| Summary: | Gu-Wei Ji,1– 3,* Ye Fan,1– 3,* Dong-Wei Sun,1– 3,* Ming-Yu Wu,4 Ke Wang,1– 3 Xiang-Cheng Li,1– 3 Xue-Hao Wang1– 3 1Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 2Key Laboratory of Liver Transplantation, Chinese Academy of Medical Sciences, Nanjing, People’s Republic of China; 3NHC Key Laboratory of Living Donor Liver Transplantation (Nanjing Medical University), Nanjing, People’s Republic of China; 4Department of Hepatobiliary Surgery, Wuxi People’s Hospital, Wuxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xue-Hao Wang; Ke WangHepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, People’s Republic of ChinaTel +86 13305178713; +86 18061675088Fax +86 25 68136450; +86 25 68136450Email wangxh@njmu.edu.cn; lancetwk@163.comBackground: Improved prognostic prediction is needed to stratify patients with early hepatocellular carcinoma (EHCC) to refine selection of adjuvant therapy. We aimed to develop a machine learning (ML)-based model to predict survival after liver resection for EHCC based on readily available clinical data.Methods: We analyzed data of surgically resected EHCC (tumor≤ 5 cm without evidence of extrahepatic disease or major vascular invasion) patients from the Surveillance, Epidemiology, and End Results (SEER) Program to train and internally validate a gradient-boosting ML model to predict disease‐specific survival (DSS). We externally tested the ML model using data from 2 Chinese institutions. Patients treated with resection were matched by propensity score to those treated with transplantation in the SEER-Medicare database.Results: A total of 2778 EHCC patients treated with resection were enrolled, divided into 1899 for training/validation (SEER) and 879 for test (Chinese). The ML model consisted of 8 covariates (age, race, alpha-fetoprotein, tumor size, multifocality, vascular invasion, histological grade and fibrosis score) and predicted DSS with C-Statistics > 0.72, better than proposed staging systems across study cohorts. The ML model could stratify 10-year DSS ranging from 70% in low-risk subset to 5% in high-risk subset. Compared with low-risk subset, no remarkable survival benefits were observed in EHCC patients receiving transplantation before and after propensity score matching.Conclusion: An ML model trained on a large-scale dataset has good predictive performance at individual scale. Such a model is readily integrated into clinical practice and will be valuable in discussing treatment strategies.Keywords: liver cancer, artificial intelligence, prognosis, modelling, surgery
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