甲状腺癌是最常见的恶性内分泌肿瘤。评估术前恶性风险的关键测试是细针穿刺活检（FNABs）的细胞学评估。评估结果往往会是不确定的，导致对良性术后诊断进行不必要的手术。我们开发了一种深度学习算法，用于分析甲状腺FNAB全切片图像（WSIs）。我们展示了在最大的报道数据集中，临床级性能，在检测定性病例上的应用和作为辅助测试明确非确定性病例的指示。该算法筛查并确定性地将45.1%（130/288）的WSIs归类为良性或恶性，其恶性风险率分别为2.7%和94.7%。它通过将21.3%（N = 23）的不确定性病例重新分类为良性，从而减少了不确定性病例（N = 108），其恶性风险率为1.8%。使用一个整个日历年收集的连续FNAB数据集复制了类似的结果，实现了对甲状腺FNAB分类的临床可接受的误差范围。版权所有（Copyright © 2023）美国病理学调查协会。由爱思唯尔公司出版。保留所有权利。

Thyroid cancer is the most common malignant endocrine tumor. The key test to assess preoperative risk of malignancy is cytologic evaluation of fine-needle aspiration biopsies (FNABs). The evaluation findings can often be indeterminate, leading to unnecessary surgery for benign post-surgical diagnoses. We have developed a deep-learning algorithm to analyze thyroid FNAB whole-slide images (WSIs). We show, on the largest reported data set of thyroid FNAB WSIs, clinical-grade performance in the screening of determinate cases and indications for its use as an ancillary test to disambiguate indeterminate cases. The algorithm screened and definitively classified 45.1% (130/288) of the WSIs as either benign or malignant with risk of malignancy rates of 2.7% and 94.7%, respectively. It reduced the number of indeterminate cases (N = 108) by reclassifying 21.3% (N = 23) as benign with a resultant risk of malignancy rate of 1.8%. Similar results were reproduced using a data set of consecutive FNABs collected during an entire calendar year, achieving clinically acceptable margins of error for thyroid FNAB classification.Copyright © 2023 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.