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Background This study aimed to evaluate the diagnostic performance of magnetic resonance imaging (MRI)-based radiomics for predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients with locally advanced rectal adenocarcinoma. Methods Eligible studies developed MRI-based radiomics or deep learning models to predict pCR and reported sufficient data to reconstruct 2 × 2 contingency tables. Only validation cohorts were included in the quantitative synthesis. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 and the Radiomics Quality Score. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio were estimated using a bivariate random-effects model. Hierarchical summary receiver operating characteristic (HSROC) analysis was performed. Results Thirty-eight studies were included. The pooled sensitivity and specificity were 0.82 (95% CI, 0.71-0.90) and 0.86 (95% CI, 0.80-0.91), respectively. The pooled PLR and NLR were 6.0 (95% CI, 4.0-8.9) and 0.21 (95% CI, 0.12-0.35), corresponding to a diagnostic odds ratio of 29 (95% CI, 14-61). HSROC analysis showed an area under the curve of 0.846. Subgroup analyses suggested improved performance for deep learning and combined clinical–radiomic models. Conclusion MRI-based radiomics demonstrates good diagnostic accuracy for predicting pCR after neoadjuvant chemoradiotherapy in rectal cancer, although methodological heterogeneity and limited prospective validation remain challenges.
Jakipov et al. (2026) studied this question.