Impact of an artificial intelligence based model to predict non-transplantable recurrence among patients with hepatocellular carcinoma.

Autor:	Altaf A; Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA., Endo Y; Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA., Munir MM; Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA., Khan MMM; Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA., Rashid Z; Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA., Khalil M; Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA., Guglielmi A; Department of Surgery, University of Verona, Verona, Italy., Ratti F; Department of Surgery, San Raffaele Hospital, Milan, Italy., Marques H; Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal., Cauchy F; Department of Hepatobiliopancreatic Surgery, APHP, Beaujon Hospital, Clichy, France., Lam V; Department of Surgery, Westmead Hospital, Sydney, NSW, Australia., Poultsides G; Department of Surgery, Stanford University, Stanford, CA, United States., Kitago M; Department of Surgery, Keio University, Tokyo, Japan., Popescu I; Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania., Martel G; Department of Surgery, University of Ottawa, Ottawa, ON, Canada., Gleisner A; Department of Surgery, University of Colorado, Aurora, CO, United States., Hugh T; Department of Surgery, School of Medicine, The University of Sydney, Sydney, NSW, Australia., Shen F; Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China., Endo I; Department of Surgery, Yokohama City University School of Medicine, Yokohama, Japan., Pawlik TM; Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA. Electronic address: Tim.Pawlik@osumc.edu.
Jazyk:	angličtina
Zdroj:	HPB : the official journal of the International Hepato Pancreato Biliary Association [HPB (Oxford)] 2024 Aug; Vol. 26 (8), pp. 1040-1050. Date of Electronic Publication: 2024 May 16.
DOI:	10.1016/j.hpb.2024.05.006
Abstrakt:	Objective: We sought to develop Artificial Intelligence (AI) based models to predict non-transplantable recurrence (NTR) of hepatocellular carcinoma (HCC) following hepatic resection (HR). Methods: HCC patients who underwent HR between 2000-2020 were identified from a multi-institutional database. NTR was defined as recurrence beyond Milan Criteria. Different machine learning (ML) and deep learning (DL) techniques were used to develop and validate two prediction models for NTR, one using only preoperative factors and a second using both preoperative and postoperative factors. Results: Overall, 1763 HCC patients were included. Among 877 patients with recurrence, 364 (41.5%) patients developed NTR. An ensemble AI model demonstrated the highest area under ROC curves (AUC) of 0.751 (95% CI: 0.719-0.782) and 0.717 (95% CI:0.653-0.782) in the training and testing cohorts, respectively which improved to 0.858 (95% CI: 0.835-0.884) and 0.764 (95% CI: 0.704-0.826), respectively after incorporation of postoperative pathologic factors. Radiologic tumor burden score and pathological microvascular invasion were the most important preoperative and postoperative factors, respectively to predict NTR. Patients predicted to develop NTR had overall 1- and 5-year survival of 75.6% and 28.2%, versus 93.4% and 55.9%, respectively, among patients predicted to not develop NTR (p < 0.0001). Conclusion: The AI preoperative model may help inform decision of HR versus LT for HCC, while the combined AI model can frame individualized postoperative care (https://altaf-pawlik-hcc-ntr-calculator.streamlit.app/). Competing Interests: Conflicts of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.)
Databáze:	MEDLINE
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