Machine learning for clinical decision support in intrahepatic cholangiocarcinoma based on a population study of the US SEER database and a Chinese single-center registry

Abstract

Background and aims To date, there is still a lack of consensus on the treatment of intrahepatic cholangiocarcinoma (iCCA). This study aims to build a clinical decision support tool based on machine learning of the Surveillance, Epidemiology, and End Results (SEER) database and the Fifth Medical Center of PLA General Hospital in China. Methods A total of 4,398 eligible patients with pathology-proven iCCA from the SEER database and 504 from the hospital data were enrolled for modeling by cross-validation based on the method of machine learning. All models were trained by the open-source Python library 4scikit-survival version 0.16.0 and explained by SHapley Additive exPlanations. Permutation importance was calculated using the library ELI5. Results All of the involved treatment modalities can contribute to a better prognosis. Three models were derived and tested among different data sources with the concordance index in the test datasets of 0.67, 0.69, and 0.73 respectively. The prediction was also consistent with the actual situation in randomly selected real patients. Model 2 trained by the hospital data was selected to develop an online tool because of the advantage of predicting the short-term prognosis.Conclusion The prediction model and tool of this study can be applied to predicting patients’ prognosis after treatment by inputting the patient’s clinical parameters or TNM stages and the treatment option and thus contribute to the optimal clinical decision.