利用深度卷积神经网络对甲状腺肿瘤进行组织病理学多分类：一项大规模初步研究。
Using deep convolutional neural networks for multi-classification of thyroid tumor by histopathology: a large-scale pilot study.
作者信息
Wang Yunjun, Guan Qing, Lao Iweng, Wang Li, Wu Yi, Li Duanshu, Ji Qinghai, Wang Yu, Zhu Yongxue, Lu Hongtao, Xiang Jun
机构信息
Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
出版信息
Ann Transl Med. 2019 Sep;7(18):468. doi: 10.21037/atm.2019.08.54.
BACKGROUND
To explore whether deep convolutional neural networks (DCNNs) have the potential to improve diagnostic efficiency and increase the level of interobserver agreement in the classification of thyroid nodules in histopathological slides.
METHODS
A total of 11,715 fragmented images from 806 patients' original histological images were divided into a training dataset and a test dataset. Inception-ResNet-v2 and VGG-19 were trained using the training dataset and tested using the test dataset to determine the diagnostic efficiencies of different histologic types of thyroid nodules, including normal tissue, adenoma, nodular goiter, papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), medullary thyroid carcinoma (MTC) and anaplastic thyroid carcinoma (ATC). Misdiagnoses were further analyzed.
RESULTS
The total 11,715 fragmented images were divided into a training dataset and a test dataset for each pathology type at a ratio of 5:1. Using the test set, VGG-19 yielded a better average diagnostic accuracy than did Inception-ResNet-v2 (97.34% 94.42%, respectively). The VGG-19 model applied to 7 pathology types showed a fragmentation accuracy of 88.33% for normal tissue, 98.57% for ATC, 98.89% for FTC, 100% for MTC, 97.77% for PTC, 100% for nodular goiter and 92.44% for adenoma. It achieved excellent diagnostic efficiencies for all the malignant types. Normal tissue and adenoma were the most challenging histological types to classify.
CONCLUSIONS
The DCNN models, especially VGG-19, achieved satisfactory accuracies on the task of differentiating thyroid tumors by histopathology. Analysis of the misdiagnosed cases revealed that normal tissue and adenoma were the most challenging histological types for the DCNN to differentiate, while all the malignant classifications achieved excellent diagnostic efficiencies. The results indicate that DCNN models may have potential for facilitating histopathologic thyroid disease diagnosis.
背景
探讨深度卷积神经网络（DCNN）是否有潜力提高甲状腺结节组织病理学切片分类的诊断效率并提升观察者间的一致性水平。
方法
从806例患者的原始组织学图像中获取的总共11715张碎片图像被分为训练数据集和测试数据集。使用训练数据集对Inception-ResNet-v2和VGG-19进行训练，并使用测试数据集进行测试，以确定不同组织学类型甲状腺结节的诊断效率，包括正常组织、腺瘤、结节性甲状腺肿、乳头状甲状腺癌（PTC）、滤泡状甲状腺癌（FTC）、髓样甲状腺癌（MTC）和未分化甲状腺癌（ATC）。对误诊情况进行进一步分析。
结果
总共11715张碎片图像按5:1的比例为每种病理类型分为训练数据集和测试数据集。使用测试集时，VGG-19的平均诊断准确率比Inception-ResNet-v2更高（分别为97.34%和94.42%）。应用于7种病理类型的VGG-19模型显示，正常组织的碎片准确率为88.33%，ATC为98.57%，FTC为98.89%，MTC为100%，PTC为97.77%，结节性甲状腺肿为100%，腺瘤为92.44%。它对所有恶性类型都实现了出色的诊断效率。正常组织和腺瘤是最难分类的组织学类型。
结论
DCNN模型，尤其是VGG-19，在通过组织病理学区分甲状腺肿瘤的任务上取得了令人满意的准确率。对误诊病例的分析表明，正常组织和腺瘤是DCNN最难区分的组织学类型，而所有恶性分类都实现了出色的诊断效率。结果表明DCNN模型可能具有促进甲状腺疾病组织病理学诊断的潜力。
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