SynthesisJournal of medical Internet research2026
Radiomics-Based AI for Predicting Neoadjuvant Immunochemotherapy Pathological Response in Non-Small Cell Lung Cancer: Systematic Review and Meta-Analysis.
Synthesis in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
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<.001). For MPR, AI demonstrated a sensitivity of 0.80 (95% CI 0.72-0.87), specificity of 0.83 (95% CI 0.73-0.90), and AUC of 0.88, also superior to traditional models (AUC: 0.88 vs 0.65, P<.001). Subgroup analysis revealed that positron emission tomography/CT-based models offered higher specificity for MPR than CT-based models (0.95 vs 0.80, P=.005). Conclusions: Radiomics-based AI demonstrates high diagnostic accuracy and superior sensitivity compared to traditional radiological criteria, showing significant potential for preoperative response assessment. However, the heterogeneity and retrospective design of current studies limit the evidence. Future large-scale, prospective, multicenter trials and multimodal data integration are required to validate these findings for clinical translation.
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