Liu Yu, Duan Xiaobei, Chen Xiaojuan, Li Kunwei, Li Qiong, Liu Ke, Long Wansheng, Lin Huan, Feng Bao, Chen Xiangmeng
Chongqing Big Data Collaborative Innovation Center, Chongqing, China.
Laboratory of Intelligent Detection and Information Processing, Guilin University of Aerospace Technology, Guilin, China.
Cancer Imaging. 2025 Jul 18;25(1):92. doi: 10.1186/s40644-025-00911-y.
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.
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.
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 across a threshold probability range of 0.02 to 0.99. Moreover, Kaplan-Meier curves showed improved discrimination between high-risk and low-risk groups when compared to other models, highlighting its enhanced predictive capability.
The RFed model demonstrates significant effectiveness in predicting the risk of postoperative progression in early-stage NSCLC patients. Its clinical application value lies in its potential to enhance stratified management and support the development of precise treatment strategies for this patient population.
虽然TNM分期系统为疾病范围提供了有价值的见解，但预测早期非小细胞肺癌（NSCLC）术后进展仍然是一项重大挑战。一种由人工智能驱动的、有效的早期NSCLC生物成像预后标志物，能够极大地帮助临床医生做出明智的治疗决策。
回顾性分析了来自四个中心（A、B、C和D）的926例经组织学证实为I期或II期实性非小细胞肺癌（NSCLC）且接受手术切除的患者。在本研究中，我们提出了一种强大的联邦学习模型（RFed），旨在预测早期NSCLC患者术后进展的风险。使用曲线下面积（AUC）和决策曲线分析（DCA）评估RFed模型的诊断效率。此外，通过Kaplan-Meier生存分析进一步验证模型的性能，使用对数秩检验评估统计学意义。最后，全面评估RFed模型的稳健性、泛化性和可解释性，以确认其临床适用性。
实验结果证明了RFed模型的卓越性能。具体而言，RFed在四个中心的测试集上分别取得了0.936、0.861、0.925和0.970的AUC值。DCA进一步显示，在0.02至0.99的阈值概率范围内，与临床模型相比，RFed提供了更大的净效益。此外，与其他模型相比，Kaplan-Meier曲线显示高危组和低危组之间的区分得到改善，突出了其增强的预测能力。
RFed模型在预测早期NSCLC患者术后进展风险方面显示出显著有效性。其临床应用价值在于有可能加强分层管理，并支持为该患者群体制定精确的治疗策略。
