Predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer via artificial intelligence: A systematic review and meta-analysis

BACKGROUNDFor patients with locally advanced rectal cancer (LARC), achieving a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) creates an opportunity for organ preservation through a "watch-and-wait" strategy, thereby avoiding radical surgery.Artificial intelligence (AI) models applied to medical imaging offer a non-invasive way to predict this response, yet the landscape of available predictive tools remains fragmented.Importantly, pCR is a histopathological endpoint that can only be confirmed after surgical resection, whereas clinical.AIM To evaluate the diagnostic performance of image-based AI models in predicting pCR (ypT0N0) for patients with LARC following nCRT.METHODS We searched PubMed, EMBASE, Web of Science, and Cochrane CENTRAL up to May 17, 2026.Studies developing image-based AI or radiomics models predicting pCR were included.Prediction Model Risk of Bias Assessment Tool, Quality Assessment of Diagnostic Accuracy Studies-AI, and Radiomics Quality Score were used to evaluate methodological quality, while Grading of Recommendations Assessment, Development and Evaluation-Diagnostic Test Accuracy assessed certainty of evidence.A bivariate random-effects model was used to pool sensitivity and specificity, and a hierarchical summary receiver operating characteristic (HSROC) curve was used to estimate the pooled area under the curve (AUC).Between-study heterogeneity was quantified using the Zhou and Dendukuri approach.For the primary meta-analysis, we used only data from validation cohorts, as these estimates better reflect generalizable performance.Training-only estimates were excluded to minimize the risk of overestimating model and-wait management.Prospective, multicentre, externally validated studies with standardized reporting are required before any clinical translation.