Deep-learning-based prediction of significant portal hypertension with single cross-sectional non-enhanced CT.

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Bibliographic Details
Title: Deep-learning-based prediction of significant portal hypertension with single cross-sectional non-enhanced CT.
Authors: Yamamoto A; Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan. loveakirayamamoto@gmail.com., Sato S; Department of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, USA., Ueda D; Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan., Walston SL; Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan., Kageyama K; Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan., Jogo A; Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan., Nakano M; Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan., Kotani K; Department of Hepatology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan., Uchida-Kobayashi S; Department of Hepatology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan., Kawada N; Department of Hepatology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan., Miki Y; Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.
Source: European radiology [Eur Radiol] 2026 Mar; Vol. 36 (3), pp. 1899-1908. Date of Electronic Publication: 2025 Sep 22.
Publication Type: Journal Article
Journal Info: Publisher: Springer International Country of Publication: Germany NLM ID: 9114774 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1084 (Electronic) Linking ISSN: 09387994 NLM ISO Abbreviation: Eur Radiol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1432-1084
DOI:10.1007/s00330-025-12010-4