Clinical value of intratumoral and peritumoral CT radiomics models for discriminating benign and malignant parotid gland tumors - Zhang, Cong; Shi, Naijing; Wang, Yiru; Hao, Mohan; Ren, Jinwu (Frontiers in Oncology, 2025)
ObjectiveTo evaluate the utility of combining unenhanced and contrast-enhanced CT intratumoral and peritumoral radiomic features with clinical variables for distinguishing benign from malignant parotid gland tumors.MethodsWe retrospectively collected clinical and imaging data from 171 patients with pathologically confirmed parotid gland tumors treated at Baoding First Central Hospital between June 2019 and June 2025 (101 benign, 70 malignant). Tumor ROIs were manually delineated slice-by-slice on non-contrast, arterial-phase and venous-phase CT images, and peritumoral regions were automatically expanded by 1–4 mm. The cohort was randomly split into training and test sets at a 7:3 ratio. After extraction and selection of radiomic features, multiple models were constructed for intratumoral, various peritumoral ranges (1–4 mm) and intratumoral+peritumoral combinations. Model performance was evaluated by ROC curves, the optimal radiomics model was selected and integrated with the clinical model to produce a combined model, and a nomogram was subsequently developed.ResultsThe AUC values of the intratumoral, peritumoral (1–4 mm) and intratumoral+peritumoral models in the training set were 0.966, 0.953, 0.927, 0.983, 0.947, 0.959, 0.956, 0.909 and 0.976, respectively; in the test set the AUCs were 0.797, 0.766, 0.791, 0.714, 0.710, 0.805, 0.836, 0.778 and 0.753, respectively. According to the DeLong test, in the training set the differences between intratumor+peritumor 3mm vs. peritumor 3mm and between intratumor+peritumor 3mm vs. intratumor+peritumor 4mm were statistically significant (p = 0.022 and p = 0.026, respectively); in the test set, differences among the models were not statistically significant (P > 0.05). From this, it can be seen the combined intratumoral + 2 mm peritumoral radiomics model demonstrated superior diagnostic performance compared to models based exclusively on either intratumoral or peritumoral features. Consequently, this model was designated as the optimal radiomic signature and was integrated with independent clinical risk factors—specifically symptomatology and tumor margin status—to construct a combined clinical–radiomics predictive model. In the training and test sets, the AUC values of the radiomics model were 0.956 and 0.836, respectively, while those of the clinical model were 0.774 and 0.703. The combined model achieved AUC values of 0.974 and 0.844, demonstrating significantly superior diagnostic performance compared to the standalone clinical or radiomics models, along with the highest clinical utility. According to the Delong test, in the training set the differences between the clinical model and the combined model, and between the clinical model and the radiomics model, were statistically significant (p = 0.000 and p = 0.000, respectively); in the test set, differences among the models were not statistically significant (P > 0.05).ConclusionA multiphase CT radiomics approach that fuses intratumoral features with a 2 mm peritumoral zone robustly distinguishes benign from malignant parotid gland tumors. Integration with key clinical predictors further enhances diagnostic accuracy, supporting clinical translation of the combined model for noninvasive tumor characterization.