甲状腺结节超声风险分层方案依赖于高风险超声特征的存在。然而，一些恶性甲状腺结节在甲状腺超声上具有良性外观。需要新的甲状腺结节风险评估方法。我们研究了多基因风险评分（PRS），结合超声分析以改进甲状腺结节风险评估。卷积神经网络分类器在621个甲状腺结节的超声静态图像和连续图像上进行了训练。基因表型关联研究（PheWAS）和PRS PheWAS被用来优化鉴别良性和恶性结节的PRS。在科罗拉多个性化医学生物库中的73,346名参与者中评估了PRS。当将深度学习模型输出与甲状腺癌PRS和遗传血统估计相结合时，良性与恶性甲状腺结节分类器的受试者工作特征曲线下面积（AUROC）从0.83提高到0.89（DeLong，p值=0.007）。联合深度学习和遗传分类器实现了0.95的临床相关敏感性，95% CI [0.88-0.99]，0.63的特异性[0.55-0.70]，以及0.47 [0.41-0.58]和0.97 [0.92-0.99]的阳性和阴性预测值。欧洲血统分析中的AUROC改进保持一致（深度学习和PRS分类器的AUROC分别为0.83和0.87）。升高的PRS与甲状腺癌结构性疾病复发风险增加有关（有序logistic回归，p值=0.002）。用PRS增强超声风险评估可以提高诊断准确性。© 作者们2023年版。牛津大学出版社代表内分泌学会发表。保留所有权利。如需授权，请发送电子邮件至journals.permissions@oup.com。

Thyroid nodule ultrasound-based risk stratification schemas rely on the presence of high-risk sonographic features. However, some malignant thyroid nodules have benign appearance on thyroid ultrasound. New methods for thyroid nodule risk assessment are needed.We investigated polygenic risk score (PRS) accounting for inherited thyroid cancer risk combined with ultrasound-based analysis for improved thyroid nodule risk assessment.The convolutional neural network classifier was trained on thyroid ultrasound still images and cine clips from 621 thyroid nodules. Phenome-wide association study (PheWAS) and PRS PheWAS were used to optimize PRS for distinguishing benign and malignant nodules. PRS was evaluated in 73,346 participants in the Colorado Center for Personalized Medicine Biobank.When the deep learning model output was combined with thyroid cancer PRS and genetic ancestry estimates, the area under the receiver operating characteristic curve (AUROC) of the benign vs. malignant thyroid nodule classifier increased from 0.83 to 0.89 (DeLong, p-value = 0.007). The combined deep learning and genetic classifier achieved a clinically relevant sensitivity of 0.95, 95% CI [0.88-0.99], specificity of 0.63 [0.55-0.70], and positive and negative predictive values of 0.47 [0.41-0.58] and 0.97 [0.92-0.99], respectively. AUROC improvement was consistent in European ancestry-stratified analysis (0.83 and 0.87 for deep learning and deep learning combined with PRS classifiers, respectively). Elevated PRS was associated with a greater risk of thyroid cancer structural disease recurrence (ordinal logistic regression, p-value = 0.002).Augmenting ultrasound-based risk assessment with PRS improves diagnostic accuracy.© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.