hemodynamic response and fibrosis improvement.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>An ML model based on trichrome\u2010stained liver biopsy slides can predict CSPH in patients with NASH with cirrhosis.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1002\/hep.32087","type":"journal-article","created":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T13:43:07Z","timestamp":1627825387000},"page":"3146-3160","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":64,"title":["A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH Cirrhosis"],"prefix":"10.1097","volume":"74","author":[{"given":"Jaime","family":"Bosch","sequence":"first","affiliation":[{"name":"Department of Biomedical Research,University of Bern,Bern,Switzerland"},{"name":"University of Barcelona\u2010IDIBAPS and CIBERehd,Barcelona,Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuhan","family":"Chung","sequence":"additional","affiliation":[{"name":"Gilead Sciences, Inc,Foster City,CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oscar M.","family":"Carrasco\u2010Zevallos","sequence":"additional","affiliation":[{"name":"PathAI, Inc.,Boston,MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen A.","family":"Harrison","sequence":"additional","affiliation":[{"name":"Pinnacle Clinical Research,San Antonio,TX"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manal F.","family":"Abdelmalek","sequence":"additional","affiliation":[{"name":"Duke University,Durham,NC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mitchell L.","family":"Shiffman","sequence":"additional","affiliation":[{"name":"Bon Secours Liver Institute of Richmond,Richmond,VA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Don C.","family":"Rockey","sequence":"additional","affiliation":[{"name":"Medical University of South Carolina,Charleston,SC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahil","family":"Shanis","sequence":"additional","affiliation":[{"name":"PathAI, Inc.,Boston,MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinkar","family":"Juyal","sequence":"additional","affiliation":[{"name":"PathAI, Inc.,Boston,MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harsha","family":"Pokkalla","sequence":"additional","affiliation":[{"name":"PathAI, Inc.,Boston,MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quang 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