Author information

Collaborators

• HE.RC.O.LE.S. Group: Guido Costa, Flavio Milana, Francesca Ratti, Nadia Russolillo, Francesco Razionale, Alessandro Giani, Francesca Carissimi, Mario Giuffrida, Valerio DE Peppo, Ivan Marchitelli, Francesca DE Stefano, Zoe Larghi Laurerio, Alessandro Cucchetti, Francesca Notte, Davide Cosola, Pio Corleone, Alberto Manzoni, Mauro Montuori, Angelo Franceschi, Luca Salvador, Maria Conticchio, Marco Braga, Silvia Mori

Affiliations

• ¹Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
• ²Department of Hepatobiliary and General Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.
• ³Hepatobiliary Surgery Division, "Vita e Salute" University, Ospedale San Raffaele IRCCS, Milano, Italy.
• ⁴Department of General and Oncological Surgery, Mauriziano Hospital "Umberto I", Turin, Italy.
• ⁵Hepatobiliary Surgery Unit, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Catholic University of the Sacred Heart, Rome, Italy.
• ⁶Department of Medicine and Surgery, University of Parma, Parma, Italy.
• ⁷Division of Hepatobiliarypancreatic Unit, IRCCS - Regina Elena National Cancer Institute, Rome, Italy.
• ⁸Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, Verona, Italy.
• ⁹Unit of General Surgery 1, University of Pavia and Foundation IRCCS Policlinico San Matteo, Pavia, Italy.
• ¹⁰General Surgery and Organ Transplantation Unit, Sapienza University of Rome, Umberto I Polyclinic of Rome, Rome, Italy.
• ¹¹General and Oncologic Surgery, Morgagni-Pierantoni Hospital, Department of Medical and Surgical Sciences - University of Bologna, Forlì, Italy.
• ¹²Department of General and Pediatric Surgery, Bolzano Central Hospital, Bolzano, Italy.
• ¹³Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.
• ¹⁴Division of General Surgery, Department of Medical and Surgical Sciences, ASUGI, Trieste, Italy.
• ¹⁵Department of General Surgery, Poliambulanza Foundation Hospital, Brescia, Italy.
• ¹⁶Department of Surgery, Ponte San Pietro Hospital, Bergamo, Italy.
• ¹⁷Alma Mater Studiorum, University of Bologna, AOU Sant'Orsola Malpighi, IRCCS at Maggiore Hospital, Bologna, Italy.
• ¹⁸Department of Emergency and Robotic Surgery, ASST Lecco, Lecco, Italy.
• ¹⁹HPB Surgical Unit, San Paolo Hospital, Savona, Italy.
• ²⁰Department of Surgical, Oncological and Gastroenterological Science (DISCOG), University of Padua, Padua, Italy.
• ²¹Hepatobiliary and Pancreatic Surgery Unit-Treviso Hospital, Treviso, Italy.
• ²²Department of Hepato-Pancreatic-Biliary Surgery, Miulli Hospital, Bari, Italy.
• ²³Bicocca Bioinformatics Biostatistics and Bioimaging Centre-B4, School of Medicine and Surgery, University of Milan - Bicocca, Monza, Italy.
• ²⁴Department of Surgery, Monza Policlinic, Monza, Italy.
• ²⁵Division of Internal Medicine, Hepatobiliary and Immunoallergic Diseases, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
• ²⁶Gastroenterology Unit, Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy.
• ²⁷Hepato-Biliary-Pancreatic Surgery Division Department of Surgery, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
• ²⁸School of Medicine and Surgery, University of Milano-Bicocca, San Gerardo Hospital, Monza, Italy.

Abstract

Importance: Clear indications on how to select retreatments for recurrent hepatocellular carcinoma (HCC) are still lacking.

Objective: To create a machine learning predictive model of survival after HCC recurrence to allocate patients to their best potential treatment.

Design, setting, and participants: 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 treatment effect modifiers were assessed. The model was then fitted individually to identify the best potential treatment. Analysis took place between January and April 2021.

Exposures: Patients were enrolled if treated by reoperative hepatectomy or thermoablation, chemoembolization, or sorafenib.

Main outcomes and measures: Survival after recurrence was the end point.

Results: A total of 701 patients with recurrent HCC were enrolled (mean [SD] age, 71 [9] years; 151 [21.5%] female). Of those, 293 patients (41.8%) received reoperative hepatectomy or thermoablation, 188 (26.8%) received sorafenib, and 220 (31.4%) received chemoembolization. Treatment, age, cirrhosis, number, size, and lobar localization of the recurrent nodules, extrahepatic spread, and time to recurrence were all treatment effect modifiers and survival after recurrence predictors. The area under the receiver operating characteristic curve of the predictive model was 78.5% (95% CI, 71.7%-85.3%) at 5 years after recurrence. According to the model, 611 patients (87.2%) would have benefited from reoperative hepatectomy or thermoablation, 37 (5.2%) from sorafenib, and 53 (7.6%) from chemoembolization in terms of potential survival after recurrence. Compared with patients for which the best potential treatment was reoperative hepatectomy or thermoablation, sorafenib and chemoembolization would be the best potential treatment for older patients (median [IQR] age, 78.5 [75.2-83.4] years, 77.02 [73.89-80.46] years, and 71.59 [64.76-76.06] years for sorafenib, chemoembolization, and reoperative hepatectomy or thermoablation, respectively), with a lower median (IQR) number of multiple recurrent nodules (1.00 [1.00-2.00] for sorafenib, 1.00 [1.00-2.00] for chemoembolization, and 2.00 [1.00-3.00] for reoperative hepatectomy or thermoablation). Extrahepatic recurrence was observed in 43.2% (n = 16) for sorafenib as the best potential treatment vs 14.6% (n = 89) for reoperative hepatectomy or thermoablation as the best potential treatment and 0% for chemoembolization as the best potential treatment. Those profiles were used to constitute a patient-tailored algorithm for the best potential treatment allocation.

Conclusions and relevance: The herein presented algorithm should help in allocating patients with recurrent HCC to the best potential treatment according to their specific characteristics in a treatment hierarchy fashion.