Collaborative assessment of the risk of postoperative progression in early-stage non-small cell lung cancer: a robust federated learning model

作者：Yu Liu, Xiaobei Duan, Xiaojuan Chen, Xiaojuan Chen, Kunwei Li, Qiong Li, Ke Liu, Wansheng Long, Huan Lin, Bao Feng, Xiangmeng Chen, Xiangmeng Chen · 发表于：Cancer Imaging · 年份：2025 · DOI：10.1186/s40644-025-00911-y · 被引用次数：5 · 研究领域：Lung Cancer Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、AI in cancer detection

BACKGROUND: While the TNM staging system provides valuable insights into the extent of disease, predicting postoperative progression in early-stage non-small cell lung cancer (NSCLC) remains a significant challenge. An effective bioimaging prognostic marker for early-stage NSCLC, powered by artificial intelligence, could greatly assist clinicians in making informed treatment decisions. METHODS: A total of 926 patients from four centers (A, B, C, and D) with histologically confirmed stage I or II solid non-small cell lung cancer (NSCLC) who underwent surgical resection were retrospectively reviewed. In this study, we propose a robust federated learning model (RFed) designed to predict the risk of postoperative progression in early-stage NSCLC patients. The diagnostic efficiency of the RFed model was evaluated using the area under the curve (AUC) and Decision Curve Analysis (DCA). Additionally, the model's performance was further validated through Kaplan-Meier survival analysis, with statistical significance assessed using the log-rank test. Finally, the robustness, generalizability, and interpretability of the RFed model were comprehensively evaluated to confirm its clinical applicability. RESULTS: Experimental results demonstrated the superior performance of the RFed model. Specifically, RFed achieved AUC values of 0.936, 0.861, 0.925, and 0.970 on the test sets from the four centers. DCA further revealed that RFed provided a greater net benefit compared to the clinical model...