Ultrasound-Based Deep Learning Radiomics to Predict Cervical Lymph Node Metastasis in Major Salivary Gland Carcinomas

Abstract

Introduction and aims: 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.

Methods: 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).

Results: 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.

Conclusion: The composite model integrating clinical, US, radiomics, and DL features accurately noninvasively predicts CLNM preoperatively in MSGCs.

Clinical relevance: 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.

Keywords: Cervical lymph node metastasis; Deep learning; Major salivary gland carcinomas; Radiomics; Ultrasound.

Fig. 1

Patient enrollment process and study design workflow. (A) Patient enrollment process of this study. (B) Study design and workflow. Note: MSGCs, major salivary gland carcinomas; pN0, pathologically lymph node-negative; pN+, pathologically lymph node-positive; ICC, intraclass correlation coefficient; mRMR, maximum relevance minimum redundancy; LASSO, the least absolute shrinkage and selection operator; ROC, receiver operating characteristic; DCA, decision curve analysis.

Fig. 2

After dimension reduction by mRMR and LASSO regression, the distribution of radiomics and deep learning features and coefficients, as well as the construction of radiomics and deep learning scores. (A, B) Distribution of radiomics (A) and deep learning (B) features and coefficients. (C, D) Distribution of radiomics scores (C) and deep learning scores (D) in the training and validation cohorts. Note: mRMR, maximum relevance minimum redundancy; LASSO, the least absolute shrinkage and selection operator. ^⁎⁎⁎Statistically significant at P < 0.001 as determined by the Mann-Whitney U test.

Fig. 3

ROC curves for radiomics and deep learning scores. (A) ROC curves for radiomics scores in the training and validation cohorts. (B) ROC curves for deep learning scores in the training and validation cohorts. Note: ROC, receiver operating characteristic; AUC, area under the curve.

Fig. 4

Feature variable selection and ROC curves of different models. (A, B) 6 features with non-zero coefficients were selected through LASSO regression; (C) ROC curves for the training cohort; (D) ROC curves for the validation cohort. Note: LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic; AUC, area under the curve; LR, logistic regression; DT, decision tree; RF, random forest; AdaBoost, adaptive boosting; XGBoost, extreme gradient boosting; ANN, artificial neural network; SVM, support vector machine; KNN, K-nearest neighbors.

Fig. 5

Predictive performance of different models. (A-D) Predictive performance of different models in the training cohort. (E-H) Predictive performance of different models in the validation cohort. Note: LR, logistic regression; DT, decision tree; RF, random forest; AdaBoost, adaptive boosting; XGBoost, extreme gradient boosting; ANN, artificial neural network; SVM, support vector machine; KNN, K-nearest neighbors.

Fig. 6

Results of multivariate regression analysis and construction of nomogram. (A) The multivariate regression results of the LR model. (B) Nomogram for the LR model. Note: logistic regression.