SynthesisWorld journal of surgical oncology2026
Radiomics for predicting microsatellite instability-high status in colorectal cancer: a systematic review and meta-analysis.
Synthesis in World journal of surgical oncology, 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
objectiveMicrosatellite instability (MSI) has emerged as a key predictive biomarker for chemotherapy and immunotherapy response, and as a prognostic indicator in colorectal cancer (CRC). The current clinical standard for MSI detection relies on polymerase chain reaction (PCR) or immunohistochemical analysis of tumor biopsy specimens. CT, PET-CT, and MRI-based radiomics models present a promising non-invasive alternative for this purpose. MATERIALS AND
methodsTo identify studies assessing the diagnostic efficacy of CT, MRI and PET-CT-based radiomics in detecting MSI status in CRC, a systematic search was performed across PubMed, Embase, the Cochrane Library, and Web of Science. The pooled area under the curve (AUC), sensitivity, and specificity were estimated using a random-effects model in Meta-DiSc 1.4, RevMan 5.4, and Stata 15. Data were visualized through forest plots and a summary receiver operating characteristic (SROC) curve. A comprehensive heterogeneity assessment was conducted via I² statistics, sensitivity analyses, threshold effect evaluation, subgroup analyses, and meta-regression.
resultsThis meta-analysis included 34 studies with a total of 7,959 patients. The overall model demonstrated a pooled area under the curve (AUC) of 0.90 (95% CI: 0.87-0.93). The pooled sensitivity and specificity were 0.85 (95% CI: 0.79-0.89) and 0.82 (95% CI: 0.78-0.86), respectively, both marked by substantial heterogeneity (I² = 87% and 92%, respectively; P < 0.01).
conclusionRadiomics holds significant promise as a non-invasive tool for MSI status prediction in CRC. In particular, machine learning and deep learning offer enhanced potential for model performance. These results pave the way for future research to develop and validate more accurate predictive models, thereby improving diagnostic precision, therapeutic decision-making, and prognosis in colorectal cancer.
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