Commentary: Machine learning developed an intratumor heterogeneity signature for predicting prognosis and immunotherapy benefits in cholangiocarcinoma

作者：Liusheng Wu, Xiaoqiang Li, Jun Yan · 发表于：Translational Oncology · 年份：2024 · DOI：10.1016/j.tranon.2024.101995 · 被引用次数：40 · 研究领域：Cholangiocarcinoma and Gallbladder Cancer Studies、Pancreatic and Hepatic Oncology Research、Cancer Genomics and Diagnostics

Machine learning has made great progress in the field of medicine, especially in oncology research showing significant potential. In this paper, the application of machine learning in the study of cholangiocarcinoma was discussed. By developing a novel intra-tumor heterogeneity feature, the study successfully achieved accurate prediction of prognosis and immunotherapy effect in patients with cholangiocarcinoma. This study not only provides strong support for personalized treatment, but also provides key information for clinicians to develop more effective treatment strategies. This breakthrough marks the continuous evolution of machine learning in cancer research and brings new hope for the future development of the medical field. Our study lays a solid foundation for deepening the understanding of the biological characteristics of cholangiocarcinoma and improving the therapeutic effect, and provides a useful reference for more extensive cancer research.