Abstract
BACKGROUND: Neoadjuvant chemo-immunotherapy has shown promise in improving survival outcomes for non-small cell lung cancer (NSCLC) patients, with pathologic response serving as a critical predictor of long-term outcomes. However, manual assessment of pathologic response is labor-intensive and subject to inter-observer variability. This study aimed to develop an automated AI-based solution to address these limitations. METHODS: We developed an AI-powered patch-based image analysis model to quantify residual viable tumor (RVT) in hematoxylin and eosin (H&E)-stained whole slide images. The model was evaluated on resected specimens from 47 NSCLC patients treated with neoadjuvant chemo-immunotherapy. The AI-derived estimates of RVT were compared with visual assessments by a board-certified pathologist. Statistical analysis included Pearson’s correlation for continuous tumor estimation and Cohen’s Kappa for concordance in major pathologic response (MPR) and pathologic complete response (pCR) classification. RESULTS: The AI model demonstrated a strong correlation with the pathologist’s continuous estimation of RVT (r = 0.77, p < 0.001, confidence interval [CI]: 0.73–0.81). In the assessment of clinical endpoints, the model achieved an 89.36% concordance rate for MPR (Kappa = 0.79, p < 0.001, CI: 0.61–0.96) and 89.36% concordance rate for pCR (Kappa = 0.56, p < 0.001, CI: 0.24–0.89) when compared with the board-certified pathologist. CONCLUSIONS: Our AI-powered model demonstrates potential as a decision-support tool for pathologic response assessments in NSCLC patients treated with neoadjuvant chemo-immunotherapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-026-15885-8.