ArticleBMC gastroenterology2025
A CT-based decision tree model for differentiating sub-3 cm gastric ectopic pancreas from gastrointestinal stromal tumors.
Article in BMC gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo develop a CT-based decision tree model integrating clinical and imaging features for the preoperative differentiation of gastric ectopic pancreas (GEPs) and gastrointestinal stromal tumors (GISTs) with a maximum diameter of less than 3 cm.
methodsThis retrospective study included 86 patients with pathologically confirmed GEPs (n = 26) and GISTs (n = 60), all with lesions smaller than 3 cm. Clinical information and CT features were collected. The dataset was divided into training and testing sets. A decision tree classification model was constructed using key variables selected from the training set via univariate analyses and logistic regression. The decision tree's hyperparameters were optimised using five-fold cross-validation. Diagnostic performance was evaluated on an independent test set, including plotting ROC curves to calculate AUC values, sensitivity, and specificity, alongside using calibration curves to assess goodness-of-fit. Furthermore, the SHAP method was employed to provide visual explanations for the final model's predictions.
resultsThe decision tree model identified four key variables: age (clinical factor) and three CT features: ratio of lesion-to-pancreas attenuation in the arterial phase(A2), lesion long-to-short diameter ratio (LD/SD ratio), and intralesional low attenuation (ILA). The model, based on these four features, achieved an AUC of 0.744(95% CI:0.589-0.950), with sensitivity of 76.9% and specificity of 84.6%. Concurrently, calibration analysis substantiated the model's exceptional predictive precision. The Brier score (0.0648) and the Hosmer-Lemeshow test (χ
conclusionsThe CT-based decision tree model, integrating four clinical and CT features, provides a reliable and visualized tool for differentiating GEPs from GISTs with a maximum diameter of less than 3 cm, demonstrating strong diagnostic performance.
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