Radiomic features from intratumoral and peritumoral regions on portal venous phase CT for multicenter prediction of TP53 mutation in pancreatic cancer.

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Abstract

TP53 mutation, occurring in 50-70% of pancreatic ductal adenocarcinomas (PDAC), is a major determinant of tumor aggressiveness and treatment response. Current assessments rely on invasive biopsy, underscoring the need for reliable non-invasive prediction. In this multicenter study, 216 PDAC patients (training = 162; external test = 54) who underwent preoperative portal-venous phase CT (PV-phase CT) were analyzed. Intratumoral and 3-mm peritumoral regions were manually segmented, and 1, 561 radiomic features were extracted. Six machine-learning classifiers were trained following feature selection and SMOTE, both of which were strictly nested within the cross-validation training folds to prevent data leakage. Model performance was evaluated by AUC, DeLong test, decision curve, and calibration analyses; interpretability was assessed using SHAP. The Intra-Peri Model (IPM) combining intratumoral and peritumoral features achieved the best performance. The XGBoost classifier yielded an AUC of 0.893 (95% CI, 0.781-1.000) in the external test set, significantly outperforming single-region models (P < 0.05). SHAP analysis identified intratumoral gray-level skewness and peritumoral texture correlation as the most influential predictors, where greater intratumoral asymmetry and lower peritumoral correlation indicated higher likelihood of TP53 mutation. Integrating intratumoral and peritumoral radiomics enables accurate, non-invasive prediction of TP53 status in PDAC. This model serves as a promising auxiliary tool for individualized treatment planning, warranting further prospective validation.

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