Radiomics-Based AI for Predicting Neoadjuvant Immunochemotherapy Pathological Response in Non–Small Cell Lung Cancer: Systematic Review and Meta-Analysis

Background: Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality worldwide. Accurate early prediction of response to neoadjuvant therapy is critical. Objective: We aimed to evaluate the diagnostic performance of radiomics-based AI in predicting pathological complete response (pCR) and major pathological response (MPR) following neoadjuvant immunochemotherapy in NSCLC and to compare it against traditional radiological criteria. Methods: A systematic search of PubMed, Embase, Cochrane Library, and Web of Science was conducted through October 26, 2025. Studies utilizing computed tomography (CT) or positron emission tomography/CT-based AI models to predict pCR or MPR were included. Methodological quality was appraised using the PROBAST (Prediction Model Risk of Bias Assessment Tool)+AI tool. Sensitivity, specificity, and area under the curve (AUC) were pooled using a bivariate random effects model. Results: Twenty-three studies involving 2004 patients in validation sets were included, with histopathology as the gold standard. For pCR, AI models achieved a pooled sensitivity of 0.77 (95% CI 0.70-0.83), specificity of 0.79 (95% CI 0.73-0.84), and AUC of 0.85, significantly outperforming traditional criteria in sensitivity (0.77 vs 0.42; P