Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma
Author:
Eida Sato1, Fukuda Motoki2, Katayama Ikuo1, Takagi Yukinori1, Sasaki Miho1, Mori Hiroki1, Kawakami Maki1, Nishino Tatsuyoshi1, Ariji Yoshiko2, Sumi Misa1ORCID
Affiliation:
1. Department of Radiology and Biomedical Informatics, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki 852-8588, Japan 2. Department of Oral Radiology, Osaka Dental University, 1-5-17 Otemae, Chuo-ku, Osaka 540-0008, Japan
Abstract
Ultrasonography is the preferred modality for detailed evaluation of enlarged lymph nodes (LNs) identified on computed tomography and/or magnetic resonance imaging, owing to its high spatial resolution. However, the diagnostic performance of ultrasonography depends on the examiner’s expertise. To support the ultrasonographic diagnosis, we developed YOLOv7-based deep learning models for metastatic LN detection on ultrasonography and compared their detection performance with that of highly experienced radiologists and less experienced residents. We enrolled 462 B- and D-mode ultrasound images of 261 metastatic and 279 non-metastatic histopathologically confirmed LNs from 126 patients with head and neck squamous cell carcinoma. The YOLOv7-based B- and D-mode models were optimized using B- and D-mode training and validation images and their detection performance for metastatic LNs was evaluated using B- and D-mode testing images, respectively. The D-mode model’s performance was comparable to that of radiologists and superior to that of residents’ reading of D-mode images, whereas the B-mode model’s performance was higher than that of residents but lower than that of radiologists on B-mode images. Thus, YOLOv7-based B- and D-mode models can assist less experienced residents in ultrasonographic diagnoses. The D-mode model could raise the diagnostic performance of residents to the same level as experienced radiologists.
Funder
Japan Society for the Promotion of Science
Subject
Cancer Research,Oncology
Reference55 articles.
1. Diagnostic performance of FDG PET/MRI for cervical lymph node metastasis in patients with clinically N0 head and neck cancer;Cebeci;Eur. Rev. Med. Pharmacol. Sci.,2023 2. Caldonazzi, N., Rizzo, P.C., Eccher, A., Girolami, I., Fanelli, G.N., Naccarato, A.G., Bonizzi, G., Fusco, N., d’Amati, G., and Scarpa, A. (2023). Value of Artificial Intelligence in Evaluating Lymph Node Metastases. Cancers, 15. 3. Sumi, M., Sato, S., and Nakamura, T. (2017). Extranodal spread of primary and secondary metastatic nodes: The dominant risk factor of survival in patients with head and neck squamous cell carcinoma. PLoS ONE, 12. 4. Validation of the pathological classification of lymph node metastasis for head and neck tumors according to the 8th edition of the TNM Classification of Malignant Tumors;Lopez;Oral Oncol.,2017 5. Nodal imaging in the neck: Recent advances in US, CT and MR imaging of metastatic nodes;Nakamura;Eur. Radiol.,2007
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