biostudies-literatureUnknown22(1)Ji GW<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>A total of 1050 ICC patients (40BMC cancer258https://www.ebi.ac.uk/biostudies/studies/S-EPMC8915487biostudies-literatureDevelopment and validation of a gradient boosting machine to predict prognosis after liver resection for intrahepatic cholangiocarcinoma.PMC8915487Li XCJi GWJiao CYWang XHXu ZGWang KfalseDevelopment and validation of a gradient boosting machine to predict prognosis after liver resection for intrahepatic cholangiocarcinoma.<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>A total of 1050 ICC patients (402022-01-01T00:00:00Z2022 Mar2025-04-22T12:57:25.475Z2025-04-06T00:24:22.898ZS-EPMC89154873527713010.1186/s12885-022-09352-3