Pre-treatment prediction of microsatellite instability in colon cancer - a nomogram model combining clinicopathological features and pre-treatment CT-based radiomics

Q: Why is pre-treatment prediction of MSI status important in colon cancer?

MSI status is crucial for selecting treatment strategies, especially in advanced stages. Accurate pre-treatment identification supports appropriate clinical decision-making.

Q: How were patients grouped for modeling in this study?

A total of 201 colon cancer patients were categorized into the dMMR/MSI group or the pMMR/MSS group based on surgical pathology results.

Q: What imaging and clinical inputs were combined in the nomogram?

The nomogram integrated clinicopathological indicators and pre-treatment CT-based radiomics features to predict dMMR/MSI status.

Sep 22, 2026

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Background Determining microsatellite instability (MSI) status in colon cancer is crucial for selecting treatment strategies in advanced stages, making accurate pre-treatment identification essential. Objective Evaluate a nomogram integrating clinicopathological indicators and pre-treatment CT-based radiomics features to predict dMMR/MSI status in colon cancer. Methods 201 patients with preoperative contrast-enhanced CT scans were grouped by surgical pathology; clinical predictors used multivariate logistic regression, and radiomics dimensionality reduction used LASSO, forming combined logistic models. Results Age, pericentric lymph node metastasis, and CA72-4 were significant; four radiomic features were selected, and AUC improved from clinical/Rad to combined model with good calibration. Conclusion The combined nomogram shows higher predictive accuracy for dMMR/MSI than standalone models.

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CategoryHealthcare

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Wei et al. BMC Medical Imaging (2026) 26:309 [https://doi.org/10.1186/s12880-026-02394-0](https://doi.org/10.1186/s12880-026-02394-0) BMC Medical Imaging RESEARCH Open Access Pre-treatment prediction of microsatellite instability in colon cancer: a nomogram model combining clinicopathological features and pre-treatment CT-based radiomics Meng Wei1, Congzhen Jia1, Ying Zhang1, Peng You1 and Weizhi Chen1* Abstract Background Determining microsatellite instability (MSI) status in colon cancer is crucial for selecting treatment strategies in advanced stages. Thus, accurately identifying MSI status before treatment is essential. Objective This study aims to evaluate the utility of nomogram model that integrates clinicopathological indicatorsand pre-treatment CT-based radiomics features for predicting DNA mismatch repair deficiency (dMMR) /microsatellite instability (MSI) status in colon cancer prior to treatment. Methods A total of 201 colon cancer patients who had undergone preoperative contrast-enhanced CT scans were categorized into the dMMR/MSI group or the proficient Mismatch Repair (pMMR)/Microsatellite Stable (MSS) group based on surgical pathology results. Multivariate logistic regression was applied to identify independent clinical predictors. The least absolute shrinkage and selection operator (LASSO) regression was applied for dimensionality reduction of radiomics features. Clinical, radiomics, and nomogram models were established through logistic regression analysis based on the risk clinicopathological predictors and radiomics features. Results Multivariate logistic regression identified patient age, pericentric lymph node metastasis, and CA72-4 levels as significant (P < 0. 05) . Four radiomic features were selected to construct the radiomics model. In the training set, the AUC values for the clinical model, Rad score, and combined model were 0 . 86, 0 . 89, and 0. 94, respectively, and in the validation set, 0 . 81, 0 . 89, and 0 . 91, respectively. The Delong test showed the nomogram model outperformed both the clinical model and Rad score (P < 0. 05) . The calibration curve confirmed good consistency between predicted and actual outcomes for dMMR/MSI colon cancer using the combined model. Conclusion The nomogram model, which combines clinicopathological features with pre-treatment CT-based radiomics features, demonstrates greater predictive accuracy for dMMR/MSI colon cancer than the standalone clinical and radiomics models. Keywords Colon neoplasms, Radiomics, Microsatellite instability, Surgery *Correspondence: Weizhi Chen[cghcwz@163.com](cghcwz@163.com) 1Department of Radiology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121000, China © The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/licenses/by-nc-nd/4.0/](vecommons.org/licenses/by-nc-nd/4.0/.)[.](vecommons.org/licenses/by-nc-nd/4.0/.) Wei et al. BMC Medical Imaging (2026) 26:309 Introduction Colorectal cancer (CRC) is a common c

FAQs

Why is pre-treatment prediction of MSI status important in colon cancer?

MSI status is crucial for selecting treatment strategies, especially in advanced stages. Accurate pre-treatment identification supports appropriate clinical decision-making.

How were patients grouped for modeling in this study?

A total of 201 colon cancer patients were categorized into the dMMR/MSI group or the pMMR/MSS group based on surgical pathology results.

What imaging and clinical inputs were combined in the nomogram?

The nomogram integrated clinicopathological indicators and pre-treatment CT-based radiomics features to predict dMMR/MSI status.

Sep 22, 2026

Upload by Jasmine