{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["158(2)"],"submitter":["Famularo S"],"pubmed_abstract":["<h4>Importance</h4>Clear indications on how to select retreatments for recurrent hepatocellular carcinoma (HCC) are still lacking.<h4>Objective</h4>To create a machine learning predictive model of survival after HCC recurrence to allocate patients to their best potential treatment.<h4>Design, setting, and participants</h4>Real-life data were obtained from an Italian registry of hepatocellular carcinoma between January 2008 and December 2019 after a median (IQR) follow-up of 27 (12-51) months. External validation was made on data derived by another Italian cohort and a Japanese cohort. Patients who experienced a recurrent HCC after a first surgical approach were included. Patients were profiled, and factors predicting survival after recurrence under different treatments that acted also as t"],"journal":["JAMA surgery"],"pagination":["192-202"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9857766"],"repository":["biostudies-literature"],"pubmed_title":["Machine Learning Predictive Model to Guide Treatment Allocation for Recurrent Hepatocellular Carcinoma After Surgery."],"pmcid":["PMC9857766"],"pubmed_authors":["Antonucci A","Molfino S","Russolillo N","Montuori M","Rossi M","Kawaguchi Y","Costa G","Valsecchi MG","Cosola D","Germani P","Zanello M","Braga M","Conci S","DE Peppo V","Farinati F","Delvecchio A","Chiarelli M","Griseri G","Abu Hilal M","Romano M","Ardito F","Notte F","Tarchi P","Romano F","Fazio F","Donadon M","Giani A","Ruzzenente A","DE Stefano F","Frena A","Milana F","Jovine E","La Barba G","Ratti F","Bernasconi DP","Fumagalli L","Aldrighetti L","Zago M","Cipriani F","Cucchetti A","Ercolani G","Giuffrida M","Conticchio M","Ferrari C","Manzoni A","Memeo R","Pinotti E","Dalla Valle R","Mori S","Famularo S","Iaria M","Dominioni T","Razionale F","Franceschi A","Hasegawa K","Carissimi F","Perri P","Corleone P","Maestri M","Ferrero A","Zimmitti G","Grazi GL","Marchitelli I","Piscaglia F","Zanus G","HE.RC.O.LE.S. Group","Lai Q","Larghi Laurerio Z","Patauner S","Salvador L","Torzilli G","Baiocchi GL","Giuliante F"],"additional_accession":[]},"is_claimable":false,"name":"Machine Learning Predictive Model to Guide Treatment Allocation for Recurrent Hepatocellular Carcinoma After Surgery.","description":"<h4>Importance</h4>Clear indications on how to select retreatments for recurrent hepatocellular carcinoma (HCC) are still lacking.<h4>Objective</h4>To create a machine learning predictive model of survival after HCC recurrence to allocate patients to their best potential treatment.<h4>Design, setting, and participants</h4>Real-life data were obtained from an Italian registry of hepatocellular carcinoma between January 2008 and December 2019 after a median (IQR) follow-up of 27 (12-51) months. External validation was made on data derived by another Italian cohort and a Japanese cohort. Patients who experienced a recurrent HCC after a first surgical approach were included. Patients were profiled, and factors predicting survival after recurrence under different treatments that acted also as t","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2026-05-28T02:14:32.255Z","creation":"2025-04-06T19:18:51.107Z"},"accession":"S-EPMC9857766","cross_references":{"pubmed":["36576813"],"doi":["10.1001/jamasurg.2022.6697"]}}