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A comprehensive model with dual plane ultrasound based deep learning radiomics to distinguish parotid pleomorphic adenoma from Warthin tumor
Abstract Objective: To develop and validate a dual-plane ultrasound (US)-based deep learning radiomics model for the noninvasive discrimination between parotid pleomorphic adenoma (PA) and Warthin tumor (WT). Methods: This retrospective two-center study enrolled 656 patients with pathologically confirmed parotid tumors (361 PA, 295 WT). Cases from Center 1 (n = 556) were randomly split into training and internal validation sets (7:3), while Center 2 cases (n = 100) formed the external test set.
Prediction of High-risk Capsule Characteristics for Recurrence of Pleomorphic Adenoma in the Parotid Gland Based on Habitat Imaging and Peritumoral Radiomics: A Two-center Study
Abbreviations AUC (area under the curve) CH (Calinski-Harabasz) DCA (decision curve analysis) ICC (intraclass correlation coefficient) LASSO (least absolute shrinkage and selection operator) mRMR (max-relevance and min-redundancy) OR (odds ratios) PA (pleomorphic adenoma) PS (pseudopodia) ROC (receiver operating characteristic) ROI (region of interest) SNs (satellite nodules) SHAP (shapley additive explanation) US (ultrasound) Key Words Radiomics Habitat Imaging Peritumoral Pleomorphic...
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