A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH Cirrhosis

Autor: Don C. Rockey, Andrew H. Beck, Murray B. Resnick, Nezam H. Afdhal, Michael Christopher Montalto, Oscar Carrasco-Zevallos, Catherine Jia, Zachary Goodman, Harsha Pokkalla, Mitchell L. Shiffman, Zahil Shanis, Ling Han, Jaime Bosch, Manal F. Abdelmalek, Arun J. Sanyal, Quang Huy Le, Chuhan Chung, Robert P. Myers, Ilan Wapinski, Stephen A. Harrison, Dinkar Juyal

Publikováno v: Bosch, Jaime; Chung, Chuhan; Carrasco-Zevallos, Oscar M; Harrison, Stephen A; Abdelmalek, Manal F; Shiffman, Mitchell L; Rockey, Don C; Shanis, Zahil; Juyal, Dinkar; Pokkalla, Harsha; Le, Quang Huy; Resnick, Murray; Montalto, Michael; Beck, Andrew H; Wapinski, Ilan; Han, Ling; Jia, Catherine; Goodman, Zachary; Afdhal, Nezam; Myers, Robert P; ... (2021). A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH Cirrhosis. Hepatology, 74(6), pp. 3146-3160. Wiley 10.1002/hep.32087

BACKGROUND AND AIMS 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 (M

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6fc2a0c59dd1dd080f4daa05edc2ac5b
https://doi.org/10.1002/hep.32087