Whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma - Tao Chen; Jialiang Wen; Xinchen Shen; Jiaqi Shen; Jiajun Deng; Mengmeng Zhao; Long Xu; Chunyan Wu; Bentong Yu; Minglei Yang; Minjie Ma; Junqi Wu; et al. (npj Digital Medicine)
Abstract 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.