背景:甲状腺癌是一种常见的甲状腺恶性肿瘤。大多数甲状腺病变需要术中冰冻病理诊断,为精准操作提供了重要信息。随着数字全幻灯片图像(WSI)的发展,深度学习方法对甲状腺组织病理学分类(石蜡切片)取得了突出的效果。我们目前的研究是明确深度学习是否有助于术中冰冻甲状腺病变的病理诊断。
方法:我们提出了一种用于冻结甲状腺病变的人工智能辅助诊断系统,该系统应用先验知识,同时对病变是否癌变进行二分判断,并对病变类型进行二次判断。癌性病变将冻结的甲状腺病变分为五类:乳头状甲状腺癌,甲状腺髓样癌,间变性甲状腺癌,滤泡性甲状腺肿瘤,和非癌性病变。我们从中山大学附属第一医院(SYSUFH)获得了4409个冰冻的甲状腺数字病理切片(WSI)来训练和测试模型,性能通过六倍交叉验证进行了验证,101个甲状腺乳头状微小癌切片用于验证系统的敏感性,甲状腺的1388个WSI用于评估外部数据集.从准确性、F1得分,召回,精度和AUC(曲线下面积)。
结果:我们开发了第一个基于深度学习的冷冻甲状腺诊断分类器,用于乳头状癌的组织病理学WSI分类,髓样癌,滤泡性肿瘤,间变性癌,和非癌性病变。在测试幻灯片上,该系统的准确度为0.9459,精密度为0.9475,AUC为0.9955.在乳头状癌测试载玻片中,该系统甚至能够准确预测直径小至2mm的病变.用加速部件测试,剪切处理可以在346.12s内进行,视觉推理预测结果可以在98.61s内得到,从而满足术中诊断的时间要求。我们的研究采用深度学习方法对术中冰冻甲状腺病变的临床分布进行高精度分类,这对协助病理学家和甲状腺病变的精准手术具有潜在的临床意义。
BACKGROUND: Thyroid cancer is a common thyroid malignancy. The majority of thyroid lesion needs intraoperative frozen pathology diagnosis, which provides important information for precision operation. As digital whole slide images (WSIs) develop, deep learning methods for histopathological classification of the thyroid gland (paraffin sections) have achieved outstanding results. Our current study is to clarify whether deep learning assists pathology diagnosis for intraoperative frozen thyroid lesions or not.
METHODS: We propose an artificial intelligence-assisted diagnostic system for frozen thyroid lesions that applies prior knowledge in tandem with a dichotomous judgment of whether the lesion is cancerous or not and a quadratic judgment of the type of cancerous lesion to categorize the frozen thyroid lesions into five categories: papillary thyroid carcinoma, medullary thyroid carcinoma, anaplastic thyroid carcinoma, follicular thyroid tumor, and non-cancerous lesion. We obtained 4409 frozen digital pathology sections (WSI) of thyroid from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) to train and test the model, and the performance was validated by a six-fold cross validation, 101 papillary microcarcinoma sections of thyroid were used to validate the system\'s sensitivity, and 1388 WSIs of thyroid were used for the evaluation of the external dataset. The deep learning models were compared in terms of several metrics such as accuracy, F1 score, recall, precision and AUC (Area Under Curve).
RESULTS: We developed the first deep learning-based frozen thyroid diagnostic classifier for histopathological WSI classification of papillary carcinoma, medullary carcinoma, follicular tumor, anaplastic carcinoma, and non-carcinoma lesion. On test slides, the system had an accuracy of 0.9459, a precision of 0.9475, and an AUC of 0.9955. In the papillary carcinoma test slides, the system was able to accurately predict even lesions as small as 2 mm in diameter. Tested with the acceleration component, the cut processing can be performed in 346.12 s and the visual inference prediction results can be obtained in 98.61 s, thus meeting the time requirements for intraoperative diagnosis. Our study employs a deep learning approach for high-precision classification of intraoperative frozen thyroid lesion distribution in the clinical setting, which has potential clinical implications for assisting pathologists and precision surgery of thyroid lesions.