Machine learning-based radiomics approach assessing preoperative non-contrast CT for microsatellite instability prediction in colon cancer

We validated the feasibility of non-contrast CT for MSI prediction in colon cancer with radiomics analysis, highlighting its potential as a flexible and cost-effective preliminary screening tool. This approach, which does not require supplementary medical examination, may enhance clinical decision-making by providing a valuable tool for identifying MSI-H molecular subtypes in colon cancer patients.

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Publication Details

Journal
Frontiers in Physiology
Published
2025-09-29
DOI
https://doi.org/10.3389/fphys.2025.1672636
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Machine learning-based radiomics approach assessing preoperative non-contrast CT for microsatellite instability prediction in colon cancer

Tao Shen, Jianfeng He, Yingjuan Wang, Dongming Ren et al.
Frontiers in Physiology
Radiomics and Machine Learning in Medical Imaging
article

Machine learning-based radiomics approach assessing preoperative non-contrast CT for microsatellite instability prediction in colon cancer

Tao Shen, Jianfeng He, Yingjuan Wang, Dongming Ren, Luda Chen
article en

Abstract

We validated the feasibility of non-contrast CT for MSI prediction in colon cancer with radiomics analysis, highlighting its potential as a flexible and cost-effective preliminary screening tool. This approach, which does not require supplementary medical examination, may enhance clinical decision-making by providing a valuable tool for identifying MSI-H molecular subtypes in colon cancer patients.

Frontiers in PhysiologyVol. 16
Kunming University of Science and Technology (CN), Kunming Medical University (CN)
Openalex Percentile: Top 60%
Radiomics and Machine Learning in Medical Imaging
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