Development and validation of a gradient boosting machine to predict prognosis after liver resection for intrahepatic cholangiocarcinoma

作者：Gu‐Wei Ji, Chenyu Jiao, Zhenggang Xu, Xiangcheng Li, Xiuli Wang, Xuehao Wang · 发表于：BMC Cancer · 年份：2022 · DOI：10.1186/s12885-022-09352-3 · 被引用次数：47 · 研究领域：Cholangiocarcinoma and Gallbladder Cancer Studies、Gallbladder and Bile Duct Disorders、Pancreatic and Hepatic Oncology Research

BACKGROUND: Accurate prognosis assessment is essential for surgically resected intrahepatic cholangiocarcinoma (ICC) while published prognostic tools are limited by modest performance. We therefore aimed to establish a novel model to predict survival in resected ICC based on readily-available clinical parameters using machine learning technique. METHODS: A gradient boosting machine (GBM) was trained and validated to predict the likelihood of cancer-specific survival (CSS) on data from a Chinese hospital-based database using nested cross-validation, and then tested on the Surveillance, Epidemiology, and End Results (SEER) database. The performance of GBM model was compared with that of proposed prognostic score and staging system. RESULTS: A total of 1050 ICC patients (401 from China and 649 from SEER) treated with resection were included. Seven covariates were identified and entered into the GBM model: age, tumor size, tumor number, vascular invasion, number of regional lymph node metastasis, histological grade, and type of surgery. The GBM model predicted CSS with C-Statistics ≥ 0.72 and outperformed proposed prognostic score or system across study cohorts, even in sub-cohort with missing data. Calibration plots of predicted probabilities against observed survival rates indicated excellent concordance. Decision curve analysis demonstrated that the model had high clinical utility. The GBM model was able to stratify 5-year CSS ranging from over 54% in low-risk subset to 0% in ...