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Authors’ Reply: Clarifying the Comparative Interpretation and Clinical Implications of Radiomics-Based AI for Pathological Response Prediction
This author reply responds to a Letter to the Editor commenting on our systematic review and meta‑analysis evaluating radiomics‑based artificial intelligence for predicting pathological response following neoadjuvant immunochemotherapy in non‑small‑cell lung cancer. We clarify several methodological points raised in the comment, including patient versus assessment counts in a cited study, cross‑study versus within‑patient comparisons of diagnostic metrics, and the sensitivity‑specificity trade‑off between artificial‑intelligence models and conventional response criteria (RECIST 1.1, PERCIST). We acknowledge two textual errors in the original discussion and confirm they do not affect primary pooled analyses. We further elaborate on eligibility constraints, heterogeneity across prediction time points, definitions of pathological complete response, and reporting standards such as DECIDE‑AI. Our core conclusion remains unchanged: radiomics‑based artificial intelligence shows promising predictive performance with a potential sensitivity advantage over RECIST 1.1, though definitive evidence requires prospective same‑patient, same‑time‑point validation studies.
