Published online Nov 8, 2026. doi: 10.35712/aig.125015
Revised: August 14, 2026
Accepted: October 16, 2026
Published online: November 8, 2026
Processing time: 121 Days and 16.1 Hours
For 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 complete response is assessed clinically and used to guide watch-and-wait decisions.
To evaluate the diagnostic performance of image-based AI models in predicting pCR (ypT0N0) for patients with LARC following nCRT.
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 performance. Pooled predictive values were calculated from the pooled sensitivity and specificity at specified prevalence levels and are reported as illustrative, prevalence-dependent estimates rather than directly pooled measures.
We included 13 studies with a total cohort of 2062 patients. The primary meta-analysis used validation cohorts from these studies, comprising 1393 patients. The pooled HSROC AUC was 0.889 (95%CI: 0.85-0.93), with sensitivity of 79.6% (95%CI: 70.6%-86.4%) and specificity of 84.2% (95%CI: 78.6%-88.5%). Between-study heterogeneity was low (I² = 11.3% by the Zhou and Dendukuri approach). Among radiomics-based machine learning (ML) studies (n = 9), the pooled AUC was 0.851 (95%CI: 0.81-0.88). Deep learning (n = 2) and hybrid approaches (n = 2) showed individual AUCs ranging from 0.72 to 0.92; however, the limited number of studies precluded reliable inference. External validation studies (n = 7) achieved a pooled AUC of 0.84 (95%CI: 0.80-0.88), compared with 0.91 (95%CI: 0.87-0.94) for internal/temporal validation studies (n = 6; ΔAUC = 0.07, P = 0.003), indicating significantly higher reported performance among internally validated models. Restricting the analysis to low-risk-of-bias studies (n = 5) yielded a comparable pooled AUC of 0.886 (95%CI: 0.85-0.92), indicating that high-risk studies did not materially inflate the primary estimate. The Egger-type regression test for publication bias was non-significant (P = 0.65).
ML and radiomics show moderate diagnostic accuracy for predicting pCR (pooled HSROC AUC = 0.889), with low between-study heterogeneity and comparable performance across validation types and risk-of-bias strata. However, the pooled positive predictive value (54.4%) remains insufficient for AI to confirm pCR independently. Because AI models predict a pathological endpoint rather than clinical complete response, their outputs cannot be directly equated with eligibility for watch-and-wait management. Prospective, multicentre, externally validated studies with standardized reporting are required before any clinical translation.
Core Tip: Artificial intelligence (AI) and radiomics models demonstrate pooled accuracy of 0.889 with low between-study heterogeneity (I2 = 11.3%). Performance was consistent between low and high risk-of-bias studies, although externally validated models showed significantly lower pooled accuracy (AUCP: 0.84) than internally/temporally validated models (AUC: 0.91; P = 0.003). However, the positive predictive value (54.4%) remains insufficient for artificial intelligence to confirm pathological complete response independently. Prospective validation with standardized reporting is required before implementation. AI should be integrated with, rather than replace, multidisciplinary assessment.