Cancer management and research · 2025
Parotid Lymph Node Metastasis Prediction of Nasopharyngeal Carcinoma Based on Ultrasound Radiomics Analysis.
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Long Xingzhang, Xue Yao, Zou Ruhai, Yang Shangman, Liu Zhong, Huang Qicai, Peng Chuan, Han Xu, Kong Weixuan, Zheng Wei
Аннотация
BACKGROUND: To evaluate the clinical utility of ultrasound radiomics in predicting parotid lymph node metastasis (PLNM) in nasopharyngeal carcinoma (NPC) patients.
METHODS: Grayscale ultrasound (US) images of parotid gland nodules were segmented, and radiomics features were extracted. An support vector machine (SVM) model was built using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm for feature selection. Different SVM models were built based on clinical characteristics, radiomics features, and a combination of these features. Performance of the models was assessed using the area under the curve (AUCs), sensitivity and specificity.
RESULTS: Among 406 patients (192 PLNM, 214 benign), a total of 406 nodules were included in this study. Thirty-one radiomics features were selected as significant using the LASSO algorithm from the 474 extracted radiomics features. In the clinical model, NPC patients with suspicious parotid gland nodules of irregular shape, poorly defined margins, long/short axis ratio (LSR) <1, and posterior acoustic enhancement (PAE) were significant variables for PLNM (p<0.05). In the validation dataset, the AUC were 0.916 (95% CI: 0.876-0.983) in the clinical model, 0.830 (95% CI: 0.784-0.872) in the single radiomics model, and 0.928 (95% CI: 0.792-0.945) in the combined model. The calibration curve of the different models and decision curve analysis (DCA) demonstrated the diagnostic performance of the combined model.
CONCLUSION: The combined model using ultrasound radiomics has clinical utility in identifying useful US features and enhancing the diagnostic accuracy of ultrasound for detecting PLNM in patients with NPC.
METHODS: Grayscale ultrasound (US) images of parotid gland nodules were segmented, and radiomics features were extracted. An support vector machine (SVM) model was built using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm for feature selection. Different SVM models were built based on clinical characteristics, radiomics features, and a combination of these features. Performance of the models was assessed using the area under the curve (AUCs), sensitivity and specificity.
RESULTS: Among 406 patients (192 PLNM, 214 benign), a total of 406 nodules were included in this study. Thirty-one radiomics features were selected as significant using the LASSO algorithm from the 474 extracted radiomics features. In the clinical model, NPC patients with suspicious parotid gland nodules of irregular shape, poorly defined margins, long/short axis ratio (LSR) <1, and posterior acoustic enhancement (PAE) were significant variables for PLNM (p<0.05). In the validation dataset, the AUC were 0.916 (95% CI: 0.876-0.983) in the clinical model, 0.830 (95% CI: 0.784-0.872) in the single radiomics model, and 0.928 (95% CI: 0.792-0.945) in the combined model. The calibration curve of the different models and decision curve analysis (DCA) demonstrated the diagnostic performance of the combined model.
CONCLUSION: The combined model using ultrasound radiomics has clinical utility in identifying useful US features and enhancing the diagnostic accuracy of ultrasound for detecting PLNM in patients with NPC.