Related Experiment Video For Cervical lymph nodes (CLNs)
Updated: Jan 14, 2026
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Development and validation of deep learning models for qualitative classification of benign and malignant enlarged
Hong Yuan1, Juan Ruan1, Wen Wen1
1Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China.
Background:
Enlargement of cervical lymph nodes (CLNs) is a common clinical response to lesions in the neck as well as in other parts of the body. Accurate qualitative diagnosis of lymph nodes can provide important reference information for clinical decision-making. While histopathological diagnosis remains the gold standard for differentiating benign from malignant CLNs, it is an invasive procedure. Ultrasonography serves as a non-invasive imaging modality widely employed in clinical practice for the preoperative evaluation and qualitative assessment of CLNs; however, its diagnostic accuracy is operator-dependent. In this study, we investigated ultrasound image features between benign and malignant CLNs and developed deep learning (DL) models for the qualitative diagnosis of CLNs.
Methods:
Patients with pathologically confirmed CLNs via ultrasound-guided biopsy from January 2020 to December 2023 were retrospectively included. The gold standard was histopathological diagnosis. Ultrasound features of CLNs were documented, and their value in differentiating benign from malignant CLNs was assessed using univariate analysis. DL models were developed to qualitatively diagnose the benign and malignant CLNs. Model performance was evaluated using receiver operating characteristic curves, accuracy curves, recall curves, and loss curves.
Results:
A total of 3,014 CLNs from 2,697 patients were included in this study, with 1,489 classified as benign cases and 1,525 as malignant cases. Almost all DL models demonstrated satisfactory performance in qualitative diagnosis of CLNs, achieving area under the curve (AUC) values ranging from 0.56 to 0.81, with the VGG16 model exhibiting the best performance with an AUC of 0.81 [95% confidence interval (CI): 0.77-0.86], accuracy of 0.73, sensitivity of 0.71, and specificity of 0.74. In comparison to ultrasonography, the VGG16, ResNet101, and ResNet50 models showed significantly superior predictive performance (P<0.05).
Conclusions:
DL models utilizing ultrasound images demonstrated promising performance in the qualitative diagnosis of CLNs. This approach enhanced the diagnostic accuracy of preoperative ultrasound assessment, thereby allowing a subset of patients to avoid unnecessary biopsies and optimizing clinical decision-making.
