Abstract
Neoadjuvant chemotherapy (NAC) is a widely used therapeutic strategy for patients with resectable non-small cell lung cancer (NSCLC). However, individual responses to NAC vary widely among patients, limiting its effective clinical application. In this study, we propose a weakly supervised deep learning model, DeepDrRVT, which integrates self-supervised feature extraction and attention-based deep multiple instance learning, to improve NAC decision making from pretreatment biopsy images. DeepDrRVT demonstrated superior predictive performance and generalizability, achieving AUCs of 0.954, 0.872 and 0.848 for complete pathologic response, and 0.968, 0.893 and 0.831 for major pathologic response in the training, internal validation and external validation cohorts, respectively. The DeepDrRVT digital assessment of residual viable tumor correlated significantly with the local pathologists' visual assessment (Pearson r = 0.98, 0.80, and 0.59; digital/visual slope = 1.0, 0.8 and 0.55) and was also associated with longer disease-free survival (DFS) in all cohorts (HR = 0.455, 95% CI 0.234-0.887, P = 0.018; HR = 0.347, 95% CI 0.135-0.892, P = 0.021 and HR = 0.446, 95% CI 0.193-1.027, P = 0.051). Furthermore, DeepDrRVT remained an independent prognostic factor for DFS after adjustment for clinicopathologic variables (HR = 0.456, 95% CI 0.227-0.914, P = 0.027; HR = 0.358, 95% CI 0.135-0.949, P = 0.039 and HR = 0.419, 95% CI 0.181-0.974, P = 0.043). Thus, DeepDrRVT holds promise as an accessible and reliable tool for clinicians to make more informed treatment decisions prior to the initiation of NAC.