Ultrasound-Based Deep Learning Radiomics to Predict Cervical Lymph Node Metastasis in Major Salivary Gland Carcinomas.
Authors
Affiliations (5)
Affiliations (5)
- Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
- Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China.
- Department of Ultrasound, The Affiliated Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
- Department of Ultrasound, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China. Electronic address: [email protected].
- Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China. Electronic address: [email protected].
Abstract
Cervical lymph node metastasis (CLNM) critically impacts surgery approaches, prognosis, and recurrence in patients with major salivary gland carcinomas (MSGCs). We aimed to develop and validate an ultrasound (US)-based deep learning (DL) radiomics model for noninvasive prediction of CLNM in MSGCs. A total of 214 patients with MSGCs from 4 medical centers were divided into training (Centers 1-2, n = 144) and validation (Centers 3-4, n = 70) cohorts. Radiomics and DL features were extracted from preoperative US images. Following feature selection, radiomics score and DL score were constructed respectively. Subsequently, the least absolute shrinkage and selection operator (LASSO) regression was used to identify optimal features, which were then employed to develop predictive models using logistic regression (LR) and 8 machine learning algorithms. Model performance was evaluated using multiple metrics, with particular focus on the area under the receiver operating characteristic curve (AUC). Radiomics and DL scores showed robust performance in predicting CLNM in MSGCs, with AUCs of 0.819 and 0.836 in the validation cohort, respectively. After LASSO regression, 6 key features (patient age, tumor edge, calcification, US reported CLN-positive, radiomics score, and DL score) were selected to construct 9 predictive models. In the validation cohort, the models' AUCs ranged from 0.770 to 0.962. The LR model achieved the best performance, with an AUC of 0.962, accuracy of 0.886, precision of 0.762, recall of 0.842, and an F1 score of 0.8. The composite model integrating clinical, US, radiomics, and DL features accurately noninvasively predicts CLNM preoperatively in MSGCs. CLNM in MSGCs is critical for treatment planning, but noninvasive prediction is limited. This study developed an US-based DL radiomics model to enable noninvasive CLNM prediction, supporting personalized surgery and reducing unnecessary interventions.