ArticleBMC medical imaging2026
Pre-treatment prediction of microsatellite instability in colon cancer: a nomogram model combining clinicopathological features and pre-treatment CT-based radiomics.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Carbohydrate antigen 72-4 and tumor-associated glycoprotein 72 - tumor marker: Past, present and future.World journal of gastrointestinal pathophysiology · 2026Review
- V7-DiVA: a feature-based Deep MIL colorectal whole-slide histopathology research platform with blinded external image-input validation.Journal of translational medicine · 2026Article
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5 authors.
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Abstract
backgroundDetermining 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.
objectiveThis study aims to evaluate the utility of nomogram model that integrates clinicopathological indicators and pre-treatment CT-based radiomics features for predicting DNA mismatch repair deficiency (dMMR) /microsatellite instability (MSI) status in colon cancer prior to treatment.
methodsA 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.
resultsMultivariate 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.
conclusionThe 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.
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