A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH Cirrhosis
| العنوان: | A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH Cirrhosis |
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| المؤلفون: | 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 |
| المصدر: | 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 <http://dx.doi.org/10.1002/hep.32087Test> |
| بيانات النشر: | Ovid Technologies (Wolters Kluwer Health), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Liver Cirrhosis, Male, Cirrhosis, Haemodynamic response, Bilirubin, Biopsy, Portal venous pressure, 610 Medicine & health, Machine learning, computer.software_genre, Diagnosis, Differential, Machine Learning, chemistry.chemical_compound, Clinical Trials, Phase II as Topic, Non-alcoholic Fatty Liver Disease, Fibrosis, Hypertension, Portal, Image Processing, Computer-Assisted, medicine, Humans, Randomized Controlled Trials as Topic, Hepatology, medicine.diagnostic_test, Receiver operating characteristic, business.industry, Middle Aged, Prognosis, medicine.disease, Portal Pressure, Liver, ROC Curve, chemistry, Liver biopsy, Portal hypertension, Female, Artificial intelligence, 610 Medizin und Gesundheit, business, computer |
| الوصف: | 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 (ML) algorithm. APPROACH AND RESULTS 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-stained biopsies in the training set (n = 130), an ML model was developed to recognize fibrosis patterns associated with HVPG, and the resultant ML HVPG score was validated in a held-out test set (n = 88). Associations between the ML HVPG score with measured HVPG and liver-related events, and performance of the ML HVPG score for clinically significant portal hypertension (CSPH) (HVPG ≥ 10 mm Hg), were determined. The ML-HVPG score was more strongly correlated with HVPG than hepatic collagen by morphometry (ρ = 0.47 vs. ρ = 0.28; P |
| وصف الملف: | application/pdf |
| تدمد: | 1527-3350 0270-9139 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6fc2a0c59dd1dd080f4daa05edc2ac5bTest https://doi.org/10.1002/hep.32087Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....6fc2a0c59dd1dd080f4daa05edc2ac5b |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15273350 02709139 |
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