Li Yongmeng, Chai Xiaodong, Yang Moxuan, Xiong Jiahang, Zeng Junyang, Chen Yun, Xu Gang, Lin Haifeng, Wang Wei, Wang Shuhao, Che Nanying
Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Department of Pathology, Beijing Chest Hospital, Capital Medical University, Beijing, China.
NPJ Precis Oncol. 2025 Jun 19;9(1):197. doi: 10.1038/s41698-025-00981-y.
With the rapid progress in artificial intelligence (AI) and digital pathology, prognosis prediction for non-small cell lung cancer (NSCLC) patients has become a critical component of personalized medicine. In this study, we developed a multimodal AI model that integrated whole-slide images and dense clinical data to predict disease-free survival (DFS) and overall survival (OS) with high accuracy for NSCLC patients undergoing surgery. Utilizing data from 618 patients at Beijing Chest Hospital, the model achieved areas under the curve (AUC) of 0.8084 for predicting progression and 0.8021 for predicting death in the test set. Importantly, the model attained balanced accuracies of 0.7047 for predicting progression and 0.6884 for predicting death. By categorizing patients into high-risk and low-risk groups, the model identified significant differences in survival outcomes, with hazard ratios of 4.85 for progression and 4.57 for death, both with p values below 0.0001. Additionally, it uncovered novel digital biomarkers associated with poor prognosis, offering further insights into NSCLC treatment. This model has the potential to revolutionize postoperative decision-making by providing clinicians with a precise tool for predicting DFS and OS, thereby improving patient outcomes.
随着人工智能（AI）和数字病理学的快速发展，非小细胞肺癌（NSCLC）患者的预后预测已成为精准医疗的关键组成部分。在本研究中，我们开发了一种多模态AI模型，该模型整合了全切片图像和密集的临床数据，以高精度预测接受手术的NSCLC患者的无病生存期（DFS）和总生存期（OS）。利用北京胸科医院618例患者的数据，该模型在测试集中预测疾病进展的曲线下面积（AUC）为0.8084，预测死亡的AUC为0.8021。重要的是，该模型预测疾病进展的平衡准确率为0.7047，预测死亡的平衡准确率为0.6884。通过将患者分为高风险和低风险组，该模型发现了生存结果的显著差异，疾病进展的风险比为4.85（P值均低于0.0001），死亡的风险比为4.57。此外，它还发现了与预后不良相关的新型数字生物标志物，为NSCLC治疗提供了进一步的见解。该模型有可能通过为临床医生提供预测DFS和OS的精确工具来彻底改变术后决策，从而改善患者的治疗结果。
