Comparative evaluation of regression and machine-learning models for hepatocellular carcinoma risk stratification across diverse aetiologies

Background & Aims: We aimed to develop machine learning (ML) models for hepatocellular carcinoma (HCC) risk stratification in patients with cirrhosis and to test their ability to identify those with an annual HCC incidence >3%, for whom more intensive surveillance may be justified. Methods: Data...

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Hauptverfasser:	Pierre Nahon, Richard Layese, Pierre-André Natella, Lucia Parlati, Tounes Saidi, Nathalie Ganne-Carrié, Gisèle N’Kontchou, Cendrine Chaffaut, Jean-Charles Nault, Jessica Bamba-Funck, Angela Sutton, Clovis Lusivika Nzinga, Fabrice Carrat, Etienne Audureau
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Elsevier 2026-04-01
Schriftenreihe:	JHEP Reports
Schlagworte:	cirrhosis HCC risk stratification machine learning surveillance cost-effectiveness
Online-Zugang:	http://www.sciencedirect.com/science/article/pii/S2589555926000108
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