Early prediction of adverse outcomes in liver cirrhosis using a CT-based multimodal deep learning model.

Purpose: Early-stage cirrhosis frequently presents without symptoms, making timely identification of high-risk patients challenging. We aimed to develop a deep learning-based triple-modal fusion liver cirrhosis network (TMF-LCNet) for the prediction of adverse outcomes, offering a promising tool to...

Full description

Bibliographic Details

Published in:	Abdominal Radiology Vol. 51; no. 1; pp. 137 - 151
Main Authors:	Xie, Nanai, Liang, Yiwen, Luo, Zixin, Hu, Jing, Ge, Ruiquan, Wan, Xiang, Wang, Changmiao, Zou, Guannan, Guo, Feng, Jiang, Yi
Format:	Journal Article
Published:	Springer Nature Jan2026
Online Access:	View this record in EBSCOhost