Chen Tao, Wen Jialiang, Shen Xinchen, Shen Jiaqi, Deng Jiajun, Zhao Mengmeng, Xu Long, Wu Chunyan, Yu Bentong, Yang Minglei, Ma Minjie, Wu Junqi, She Yunlang, Zhong Yifan, Hou Likun, Jin Yanrui, Chen Chang
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China.
NPJ Digit Med. 2025 Jan 29;8(1):69. doi: 10.1038/s41746-025-01470-z.
Existing prognostic models are useful for estimating the prognosis of lung adenocarcinoma patients, but there remains room for improvement. In the current study, we developed a deep learning model based on histopathological images to predict the recurrence risk of lung adenocarcinoma patients. The efficiency of the model was then evaluated in independent multicenter cohorts. The model defined high- and low-risk groups successfully stratified prognosis of the entire cohort. Moreover, multivariable Cox analysis identified the model defined risk groups as an independent predictor for disease-free survival. Importantly, combining TNM stage with the established model helped to distinguish subgroups of patients with high-risk stage II and stage III disease who are highly likely to benefit from adjuvant chemotherapy. Overall, our study highlights the significant value of the constructed model to serve as a complementary biomarker for survival stratification and adjuvant therapy selection for lung adenocarcinoma patients after resection.
现有的预后模型有助于估计肺腺癌患者的预后，但仍有改进空间。在本研究中，我们基于组织病理学图像开发了一种深度学习模型，以预测肺腺癌患者的复发风险。然后在独立的多中心队列中评估该模型的效率。该模型成功地定义了高风险和低风险组，对整个队列的预后进行了分层。此外，多变量Cox分析确定该模型定义的风险组是无病生存的独立预测因子。重要的是，将TNM分期与已建立的模型相结合，有助于区分高危II期和III期疾病的患者亚组，这些患者极有可能从辅助化疗中获益。总体而言，我们的研究突出了所构建模型作为肺腺癌患者切除术后生存分层和辅助治疗选择的补充生物标志物的重要价值。
