Oral surgery, oral medicine, oral pathology and oral radiology · 2025
Ultrasound-based deep learning to differentiate salivary gland tumors.
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He Jialu, Zhou Xueer, Hu Yilin, Zhou Jinbo, Zhu Guiquan, Pan Jian
Аннотация
OBJECTIVE: Accurate preoperative diagnosis is essential for selecting appropriate surgical interventions. This study aims to develop a deep learning model based on ultrasound (US) imaging to accurately differentiate between benign and malignant salivary gland tumors (SGTs).
STUDY DESIGN: A retrospective study was conducted on 315 patients who had preoperative US examinations and pathologically confirmed SGTs following surgical resection at our department (2020-2024). We included all three major salivary glands in our analysis, addressing class imbalance issues and expanding the scope of our study. US images were processed using several convolutional neural networks, including Inception v3, ResNet101d, EfficientNet, DenseNet, Vision Transformer, and ResNet50d. The ResNet50d model was fine-tuned using Focal Loss to further address class imbalance. The model's performance was compared with sonographers' diagnoses.
RESULTS: DeepSGT effectively identified critical regions within US images, achieving great diagnostic performance with an accuracy of 91.1%, sensitivity of 92.9%, specificity of 89.2%, and an area under the curve (AUC) of 0.94. This performance significantly exceeded that of sonographers, who had an accuracy of 80% and an AUC of 0.73.
CONCLUSIONS: The DeepSGT model demonstrates superior diagnostic capabilities over traditional methods in distinguishing benign from malignant SGTs, offering a valuable tool for clinical decision-making.
STUDY DESIGN: A retrospective study was conducted on 315 patients who had preoperative US examinations and pathologically confirmed SGTs following surgical resection at our department (2020-2024). We included all three major salivary glands in our analysis, addressing class imbalance issues and expanding the scope of our study. US images were processed using several convolutional neural networks, including Inception v3, ResNet101d, EfficientNet, DenseNet, Vision Transformer, and ResNet50d. The ResNet50d model was fine-tuned using Focal Loss to further address class imbalance. The model's performance was compared with sonographers' diagnoses.
RESULTS: DeepSGT effectively identified critical regions within US images, achieving great diagnostic performance with an accuracy of 91.1%, sensitivity of 92.9%, specificity of 89.2%, and an area under the curve (AUC) of 0.94. This performance significantly exceeded that of sonographers, who had an accuracy of 80% and an AUC of 0.73.
CONCLUSIONS: The DeepSGT model demonstrates superior diagnostic capabilities over traditional methods in distinguishing benign from malignant SGTs, offering a valuable tool for clinical decision-making.