Preoperative prediction of intrahepatic cholangiocarcinoma lymph node metastasis by means of machine learning: a multicenter study in China

作者：Tingfeng Huang, Hongzhi Liu, Zhaowang Lin, Jie Kong, Kongying Lin, Zhipeng Lin, Yifan Chen, Qizhu Lin, Weiping Zhou, Jingdong Li, Jiangtao Li, Yongyi Zeng · 发表于：BMC Cancer · 年份：2022 · DOI：10.1186/s12885-022-10025-4 · 被引用次数：15 · 研究领域：Cholangiocarcinoma and Gallbladder Cancer Studies、Gallbladder and Bile Duct Disorders、Hepatocellular Carcinoma Treatment and Prognosis

BACKGROUND: Hepatectomy is currently the most effective modality for the treatment of intrahepatic cholangiocarcinoma (ICC). The status of the lymph nodes directly affects the choice of surgical method and the formulation of postoperative treatment plans. Therefore, a preoperative judgment of lymph node status is of great significance for patients diagnosed with this condition. Previous prediction models mostly adopted logistic regression modeling, and few relevant studies applied random forests in the prediction of ICC lymph node metastasis (LNM). METHODS: A total of 149 ICC patients who met clinical conditions were enrolled in the training group. Taking into account preoperative clinical data and imaging features, 21 indicators were included for analysis and modeling. Logistic regression was used to filter variables through multivariate analysis, and random forest regression was used to rank the importance of these variables through the use of algorithms. The model's prediction accuracy was assessed by the concordance index (C-index) and calibration curve and validated with external data. RESULT: Multivariate analysis shows that Carcinoembryonic antigen (CEA), Carbohydrate antigen19-9 (CA19-9), and lymphadenopathy on imaging are independent risk factors for lymph node metastasis. The random forest algorithm identifies the top four risk factors as CEA, CA19-9, and lymphadenopathy on imaging and Aspartate Transaminase (AST). The predictive power of random forest is significan...