ArticleAbdominal radiology (New York)2026
Multitask radioclinical decision stratification in non-metastatic colon cancer: integrating MMR status, pT staging, and high-risk pathological factors.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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7 authors.
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Abstract
objectivesConstructing a multi-task global decision support system based on preoperative enhanced CT features to predict the mismatch repair (MMR) status, T stage, and pathological risk factors (e.g., histological differentiation, lymphovascular invasion) for patients with non-metastatic colon cancer.
methods372 eligible non-metastatic colon cancer (NMCC) participants (training cohort: n = 260; testing cohort: n = 112) were enrolled from two institutions. The 34 features (imaging features: n = 27; clinical features: n = 7) were subjected to feature selection using LASSO, Boruta, ReliefF, mRMR, and XGBoost-RFE, respectively. In each of the three categories-MMR, pT staging, and pathological risk factors-four features were selected to construct the total feature set. Subsequently, the multitask model was built with 14 machine learning algorithms. The predictive performance of the machine model was evaluated using the area under the receiver operating characteristic curve (AUC).
resultsThe final feature set for constructing the model is based on the mRMR feature screening method. For the final MMR classification, pT staging, and pathological risk factors, SVC, Bernoulli NB, and Decision Tree algorithm were selected respectively, with AUC scores of 0.80 [95% CI 0.71-0.89], 0.82 [95% CI 0.71-0.94], and 0.85 [95% CI 0.77-0.93] on the test set. Furthermore, a direct multiclass model constructed using the total feature set resulted in an average AUC of 0.77 across four management plans in the test set.
conclusionThe multi-task machine learning model proposed in this study enables non-invasive and precise preoperative stratification of patients with NMCC based on MMR status, pT stage, and pathological risk factors. This predictive tool demonstrates significant potential in facilitating preoperative risk stratification and guiding individualized therapeutic strategies.
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