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Updated: Jun 27, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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CT-Based Radiomics to Predict KRAS Mutation in CRC Patients Using a Machine Learning Algorithm: A Retrospective

Jacobo Porto-Álvarez¹, Eva Cernadas², Rebeca Aldaz Martínez¹

¹Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain.

|August 26, 2023

Summary

This summary is machine-generated.

Computer tomography (CT)-based radiomics can predict the KRAS mutation in colorectal cancer (CRC) patients. This non-invasive approach shows promise for managing CRC and potentially diagnosing patients before invasive procedures.

Keywords:

KRAS mutation colorectal cancer radiogenomics radiomics texture analysis

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Area of Science:

Oncology
Radiology
Medical Imaging

Background:

Colorectal cancer (CRC) is a prevalent global malignancy.
The KRAS mutation occurs in 30-50% of CRC cases and predicts resistance to anti-EGFR therapy.
Accurate prediction of KRAS mutation status is crucial for effective CRC treatment strategies.

Purpose of the Study:

To evaluate the efficacy of computer tomography (CT)-based radiomics in predicting KRAS mutations in CRC patients.
To explore the correlation between CT imaging features and KRAS mutation status.
To assess the potential of radiomics as a non-invasive tool for CRC patient management.

Main Methods:

Retrospective study of 56 CRC patients with confirmed KRAS status.
Extraction of radiomics features from contrast-enhanced CT (CECT) scans prior to treatment.
Application of various machine learning classifiers (AdaBoost, neural network, decision tree, SVM, random forest) for prediction.
Analysis of texture descriptors to identify imaging patterns associated with KRAS mutations.

Main Results:

The AdaBoost ensemble using clinical data achieved the highest prediction accuracy (76.8%) and kappa (53.7%), with 73.3% sensitivity and 80.8% specificity.
Texture descriptors yielded an accuracy of 73.2% and kappa of 46%, with 76.7% sensitivity and 69.2% specificity.
A significant correlation was observed between CT texture patterns and KRAS mutation status.

Conclusions:

CT-based radiomics can effectively predict KRAS mutations in colorectal cancer patients.
Radiomics offers a promising non-invasive method for assessing KRAS status, potentially guiding treatment decisions.
This approach may play a future role in the early diagnosis and management of CRC, reducing the need for invasive procedures.