ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1542265
Application of deep learning based on convolutional neural network model in multimodal ultrasound diagnosis of unexplained cervical lymph node enlargement
Provisionally accepted
- 1First Affiliated Hospital of Harbin Medical University, Harbin, China
- 2Department of Breast Surgery, Jilin Cancer Hospital, Changchun, Jilin Province, China
- 3Department of Ultrasound, Jilin Cancer Hospital, changchun, China
- 4Department of Ultrasound, Jilin Cancer Hospital,, changchun, China
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This study retrospectively analyzed the multimodal ultrasound features and clinical characteristics of 586 patients with unexplained cervical lymphadenopathy who were treated at three hospitals between October 2019 and December 2022. Statistically significant differences were found in the clinical and ultrasound features of all patients, including location, shape, margin, and color Doppler flow imaging (CDFI) (p<0.05). Deep learning models, particularly convolutional neural networks (CNNs), demonstrated great potential in classifying cervical lymph node pathologies using multimodal ultrasound images, including 2D imaging, color Doppler flow imaging (CDFI), and elastography. First, we pre-trained four convolutional neural networks using a public medical image dataset. Then, we fine-tuned the models for three-class classification of lymph nodes into metastatic, lymphoma, and benign using 2D, CDFI, and elastography images from the patients' lymph nodes. The pre-trained ResNet model performed excellently, with an elastography AUC of 0.925, outperforming other models. Elastography became the most reliable feature extraction dataset, significantly enhancing the model's accuracy in distinguishing between benign, lymphoma, and metastatic lymph nodes. Ablation experiments showed that pre-training significantly improved accuracy compared to non-pre-trained models. Grad-CAM visualization provided valuable interpretability, revealing how the model focuses on specific areas corresponding to each pathology. Based on this model, we developed a user-friendly server, CV4LymphNode (https://hwwlab.com/webserver/cv4lymphnode). This study highlights the potential of deep learning in accurately classifying cervical lymph node pathologies.
Keywords: deep learning, Color Doppler Flow Imaging(CDFI), elastography, lymph node classification, Webserver
Received: 09 Dec 2024; Accepted: 20 May 2025.
Copyright: © 2025 Yang, Jiang, Zhang, Li and Tong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiuhua Yang, First Affiliated Hospital of Harbin Medical University, Harbin, China
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