SynthesisANZ journal of surgery2026
Artificial Intelligence Models Using Magnetic Resonance Imaging to Predict Response to Chemoradiotherapy in Rectal Cancer: A Systematic Review.
Synthesis in ANZ journal of surgery, 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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Abstract
backgroundPathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) is a key prognostic marker with implications for response-adapted management. Although magnetic resonance imaging (MRI) is central to response assessment, differentiating residual tumour from treatment-related changes remains challenging. Artificial intelligence (AI) and machine learning (ML) models applied to MRI show promise in predicting pCR; however, variability in methodology and performance limits clinical translation.
methodsA search of Embase, Medline, Cochrane and Web of Science was conducted in April 2025 in accordance with Preferred Reporting Items for Reviews and Meta-Analysis (PRISMA) guidelines. Eligible studies used MRI-only AI or ML models to predict pCR following chemoradiotherapy in adults with rectal cancer. Screening, full-text review and data extraction were performed independently by two reviewers. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2).
resultsTwenty-two studies comprising 94 predictive models were included. Most studies were retrospective, used T2-weighted MRI and demonstrated variability in MRI protocols, modelling methods and validation strategies. Only five studies conducted external validation. Median AUC was 0.801, with performance ranging from poor to excellent (AUC 0.49-0.997).
conclusionMRI-based AI models demonstrate moderate discriminative performance for predicting pCR following neoadjuvant therapy in LARC. However, methodological heterogeneity, inconsistent reporting and limited external validation currently hinder generalisability. Greater methodological standardisation and multicentre external validation are required before clinical implementation.
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