基于乳腺X线摄影的深度学习技术鉴别乳腺影像报告和数据系统3类与4类疾病的价值  

The value of deep learning technology based on mammography in differentiating breast imaging reporting and data system category 3 and 4 lesions

作　　者：欧阳汝珊 李霖 林小慧 赖小慧 李增艳 马捷 Ouyang Rushan;Li Lin;Lin Xiaohui;Lai Xiaohui;Li Zengyan;Ma Jie(the Second Clinical Medical College of Jinan University,Shenzhen 518020,China;Department of Radiology,Shenzhen People′s Hospital,the Second Clinical Medical College,Jinan University,Shenzhen 518020,China;Department of Radiology,Luohu People′s Hospital,Shenzhen 518001,China)

机构地区：[1]暨南大学第二临床医学院,深圳518020 [2]深圳市人民医院放射科、暨南大学第二临床医学院,深圳518020 [3]深圳市罗湖区人民医院放射科,深圳518001

出　　处：《中华放射学杂志》2023年第2期166-172,共7页Chinese Journal of Radiology

基　　金：深圳市科技研发资金(GJHZ20210705142208024)。

摘　　要：目的探讨基于乳腺X线摄影的深度学习技术鉴别乳腺影像报告和数据系统(BI-RADS)3类与4类疾病的价值。方法回顾性分析2020年1至12月在深圳市人民医院及深圳市罗湖区人民医院乳腺X线摄影评估为BI-RADS 3类及4类305例患者的临床及影像资料。305例患者共314个病灶, 均为女性, 年龄21~83(47±12)岁。按1∶1比例交叉、简单随机分配给2名工作经验分别为5年及6年普通影像诊断医师(普通医师A、普通医师B)和2名工作经验均为21年且经过专业乳腺影像培训的乳腺影像诊断医师(专业医师A、专业医师B)单独阅片, 之后分别结合深度学习系统再次阅片, 最终将乳腺病变重新分为BI-RADS 3类或4类。采用受试者操作特征曲线及曲线下面积(AUC)评价诊断效能, 以DeLong法比较AUC的差异。结果普通医师A结合深度学习系统重新分类BI-RADS 3类与4类乳腺病灶的AUC较普通医师A单独诊断明显提高(AUC分别为0.79、0.63, Z=2.82、P=0.005);普通医师B结合深度学习系统重新分类BI-RADS 3类与4类乳腺病灶的AUC较普通医师B明显提高(AUC分别为0.83、0.64, Z=3.32、P=0.001)。专业医师A结合深度学习系统与专业医师A、专业医师B结合深度学习系统与专业医师B重新分类BI-RADS 3类与4类乳腺病灶的AUC差异均无统计意义(P>0.05)。结论基于乳腺X线摄影的深度学习系统辅助普通医师鉴别BI-RADS 3类与4类疾病的效能更显著。Objective To explore the value of deep learning technology based on mammography in differentiating for breast imaging reporting and data system(BI-RADS)category 3 and 4 lesions.Methods The clinical and imaging data of 305 patients with 314 lesions assessed as BI-RADS category 3 and 4 by mammography were analyzed retrospectively in Shenzhen People′s Hospital and Shenzhen Luohu People′s Hospital from January to December 2020.All 305 patients were female,aged 21 to 83(47±12)years.Two general radiologists(general radiologist A and general radiologist B)with 5 and 6 years of work experience and two professional breast imaging diagnostic radiologists(professional radiologist A and professional radiologist B)with 21 years of work experience and specialized breast imaging training were randomly assigned to read the imaging independently at a 1∶1 ratio,and then to read the imaging again in combination with the deep learning system.Finally,breast lesions were reclassified into BI-RADS category 3 or 4.The receiver operating characteristic curve and area under the curve(AUC)were used to evaluate the diagnostic performance,and the differences of AUCs were compared by DeLong method.Results The AUC of general radiologist A combined with deep learning system to reclassify BI-RADS category 3 and 4 breast lesions was significantly higher than that of general radiologist A alone(AUC=0.79,0.63,Z=2.82,P=0.005,respectively).The AUC of general radiologist B combined with deep learning system to reclassify BI-RADS category 3 and 4 breast lesions was significantly higher than that of general radiologist B(AUC=0.83,0.64,Z=3.32,P=0.001,respectively).There was no significant difference in the AUCs between professional radiologist A combined with deep learning system and professional radiologist A,and professional radiologist B combined with deep learning system and professional radiologist B in reclassifying BI-RADS category 3 and 4 breast lesions(P>0.05).Conclusion The deep learning system based on mammography is more effective in a

关 键 词：乳腺肿瘤 乳房X线摄影术 人工智能 深度学习 

分 类 号：R737.9[医药卫生—肿瘤]