Associate Proffessor, Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of dentistry, Nagoya, Japan.
Instructor, Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of dentistry, Nagoya, Japan.
Oral Surg Oral Med Oral Pathol Oral Radiol. 2019 May;127(5):458-463. doi: 10.1016/j.oooo.2018.10.002. Epub 2018 Oct 15.
Although the deep learning system has been applied to interpretation of medical images, its application to the diagnosis of cervical lymph nodes in patients with oral cancer has not yet been reported. The purpose of this study was to evaluate the performance of deep learning image classification for diagnosis of lymph node metastasis.
The imaging data used for evaluation consisted of computed tomography (CT) images of 127 histologically proven positive cervical lymph nodes and 314 histologically proven negative lymph nodes from 45 patients with oral squamous cell carcinoma. The performance of a deep learning image classification system for the diagnosis of lymph node metastasis on CT images was compared with the diagnostic interpretations of 2 experienced radiologists by using the Mann-Whitney U test and χ analysis.
The performance of the deep learning image classification system resulted in accuracy of 78.2%, sensitivity of 75.4%, specificity of 81.0%, positive predictive value of 79.9%, negative predictive value of 77.1%, and area under the receiver operating characteristic curve of 0.80. These values were not significantly different from those found by the radiologists.
The deep learning system yielded diagnostic results similar to those of the radiologists, which suggests that this system may be valuable for diagnostic support.
深度学习系统已应用于医学图像解读，但尚未应用于口腔癌患者颈部淋巴结的诊断。本研究旨在评估深度学习图像分类在诊断淋巴结转移中的性能。
评估使用的影像学数据来自 45 例口腔鳞状细胞癌患者的 127 个经组织学证实的阳性颈部淋巴结和 314 个经组织学证实的阴性淋巴结的计算机断层扫描（CT）图像。通过 Mann-Whitney U 检验和 χ 分析比较深度学习图像分类系统对 CT 图像上淋巴结转移诊断的性能与 2 名经验丰富的放射科医生的诊断解读。
深度学习图像分类系统的性能得出准确率为 78.2%、敏感度为 75.4%、特异性为 81.0%、阳性预测值为 79.9%、阴性预测值为 77.1%、受试者工作特征曲线下面积为 0.80。这些值与放射科医生的发现没有显著差异。
深度学习系统得出的诊断结果与放射科医生相似，这表明该系统可能对诊断支持具有重要价值。
