ORIGINAL RESEARCH article
Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1672636
This article is part of the Research TopicMedical Knowledge-Assisted Machine Learning Technologies in Individualized Medicine Volume IIView all 24 articles
Machine Learning-Based Radiomics Approach Assessing Preoperative Non-Contrast CT for Microsatellite Instability Prediction in Colon Cancer
Provisionally accepted
- 1Kunming University of Science and Technology, Kunming, China
- 2The Third Affiliated Hospital of Kunming Medical University, Kunming, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Objectives: To assess the feasibility of non-contrast CT-based radiomics model for predicting microsatellite instability (MSI) status in colon cancer. Methods: Leveraging non-contrast abdominal CT imaging data from 57 retrospectively enrolled patients with balanced class distribution (training cohort: n=38, 19 non-MSI-H and 19 MSI-H; test cohort: n=19, 9 non-MSI-H and 10 MSI-H), we implemented a voxel volume-based tumor feature selection method. Feature selection integrated four feature selection filters—correlation analysis, univariate logistic regression, least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE). We comparatively evaluated multiple classifiers using cross-validation combined with accuracy for choosing the best classifier. Results: A multilayer perceptron-based classification model was developed, achieving average multifold accuracy of 0.871 in cross-validation on the training cohort. In the test cohort, the model achieved an AUC of 0.944 (95% CI 0.841–1.000) with accuracy of 0.842, while maintaining sensitivity of 0.889 and specificity of 0.800, demonstrating excellent and comparable performance to previous contrast-enhanced CT-based radiomics models. This is a provisional file, not the final typeset article Conclusion: 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.
Keywords: Colon Cancer, Microsatellite Instability, Non-contrast CT, Radiomics, machine learning
Received: 24 Jul 2025; Accepted: 15 Sep 2025.
Copyright: © 2025 Ren, Wang, Chen, He and Shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jianfeng He, jfenghe@kust.edu.cn
Tao Shen, shentao@kmmu.edu.cn
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.