BMC gastroenterology · 2025
A CT-based decision tree model for differentiating sub-3 cm gastric ectopic pancreas from gastrointestinal stromal tumors.
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Duan Jiaqi, Zhao Yunzhi, Shi Qihao, Wang Yining, Huang Xiaoshan, Rao Qinpan, Fan Shufeng, Xu Jianxia
Аннотация
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.
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.