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

Yassin Rahnama^1,2, Amir Shahbazi¹, Anita Dadashi^1,2

¹Colorectal Research Center, Tehran University of Medical Sciences, Tehran, Iran.

Journal of Gastrointestinal Cancer

|July 8, 2026

Summary

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Radiomics shows promise for predicting colorectal cancer (CRC) genetic mutations non-invasively. Integrating clinical data improves sensitivity, but methodological quality needs standardization for clinical use.

Area of Science:

Oncology
Medical Imaging
Genetics

Background:

Personalized therapy for colorectal cancer (CRC) relies on genetic mutation evaluation.
Invasive biopsies for genetic analysis in CRC are prone to sampling errors.
Radiomics offers a non-invasive approach to predict CRC genetic mutations from medical images.

Purpose of the Study:

To systematically review and meta-analyze the diagnostic accuracy of radiomics models for predicting key genetic mutations in CRC.
To evaluate the methodological quality of existing radiomics studies in CRC.

Main Methods:

A comprehensive literature search was performed across major databases (PubMed, Scopus, Web of Science, Embase) following PRISMA guidelines.
Included studies used pre-operative CT, MRI, or PET/CT for radiomics models predicting CRC genetic mutations.

Keywords:

Artificial intelligence Colorectal cancer Genetic mutations Meta-analysis Radiomics Systematic review

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Diagnostic accuracy was meta-analyzed (sensitivity, specificity), and methodological quality was assessed using RQS and QUADAS-2.

Main Results:

Sixteen studies were included, yielding pooled sensitivity of 0.75 and specificity of 0.78 (AUC 0.79).
Radio-clinical models (combining clinical and radiomics features) demonstrated higher sensitivity than radiomics-only models.
The overall methodological quality was low, with a mean Radiomics Quality Score (RQS) of 45%.

Conclusions:

Radiomics models show potential for non-invasive prediction of CRC genetic mutations.
Clinical data integration enhances the sensitivity of radiomics models.
Standardized protocols and prospective validation are essential for clinical translation due to methodological limitations.