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已发表论文
基于超声影像组学分析的鼻咽癌腮腺淋巴结转移预测
Authors Long X, Xue Y, Zou R, Yang S, Liu Z, Huang Q, Peng C, Han X, Kong W, Zheng W
Received 18 March 2025
Accepted for publication 28 August 2025
Published 13 September 2025 Volume 2025:17 Pages 1971—1980
DOI https://doi.org/10.2147/CMAR.S526722
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Harikrishna Nakshatri
Xingzhang Long,1,2,* Yao Xue,1,3,* Ruhai Zou,1 Shangman Yang,1 Zhong Liu,4 Qicai Huang,4 Chuan Peng,1 Xu Han,1 Weixuan Kong,1 Wei Zheng1
1Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People’s Republic of China; 2Department of Ultrasound, Shenzhen Hospital, Southern Medical University, Shenzhen, People’s Republic of China; 3Department of Ultrasound, The Tenth Clinical Medical College of Guangzhou University of Traditional Chinese Medicine, Zhongshan Hospital of Traditional Chinese Medicine, Zhongshan, People’s Republic of China; 4National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements, and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, People’s Republic of China
*These authors contributed equally to this work
Correspondence:Wei Zheng, Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou, 510060, People’s Republic of China, Email zhengwei@sysucc.org.cn
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.
Keywords: nasopharyngeal carcinoma, parotid lymph node metastasis, ultrasound, radiomics
1Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People’s Republic of China; 2Department of Ultrasound, Shenzhen Hospital, Southern Medical University, Shenzhen, People’s Republic of China; 3Department of Ultrasound, The Tenth Clinical Medical College of Guangzhou University of Traditional Chinese Medicine, Zhongshan Hospital of Traditional Chinese Medicine, Zhongshan, People’s Republic of China; 4National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements, and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, People’s Republic of China
*These authors contributed equally to this work
Correspondence:Wei Zheng, Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou, 510060, People’s Republic of China, Email zhengwei@sysucc.org.cn
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.
Keywords: nasopharyngeal carcinoma, parotid lymph node metastasis, ultrasound, radiomics