Prediction of solid pseudopapillary tumor invasiveness of the pancreas based on multiphase contrast-enhanced CT radiomics nomogram - Ren, Dabin; Liu, Liqiu; Sun, Aiyun; Wei, Yuguo; Wu, Tingfan; Wang, Yongtao; He, Xiaxia; Liu, Zishan; Zhu, Jie; Wang, Guoyu (Frontiers in Oncology, 2025)
ObjectivesTo construct a multiphase contrast-enhanced CT-based radiomics nomogram that combines traditional CT features and radiomics signature for predicting the invasiveness of pancreatic solid pseudopapillary neoplasm (PSPN).MethodsA total of 114 patients with surgical pathologic diagnoses of PSPN were retrospectively included and classified into training (n = 79) and validation sets (n = 35). Univariate and multivariate analyses were adopted for screening traditional CT features significantly associated with the invasiveness of PSPN as independent predictors, and a traditional CT model was established. Radiomics features were extracted from the contrast-enhanced CT images, and logistic regression analysis was employed to establish a machine learning model, including an unenhanced model (model U), an arterial phase model (model A), a venous phase model (model V), and a combined radiomics model (model U+A+V). A radiomics nomogram was subsequently constructed and visualized by combining traditional CT independent predictors and radiomics signature. Model performance was assessed through Delong’s test and receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was applied to assess the model’s clinical utility.ResultsMultivariate analysis suggested that solid tumors (OR = 6.565, 95% CI: 1.238–34.816, P = 0.027) and ill-defined tumor margins (OR = 2.442, 95% CI: 1.038–5.741, P = 0.041) were independent predictors of the invasiveness of PSPN. The areas under the curve (AUCs) of the traditional CT model in the training and validation sets were 0.653 and 0.797, respectively. Among the four radiomics models, the model U+A+V exhibited the best diagnostic performance, with AUCs of 0.857 and 0.839 in the training and validation sets, respectively. In addition, the AUCs of the nomogram in the training and validation sets were 0.87 and 0.867, respectively, which were better than those of the radiomics model and the traditional CT model. The DCA results indicated that with the threshold probability being within the relevant range, the radiomics nomogram offered an increased net benefit to clinical decision making.ConclusionMultiphase contrast-enhanced CT radiomics can noninvasively predict the invasiveness of PSPN. In addition, the radiomics nomogram combining radiomics signature and traditional CT signs can further improve classification ability.