Cystic cervical lymph nodes of papillary thyroid carcinoma, tuberculosis and human papillomavirus positive oropharyngeal squamous cell carcinoma: utility of deep learning in their differentiation on CT
Cystic cervical lymph nodes of papillary thyroid carcinoma, tuberculosis and human papillomavirus positive oropharyngeal squamous cell carcinoma: utility of deep learning in their differentiation on CT
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DOI：
10.1016/j.amjoto.2021.103026
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发表时间：
2021-04-13
影响因子：
2.5
通讯作者：
Sakai, Osamu
中科院分区：
文献类型：
--
作者：
Onoue, Keita;Fujima, Noriyuki;Sakai, Osamu
目的：颈部淋巴结内有囊性改变，可见于多种病理，包括甲状腺乳头状癌（PTC）、结核（TB）和HPV阳性口咽鳞状细胞癌（HPV+OPSCC）。在没有已知的原发肿瘤或可靠病史的情况下，鉴别这些淋巴结是困难的。在这项研究中，我们评估了深度学习在CT上区分PTC，TB和HPV+OPSCC的病理淋巴结方面的效用。方法：根据病理记录和可疑的形态学特征，共选择173个淋巴结（PTC 55个，TB 58个，HPV+OPSCC 60个）。将这些淋巴结分为训练集（n = 131）和测试集（n = 42）。在深度学习分析中，从CT切片中提取JPEG淋巴结图像，其中包括每个淋巴结的最大区域，并将其输入深度学习训练会话以创建诊断模型。迁移学习与ResNet-101的深度学习模型架构一起使用。使用测试集，将深度学习模型的诊断性能与组织病理学诊断以及两名委员会认证的神经放射科医生的诊断性能进行了比较。结果如下：深度学习模型的诊断准确率为0.76（=32/42），而放射科医生1和放射科医生2的诊断准确率分别为0.48（=20/42）和0.41（=17/42）。深度学习得出的诊断准确率明显高于两位神经放射科医生（分别P < 0.01）。结论：深度学习算法有望成为解释颈部淋巴结病的有用诊断支持工具。
Objectives: Cervical lymph nodes with internal cystic changes are seen with several pathologies, including papillary thyroid carcinoma (PTC), tuberculosis (TB), and HPV-positive oropharyngeal squamous cell carcinoma (HPV+OPSCC). Differentiating these lymph nodes is difficult in the absence of a known primary tumor or reliable medical history. In this study, we assessed the utility of deep learning in differentiating the pathologic lymph nodes of PTC, TB, and HPV+OPSCC on CT. Methods: A total of 173 lymph nodes (55 PTC, 58 TB, and 60 HPV+OPSCC) were selected based on pathology records and suspicious morphological features. These lymph nodes were divided into the training set (n = 131) and the test set (n = 42). In deep learning analysis, JPEG lymph node images were extracted from the CT slice that included the largest area of each node and fed into a deep learning training session to create a diagnostic model. Transfer learning was used with the deep learning model architecture of ResNet-101. Using the test set, the diagnostic performance of the deep learning model was compared against the histopathological diagnosis and to the diagnostic performances of two board-certified neuroradiologists. Results: Diagnostic accuracy of the deep learning model was 0.76 (=32/42), whereas those of Radiologist 1 and Radiologist 2 were 0.48 (=20/42) and 0.41 (=17/42), respectively. Deep learning derived diagnostic accuracy was significantly higher than both of the two neuroradiologists (P < 0.01, respectively). Conclusion: Deep learning algorithm holds promise to become a useful diagnostic support tool in interpreting cervical lymphadenopathy.