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Deep transfer learning radiomics combined with explainable machine learning for predicting malignancy risk in parotid
Wei Wei1, Wang Zhou2, Chen Chen3
1Department of Ultrasound, the First Affiliated Hospital of Anhui Medical University, Hefei, China; The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, China.
Objective:
This study aimed to develop and validate an ultrasound (US)-based deep transfer learning radiomics model, integrated with explainable machine learning, for the preoperative malignant risk prediction of parotid gland tumors (PGTs).
Methods:
Data from 1,191 patients were retrospectively collected from three medical centers, and postoperative histopathological examination was used as the reference standard. Radiomics features and deep transfer learning features (ResNet50, Inception_V3, Vgg19) were extracted from the US images. Key predictive variables were selected using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO). Six classifiers-decision tree, gradient boosting machine, k-nearest neighbors, logistic regression, naïve Bayes, and random forest-were employed to construct models based on five feature sets: Clinical model, radiomics (Rad) model, deep transfer learning radiomics (DTLR) model, combined deep transfer learning and radiomics (DTLR-Rad) model, and a comprehensive combined model (CM Clinical + DTLR-Rad). Model performance was evaluated using the area under the curve (AUC). Feature importance was interpreted using SHapley Additive exPlanations (SHAP). A web application for real-time, personalized risk prediction was developed.
Results:
In external test sets 1 and 2, the CM Clinical + DTLR-Rad model based on the random forest classifier achieved the highest AUCs among the evaluated models, with 0.922 (95% CI: 0.890-0.954) and 0.959 (95% CI: 0.932-0.985), respectively. The integrated model outperformed the clinical-only and single-modality models in both external test sets. SHAP visualizations demonstrated the contribution of individual features. The web application provided both prediction probabilities and feature-level interpretability.
Conclusion:
The CM Clinical + DTLR-Rad model demonstrated good predictive performance. The integration of interpretable machine learning and a web-based application may enhance preoperative risk stratification for PGTs and support clinical decision-making.
