Radiomics Models as Tools for Predicting Genetic Mutations in Colorectal Cancer: A Systematic Review and Meta-Analysis.

Authors

Yassin Rahnama, Amir Shahbazi, Anita Dadashi, Fatemeh Fathabadi, Faeze Salahshour, Sina Delazar, Mojtaba Sedaghat, Amir Keshvari, Alireza Kazemeini, Mohammad Reza Keramati, Mohammad Sadegh Fazeli, Behnam Behboudi, Seyed Mohsen Ahmadi-Tafti

Published in

Journal of gastrointestinal cancer. Volume 57. Issue 1. Jul 08, 2026. Epub Jul 08, 2026.

Abstract

The evaluation of genetic mutations is crucial for personalized therapy in colorectal cancer (CRC), but the invasive tissue biopsy is subject to sampling bias and other complications. Radiomics has emerged as a non-invasive tool to predict these mutations from standard medical images. In this systematic review and meta-analysis, we aimed to evaluate the diagnostic accuracy and methodological quality of radiomics models for predicting key genetic mutations in CRC.
A comprehensive search of PubMed, Scopus, Web of Science, and Embase was conducted in accordance with PRISMA guidelines. Studies evaluating radiomics models for predicting genetic mutations in CRC patients using pre-operative CT, MRI, or PET/CT were included. A meta-analysis of diagnostic accuracy was performed to calculate the pooled sensitivity, specificity. Methodological quality was assessed using the Radiomics Quality Score (RQS) and QUADAS-2 tools.
Sixteen studies were included in the quantitative analysis. The pooled sensitivity and specificity were 0.75 (95% CI, 0.67-0.81) and 0.78 (95% CI, 0.70-0.85), respectively, with an overall AUC of 0.79. Subgroup analyses revealed that radio-clinical models integrating both clinical and radiomics features achieved superior sensitivity compared to models with only radiological input. However, the overall methodological quality of the included studies was low, with a mean RQS of 45%.
Conventional radiomics models demonstrate promising results for the non-invasive prediction of genetic mutations in CRC, with sensitivity enhanced by the integration of clinical data. Despite this potential, significant methodological shortcomings and heterogeneity across studies highlight the need for standardized protocols and large-scale, prospective validation before these models can be translated into routine clinical practice.

PMID:
42420571
Bibliographic data and abstract were imported from PubMed on 09 Jul 2026.

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