Student Publications [Scholarly]

From Multimodal Data to Clinical Insight: An Explainable Model for Preoperative Salivary Gland Lesion Diagnosis

Document Type

Article

Abstract

Objective: To develop and validate a multimodal dual-step support vector machine model (SVM-DualNet) for the preoperative three-class classification of salivary gland lesions (SGLs) to support clinical decision-making. Methods: We retrospectively collected clinical, conventional ultrasound (CUS), shear wave elastography (SWE), and radiomics features from 284 patients with SGLs. For malignancy discrimination and pleomorphic adenoma identification, linear SVM models based on different modality combinations were constructed and compared. The best-performing binary models were sequentially combined to form SVM-DualNet. SHapley Additive exPlanations (SHAP) were applied for global and case-level interpretation and incorporated into diagnostic assistance. Clinical utility was evaluated by comparing the junior radiologist's diagnostic performance before and after SHAP assistance and by comparison with the senior radiologist. Results: In the test cohort, SVM-DualNet achieved a balanced accuracy of 0.76 and a macro F1 score of 0.82 for the three-class classification. The binary models discriminated malignancy and pleomorphic adenoma with AUCs of 0.90 (95% CI: 0.82–0.97) and 0.85 (95% CI: 0.76–0.94), respectively. SHAP-assisted review improved the junior radiologist's balanced accuracy from 0.55 to 0.70 and macro F1 from 0.57 to 0.75, approaching the senior radiologist's performance. Conclusions: The model provides reliable preoperative classification of SGLs and can assist clinicians in decision-making. © 2026 John Wiley & Sons Ltd.

Publication Title

Oral Diseases

Publication Date

2026

ISSN

1354-523X

DOI

10.1111/odi.70199

Keywords

diagnostic assistance, machine learning, radiomics, salivary gland lesions, shear wave elastography, ultrasound

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