ArticleBMC medical imaging2025
A radiomics-based interpretable model integrating delayed-phase CT and clinical features for predicting the pathological grade of appendiceal pseudomyxoma peritonei.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Carbohydrate antigen 72-4 and tumor-associated glycoprotein 72 - tumor marker: Past, present and future.World journal of gastrointestinal pathophysiology · 2026Review
- Non-invasive prediction of occult peritoneal metastasis (OPM) in gastric cancer using logistic regression and random forest integrative models with CT radiomics and clinical parameters: machine learning prediction of gastric OPM.Journal of gastrointestinal oncology · 2026Article
- Prognostic prediction model for rectal cancer based on CMS subtype indicators and SHAP-based interpretable analysis.Abdominal radiology (New York) · 2026Article
- Shear wave elastography to distinguish between hard and soft mucin in pseudomyxoma peritonei.Pleura and peritoneum · 2026Article
- Research progress in the diagnosis and treatment of pseudomyxoma peritonei.World journal of surgical oncology · 2026Review
- Synchronous gastric and appendiceal mucinous adenocarcinomas: a rare case report.Frontiers in oncology · 2026Article
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Authors and funding
6 authors.
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Abstract
objectiveThis study aimed to develop an interpretable machine learning model integrating delayed-phase contrast-enhanced CT radiomics with clinical features for noninvasive prediction of pathological grading in appendiceal pseudomyxoma peritonei (PMP), using Shapley Additive Explanations (SHAP) for model interpretation. MATERIALS AND
methodsThis retrospective study analyzed 158 pathologically confirmed PMP cases (85 low-grade, 73 high-grade) from January 4, 2015 to April 30, 2024. Comprehensive clinical data including demographic characteristics, serum tumor markers (CEA, CA19-9, CA125, D-dimer, CA-724, CA-242), and CT-peritoneal cancer index (CT-PCI) were collected. Radiomics features were extracted from preoperative contrast-enhanced CT scans using standardized protocols. After rigorous feature selection and five-fold cross-validation, we developed three predictive models: clinical-only, radiomics-only, and a combined clinical-radiomics model using logistic regression. Model performance was evaluated through ROC analysis (AUC), Delong test, decision curve analysis (DCA), and Brier score, with SHAP values providing interpretability.
resultsThe combined model demonstrated superior performance, achieving AUCs of 0.91 (95%CI:0.86-0.95) and 0.88 (95%CI:0.82-0.93) in training and testing sets respectively, significantly outperforming standalone models (P < 0.05). DCA confirmed greater clinical utility across most threshold probabilities, with favorable Brier scores (training:0.124; testing:0.142) indicating excellent calibration. SHAP analysis identified the top predictive features: wavelet-LHH_glcm_InverseVariance (radiomics), original_shape_Elongation (radiomics), and CA-199 (clinical).
conclusionOur SHAP-interpretable combined model provides an accurate, noninvasive tool for PMP grading, facilitating personalized treatment decisions. The integration of radiomics and clinical data demonstrates superior predictive performance compared to conventional approaches, with potential to improve patient outcomes.
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