Artificial intelligence-based model for the recurrence of hepatocellular carcinoma after liver transplantation.

Autor: Altaf A; King Edward Medical University, Lahore, Pakistan; Department of HPB and Liver Transplant Surgery, Shifa International Hospital, Islamabad, Pakistan. Electronic address: https://twitter.com/abdullahaltaf97., Mustafa A; Department of Robotics and Artificial Intelligence, National University of Science and Technology, Islamabad, Pakistan., Dar A; Department of HPB and Liver Transplant Surgery, Shifa International Hospital, Islamabad, Pakistan., Nazer R; Department of Radiology, Shifa International Hospital, Islamabad, Pakistan., Riyaz S; Department of Gastroenterology and Hepatology, Shifa International Hospital, Islamabad, Pakistan. Electronic address: https://twitter.com/shahzadriyaz., Rana A; Department of Radiology, Shifa International Hospital, Islamabad, Pakistan. Electronic address: https://twitter.com/atifranaIR., Bhatti ABH; Department of HPB and Liver Transplant Surgery, Shifa International Hospital, Islamabad, Pakistan; Department of Surgery, Shifa Tameer-e-Millat University, Islamabad, Pakistan. Electronic address: Abubakar.hafeez@yahoo.com.
Jazyk: angličtina
Zdroj: Surgery [Surgery] 2024 Nov; Vol. 176 (5), pp. 1500-1506. Date of Electronic Publication: 2024 Aug 23.
DOI: 10.1016/j.surg.2024.07.039
Abstrakt: Background: Artificial intelligence-based models might improve patient selection for liver transplantation in hepatocellular carcinoma. The objective of the current study was to develop artificial intelligence-based deep learning models and determine the risk of recurrence after living donor liver transplantation for hepatocellular carcinoma.
Methods: The study was a single-center retrospective cohort study. Patients who underwent living donor liver transplantation for hepatocellular carcinoma were divided into training and validation cohorts (n = 192). The deep learning models were used to stratify patients in the training cohort into low- and high-risk groups, and 5-year recurrence-free survival was assessed in the validation cohort.
Results: The median follow-up period was 59.1 (33.9-72.4) months. The artificial intelligence model (pretransplant factors) had an area under the curve of 0.86 in the training cohort and 0.71 in the validation cohort. The largest tumor diameter and alpha-fetoprotein level had the greatest Shapley Additive exPlanations values for recurrence (>0.4). The 5-year recurrence-free survival rates in the low- and high-risk groups were 92.6% and 45% (P < .001). In the second artificial intelligence model (pretransplant factors + grade), the area under the curve for the validation cohort was 0.77, with 5-year recurrence-free survival rates of 96% and 30% in the low- and high-risk groups (P < .001). None of the low-risk patients outside the Milan and University of California San Francisco Criteria had recurrence during follow-up.
Conclusions: The artificial intelligence-based hepatocellular carcinoma transplant recurrence models might improve patient selection for liver transplantation.
(Copyright © 2024 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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