摘要目的:探索基于颜色矩的甲状腺冷冻切片判读系统并评估该系统在实际甲状腺癌冷冻切片病理辅助诊断中的应用价值。方法:从浙江省台州市中心医院（台州学院附属医院）收集2018年6月至2020年1月共550张甲状腺冷冻病理切片（含恶性和非恶性切片），将550张数字甲状腺冷冻切片分为训练集（190张）、验证集（48张）、测试集A（60张）和测试集B（252张）。由病理医师标注训练集和验证集中恶性肿瘤病理切片的恶性肿瘤区域，使用标注信息分别训练基于投票法的甲状腺冷冻切片判读模型和基于颜色矩的甲状腺冷冻切片判读模型，然后分别使用测试集A和测试集B对两种病理切片判读模型的性能进行评价。结果:基于投票法的甲状腺切片判读模型在测试集A、B的分类准确率分别为90.0%和83.7%，而基于颜色矩的病理切片判读模型在测试集A、B上的准确率为91.6%和90.9%，表示本研究提出的模型在甲状腺癌冷冻切片病理辅助诊断工作应用中具有更高的准确率和稳定性。结论:基于颜色矩的病理切片判读模型在甲状腺癌冷冻切片病理辅助诊断工作中比其他的数字病理切片分类模型更有应用价值。

abstractsObjective:To develop a color-moment based model for frozen-section diagnosis of thyroid lesions, and to evaluate the model′s value in the frozen-section diagnosis of thyroid cancer.Methods:In this study, 550 frozen thyroid pathological slides, including malignant and non-malignant cases, were collected from Taizhou Central Hospital (Taizhou University Hospital), China, between June 2018 and January 2020. The 550 digitalized frozen-section slides of thyroid were divided into training set (190 slides), validation set (48 slides), test set A (60 slides) and test set B (252 slides). The tumor regions on the slides of malignant cases in the training and validation sets were labeled by pathologists. The labeling information was then used to train the thyroid frozen-section diagnosis models based on the voting method and those based on the color moment. Finally, the performance of two pathological slide diagnosis models was evaluated using the test set A and test set B, respectively.Result:The classification accuracy of the thyroid frozen-section diagnosis model based on the voting method was 90.0% and 83.7%, using test sets A and B, respectively, while that based on color moments was 91.6% and 90.9%, respectively. For actual frozen-section diagnosis of thyroid cancer, the model developed in this study had higher accuracy and stability.Conclusion:This study proposes a color-moment based frozen-section diagnosis model, which is more accurate than other classification models for frozen-section diagnoses of thyroid cancer.