基于深度学习技术的乳腺智能检测系统在触诊阴性乳腺肿瘤诊断中的应用

目的 拟探讨基于深度学习技术的乳腺X线智能检测系统在临床触诊阴性乳腺肿瘤诊断中的应用价值.方法 回顾性收集2014年1月至2016年12月期间就诊于陕西省肿瘤医院的临床触诊阴性乳腺肿瘤患者322例,均手术治疗且临床病理资料齐全.使用MammoWorks?乳腺智能检测系统对所有入组患者乳腺X线图片进行分析,以术后病理结果...

Application of breast intelligent detection system based on deep learning technology in palpation negative breast tumor diagnosis

Objective To investigate the application value of breast X-ray intelligent detection system based on deep learning technology in clinical palpation negative breast tumor diagnosis.Methods A total of 322 patients with palpation negative breast cancer in Shaanxi cancer hospital from January 2014 to December 2016 were retrospectively collected.MammoWorksTM breast intelligent detection system was used to analyze the mammograms of all patients.The sensitivity,positive predictive value,the number of false positive markers in each image and the influence of clinicopathological characteristics on the detection efficiency of MammoWorksTM breast intelligent detection system before and after the algorithm update were analyzed,and the effects of different versions of MammoWorksTM breast intelligent detection system were compared.Results The diagnostic sensitivity of different versions of MammoWorksTM(previous version:3.5.2.6;updated version:3.5.4.43)in clinical palpation negative breast tumors were 78.57%(253/322)and 95.65%(308/322),respectively.The positive predictive value(calculated by the number of markers)were 55.32%(801/1448)and 44.27%(1120/2530),respectively.The number of false-positive markers in each image were 0.50(0.25,0.75)and 1.50(0.25,0.75),1.00(0.75,1.50),respectively.After deep learning,the algorithm was updated,the detection sensitivity increased(χ^2=35.926,P<0.001),the positive predictive value decreased(χ^2=45.02,P<0.001),and the false-positive markers in each image increased compared with the previous version(Z=-14.105,P<0.001).The consistency of the two versions was poor(kappa=0.234).In different breast density and lesion type groups,the sensitivity of the early version of the system was significantly different(χ^2=12.198,P<0.05;χ^2=16.235,P<0.05).After the system update,the sensitivity difference in different clinicopathological characteristics was eliminated,and there was no significant difference between the groups(P>0.05).Conclusion The detection sensitivity of MammoWorksTM system is higher than before through algorithm updating,which has certain application value in clinical palpation negative breast cancer diagnosis.However,due to more false-positive markers in each image,it still needs to be further optimized to improve the detection efficiency.