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Objective <p>To 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&#xa0;cm. </p> Methods <p>This retrospective study included 86 patients with pathologically confirmed GEPs (<i>n</i> = 26) and GISTs (<i>n</i> = 60), all with lesions smaller than 3&#xa0;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.</p> Results <p>The 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<sup>2</sup> = 5.365, df = 8, <i>P</i> = 0.718) both demonstrated a high degree of agreement between the model's predicted probabilities and the actual observed values.</p> Conclusions <p>The 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&#xa0;cm, demonstrating strong diagnostic performance.</p>

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A CT-based decision tree model for differentiating sub-3 cm gastric ectopic pancreas from gastrointestinal stromal tumors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月19日

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  • Jiaqi Duan,
  • Yunzhi Zhao,
  • Qihao Shi,
  • Yining Wang,
  • Xiaoshan Huang,
  • Qinpan Rao,
  • Shufeng Fan,
  • Jianxia Xu

摘要

Objective

To 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.

Methods

This 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.

Results

The 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 (χ2 = 5.365, df = 8, P = 0.718) both demonstrated a high degree of agreement between the model's predicted probabilities and the actual observed values.

Conclusions

The 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.