Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors

Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors

Abstract Objective To develop and validate a computed tomography (CT)-based deep learning radiomics model to predict treatment response and progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) treated with transarterial chemoembolization (TACE)-hepatic arteri...

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Bibliographic Details
Main Authors: Linan Yin, Ruibao Liu, Wei Li, Shijie Li, Xunbo Hou
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BMC Gastroenterology
Subjects:
Deep learning model
Radiomics
Prognosis
Unresectable liver cancer
Online Access:https://doi.org/10.1186/s12876-024-03555-7
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https://doi.org/10.1186/s12876-024-03555-7

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