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Machine Learning to Improve Prognosis Prediction of Early Hepatocellular Carcinoma After Surgical Resection
Journal of Hepatocellular Carcinoma ( IF 4.1 ) Pub Date : 2021-08-10 , DOI: 10.2147/jhc.s320172 Gu-Wei Ji 1, 2, 3 , Ye Fan 1, 2, 3 , Dong-Wei Sun 1, 2, 3 , Ming-Yu Wu 4 , Ke Wang 1, 2, 3 , Xiang-Cheng Li 1, 2, 3 , Xue-Hao Wang 1, 2, 3
Journal of Hepatocellular Carcinoma ( IF 4.1 ) Pub Date : 2021-08-10 , DOI: 10.2147/jhc.s320172 Gu-Wei Ji 1, 2, 3 , Ye Fan 1, 2, 3 , Dong-Wei Sun 1, 2, 3 , Ming-Yu Wu 4 , Ke Wang 1, 2, 3 , Xiang-Cheng Li 1, 2, 3 , Xue-Hao Wang 1, 2, 3
Affiliation
Background: 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.
更新日期：2021-08-10
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.
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