Predicting NSCLC surgical outcomes using deep learning on histopathological images: development and multi-omics validation of Sr-PPS model

Background: Currently, there remains a critical need for reliable tools to accurately predict post-surgical outcomes in non-small cell lung cancer (NSCLC) patients in clinical practice. We aimed to develop and validate a deep learning-based model utilizing histopathological slides to accurately predict post-surgical outcomes in NSCLC patients. Methods: In this study, we analyzed histopathological slides and comprehensive clinical data from 337 Local-NSCLC patients for model development, and further validated the model using an independent cohort of 554 NSCLC patients from The Cancer Genome Atlas (TCGA) database. Utilizing the advanced Res2Net deep learning architecture, we developed and optimized a novel Surgical Prognosis Prediction Score (Sr-PPS) system. Results: The Sr-PPS model demonstrated significantly enhanced predictive accuracy for both disease-free survival (DFS) and overall survival (OS) in NSCLC patients. Multivariate Cox regression analysis validated Sr-PPS as a robust independent predictor of post-surgical outcomes in NSCLC patients. Patients with low Sr-PPS scores exhibited enhanced anti-tumor immune microenvironment characteristics, characterized by significant upregulation of immune activation pathways (particularly T-cell migration and B-cell receptor signaling), coupled with marked downregulation of oncogenic pathways, including insulin-like growth factor receptor signaling and STAT protein phosphorylation. Further genomic analyses revealed significant associations between Sr-PPS scores and mutations in key oncogenic driver genes, including CTNND2, PRRX1, and ALK. Conclusions: Our deep learning-based Sr-PPS model not only demonstrates robust predictive capability for post-surgical outcomes in NSCLC patients but also elucidates underlying molecular mechanisms, thereby providing a valuable framework for personalized treatment stratification.