目的:探讨基于二维超声图像的纹理分析对桥本甲状腺炎背景下甲状腺结节良、恶性的鉴别诊断价值.方法:回顾性分析2018年2月-8月在本院经病理证实的合并桥本甲状腺炎的甲状腺结节的二维超声图像.根据病理结果将甲状腺结节分为良性组和恶性组.采用ITK-SNAPE软件在甲状腺结节的二维超声图像上手工勾画兴趣区,通过python的Pyradiomics包提取纹理特征,采用独立样本t检验或Mann-Whitney U检验、Lasso回归对特征进行筛选和建模,采用受试者操作特征(ROC)曲线评估模型诊断效能.结果:共纳入合并桥本甲状腺炎的甲状腺结节93例(良性组45例,恶性组48例).筛选出3个纹理特征.构建的模型鉴别合并桥本甲状腺炎的甲状腺结节的良、恶性的ROC曲线下面积为0.842(95％CI:0.764～0.920),敏感度为0.791(95％CI:0.667～0.896),特异度为0.778(95％CI:0.667～0.889),符合率为0.785(95％CI:0.688～0.863).模型的诊断效能高于单一纹理特征的诊断效能.结论:基于二维超声图像的纹理分析可用于桥本甲状腺炎背景下甲状腺结节的良、恶性的鉴别诊断.

Objective To evaluate the assistant diagnostic value of S‐Detect artificial intelligence system in differential diagnosis of benign and malignant breast tumors . Methods Clinical data and ultrasound images of 201 patients undergoing breast ultrasound examination in Tongji Hospital from M arch 2018 to M ay 2018 were acquired . Two‐dimensional grayscale and color Doppler ultrasound images ,S‐Detect mode images and elastographic images of 220 breast lesions were analyzed . T he BI‐RADS categories of each lesion were divided into two groups :experienced group and random group .And according to w hether to refer to S‐Detect diagnostic results ,the BI‐RADS categories in experienced group were divided into A 1 group and P1 group .In additional ,the highest and lowest categories of the same tumor in random group were A 2 group ,and the diagnostic results of A 2 group combining with S‐Detect system were belonged to P2 group . T he ROC curves were plotted and the area under the curve ,sensitivity ,specificity or the accuracy of the different groups were compared . Agreements of diagnostic results between different groups were analyzed by Kappa test . Results Out of 220 breast lesions ,181 lesions were benign and 39 lesions were malignant . The S‐Detect artificial intelligence system had a relatively high diagnostic efficiency ,and the sensitivity , specificity and accuracy of S‐Detect classification were 92 .3% ,90 .6% ,90 .9% , respectively . With its assistance ,the specificity and accuracy in the experienced group had an increasing trend ( A 1 group :86 .7% , 88 .6% ; P1 group :91 .2% ,92 .3% ) ,and the diagnostic accuracy in random group was significantly improved ( A2 group :63 .6% －85 .5% ; P2 group :93 .2% －94 .1% ) . Both S‐Detect system and elasticity score helped to improve the efficacy of ultrasound physicians in differential diagnosis of benign and malignant breast lesions . But there were differences in diagnostic performance and assistant diagnostic ability between the two techniques . Conclusions S‐Detect technique contributes to the augment of diagnostic accuracy of ultrasound doctors in identifying breast cancer , improves the quality of random breast ultrasound examinations ,and reduces missed diagnosis and misdiagnosis of breast examinations .