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.
超声检查因其高空间分辨率，成为对计算机断层扫描和/或磁共振成像所发现肿大淋巴结进行详细评估的首选方法。然而，超声检查的诊断性能依赖于检查者的专业水平。为辅助超声诊断，我们开发了基于YOLOv7的深度学习模型用于超声图像中转移性淋巴结的检测，并将其检测性能与经验丰富的放射科医师及低年资住院医师的判读结果进行比较。研究纳入了126例头颈部鳞状细胞癌患者的462幅B超和D超图像，涵盖经组织病理学证实的261个转移性和279个非转移性淋巴结。基于YOLOv7的B超和D超模型分别使用对应模式的训练和验证图像进行优化，并采用相应模式的测试图像评估其对转移性淋巴结的检测性能。结果显示：D超模型的性能与放射科医师相当，且优于住院医师对D超图像的判读；而B超模型在B超图像上的表现虽高于住院医师，但低于放射科医师。因此，基于YOLOv7的B超和D超模型可辅助低年资住院医师进行超声诊断，其中D超模型甚至能将住院医师的诊断能力提升至与经验丰富放射科医师相当的水平。
肿瘤(癌症)患者之家