King Edward Medical University, Lahore, Pakistan; Department of HPB and Liver Transplant Surgery, Shifa International Hospital, Islamabad, Pakistan. Electronic address: https://twitter.com/abdullahaltaf97.
Department of Robotics and Artificial Intelligence, National University of Science and Technology, Islamabad, Pakistan.
Surgery. 2024 Nov;176(5):1500-1506. doi: 10.1016/j.surg.2024.07.039. Epub 2024 Aug 23.
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.
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.
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.
The artificial intelligence-based hepatocellular carcinoma transplant recurrence models might improve patient selection for liver transplantation.
基于人工智能的模型可能会改善肝细胞癌肝移植患者的选择。本研究的目的是开发基于人工智能的深度学习模型，并确定肝细胞癌活体供肝移植后复发的风险。
这是一项单中心回顾性队列研究。将接受活体供肝移植治疗肝细胞癌的患者分为训练和验证队列（n=192）。使用深度学习模型将训练队列中的患者分为低危和高危组，并在验证队列中评估 5 年无复发生存率。
中位随访时间为 59.1（33.9-72.4）个月。人工智能模型（移植前因素）在训练队列中的曲线下面积为 0.86，在验证队列中的曲线下面积为 0.71。最大肿瘤直径和甲胎蛋白水平对复发的 Shapley Additive exPlanations 值最大（>0.4）。低危和高危组的 5 年无复发生存率分别为 92.6%和 45%（P<.001）。在第二个人工智能模型（移植前因素+分级）中，验证队列的曲线下面积为 0.77，低危和高危组的 5 年无复发生存率分别为 96%和 30%（P<.001）。在随访期间，不符合米兰和加利福尼亚大学旧金山分校标准的低危患者均未复发。
基于人工智能的肝细胞癌移植复发模型可能会改善肝移植患者的选择。
