Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Journal of hepatology ; h5-index 119.0

BACKGROUND AND AIMS : To develop algorithms based on machine learning predictive approaches to refine individualized predictions of hepatocellular carcinoma (HCC) risk according to HCV eradication in patients with cirrhosis included in the French ANRS CO12 CirVir cohort.

METHODS : Patients with compensated biopsy-proven HCV-cirrhosis were included in 35 centers and followed a semi-annual HCC surveillance program. Three prognostic models for HCC occurrence were built, using (1) Fine-Gray regression as a benchmark, (2) single decision tree (DT), and (3) random survival forest for competing risks survival (RSF). Model performance was evaluated from C-indexes validated externally in the ANRS CO22 Hepather cohort (N=668 enrolled between 08/2012-01/2014).

RESULTS : 836 patients were analyzed, among whom 156 (19%) developed HCC and 434 (52%) achieved sustained virological response (SVR) (median follow-up: 63 months). Fine-Gray regression models identified six independent predictors of HCC occurrence in patients before SVR: past excessive alcohol intake, genotype 1, elevated alpha-fetoprotein and GGT, low platelet count and albuminemia; and three in patients after SVR: elevated AST and low platelet count and PT. DT analysis confirmed these associations but revealed more complex interactions, yielding eight patient groups with differentiated cancer risks and varying predictors involved depending on SVR achievement. RSF ranked platelet count GGT, AFP and albuminemia as the most important predictors of HCC in non-SVR patients, and prothrombin time, ALT, age and platelet count after SVR achievement. Externally-validated C-indexes before/after SVR were 0.64/0.64 [Fine-Gray], 0.60/62 [DT] and 0.71/0.70 [RSF].

CONCLUSIONS : Risk factors for hepatocarcinogenesis differ according to SVR status. Machine learning algorithms can prove useful to individually assess HCC risk by revealing complex interactions between cancer predictors. Such approaches could help developing more cost-effective tailored surveillance programs.

Audureau Etienne, Carrat Fabrice, Layese Richard, Cagnot Carole, Asselah Tarik, Guyader Dominique, Larrey Dominique, De Lédinghen Victor, Ouzan Denis, Zoulim Fabien, Roulot Dominique, Tran Albert, Bronowicki Jean-Pierre, Zarski Jean-Pierre, Riachi Ghassan, Calès Paul, Péron Jean-Marie, Alric Laurent, Bourlière Marc, Mathurin Philippe, Blanc Jean-Frédéric, Abergel Armand, Chazouillères Olivier, Mallat Ariane, Grangé Jean-Didier, Attali Pierre, d’Alteroche Louis, Wartelle Claire, Dao Thông, Thabut Dominique, Pilette Christophe, Silvain Christine, Christidis Christos, Nguyen-Khac Eric, Bernard-Chabert Brigitte, Zucman David, Di Martino Vincent, Sutton Angela, Pol Stanislas, Nahon Pierre, Nahon Pierre, Marcellin Patrick, Guyader Dominique, Pol Stanislas, Fontaine Hélène, Larrey Dominique, De Lédinghen Victor, Ouzan Denis, Zoulim Fabien, Roulot Dominique, Tran Albert, Bronowicki Jean-Pierre, Zarski Jean-Pierre, Leroy Vincent, Riachi Ghassan, Calès Paul, Péron Jean-Marie, Alric Laurent, Bourlière Marc, Mathurin Philippe, Dharancy Sebastien, Blanc Jean-Frédéric, Abergel Armand, Chazouillères Olivier, Mallat Ariane, Grangé Jean-Didier, Attali Pierre, Louis d’Alteroche Wartelle, Claire Dao, Thông Thabut, Dominique Pilette, Christophe Silvain, Christine Christidis, Christos Nguyen-Khac, Eric Bernard-Chabert, Brigitte Zucman, David Di Martino

2020-Jun-29

HCV clearance, cirrhosis, liver cancer, machine learning, screening