Lu Yuxing, Liu Fei, Yu Yunfang, Chen Bojiang, Yu Wenyao, Zou Zixing, Li Kun, Man Miao, Ou Caiwen, Wang Chengdi, Zhang Kang, Wang Jinzhuo, Huang Xiaoying
Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing 100871, China; State Key Laboratory of Eye Health, Eye Hospital and Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou, China.
State Key Laboratory of Eye Health, Eye Hospital and Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou, China; National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Institute for AI in Medicine and Faculty of Medicine, Macau University of Technology, Macau, China.
Cell Rep Med. 2025 Jul 15;6(7):102216. doi: 10.1016/j.xcrm.2025.102216. Epub 2025 Jul 2.
Lung cancer remains the leading cause of cancer-related mortality worldwide. The need for cost-effective, non-invasive methods to detect specific gene mutations for targeted therapy and predict patient survival outcomes underscores the importance of advancing diagnostic and prognostic capabilities. Contemporary lung cancer diagnostic models often fail to integrate diverse patient data, leading to incomplete clinical assessments. To address these challenges, we propose LUCID, a multimodal data integration framework designed to predict epidermal growth factor receptor (EGFR) mutation status and survival outcomes in patients with lung cancer. Tailored for early-stage clinical assessment, LUCID leverages lung computed tomography (CT) images, chief complaints, laboratory test results, and demographic data to deliver comprehensive, non-invasive predictions. LUCID achieved strong performance in a retrospective cohort of 5,175 patients, with areas under the receiver operating characteristic curve (AUCs) ranging from 0.851 to 0.881 for EGFR mutation prediction and from 0.821 to 0.912 for survival time prediction. The model also demonstrated robustness across external validation cohorts and in scenarios with missing modalities.
肺癌仍然是全球癌症相关死亡的主要原因。对于具有成本效益的非侵入性方法来检测特定基因突变以进行靶向治疗并预测患者生存结果的需求，凸显了提高诊断和预后能力的重要性。当代肺癌诊断模型往往未能整合多样的患者数据，导致临床评估不完整。为应对这些挑战，我们提出了LUCID，这是一个多模态数据整合框架，旨在预测肺癌患者的表皮生长因子受体（EGFR）突变状态和生存结果。针对早期临床评估量身定制，LUCID利用肺部计算机断层扫描（CT）图像、主要症状、实验室检查结果和人口统计学数据来提供全面的非侵入性预测。LUCID在一个5175名患者的回顾性队列中表现出色，对于EGFR突变预测，受试者操作特征曲线（AUC）下面积范围为0.851至0.881，对于生存时间预测，AUC范围为0.821至0.912。该模型在外部验证队列以及存在缺失模态的情况下也表现出稳健性。
