摘要
目的研究人工智能(CAD机器诊断系统)在乳腺癌超声判断的应用价值。方法纳入在我院进行乳腺穿刺活检或手术切除的乳腺病变患者5311例。以病理结果为对照,比较医师读图和CAD机器诊断系统以及二者联合应用对乳腺病变的良恶性诊断结果差异。结果(1)医师读图诊断结果的ROC曲线下面积为0.837,敏感度、特异度、准确度分别为91.65%、75.67%、81.98%。(2)CAD系统诊断结果的ROC曲线下面积为0.880,敏感度、特异度、准确度分别为87.41%、88.64%、88.16%。(3)分析医师读图和CAD系统判读错误的病例,对部分BI-RADS分类进行调级,调级后联合判断乳腺病变的良恶性,ROC曲线下面积为0.957,敏感度、特异度、准确度分别为96.91%、88.41%、91.56%。(4)三者ROC曲线比较差异有统计学意义(均P<0.05);医师读图和联合判断比较,特异度、准确度差异有统计学意义(P<0.05);CAD系统和联合判断比较,敏感度差异有统计学意义(P<0.05);CAD系统和医师读图比较,特异度差异有统计学意义(P<0.05)。结论相比较医师读图,CAD机器诊断系统以及优化BI-RADS分类后联合诊断对乳腺癌的良恶性判断效果更好。
Objective To study the application value of CAD machine diagnosis system in ultrasound diagnosis of breast cancer.Methods A total of 5311 patients with breast lesions who underwent breast biopsy or surgical resections in our hospital were enrolled in the study.With the pathological results as the standards,the diagnosis results by physicians’image reading were compared with the results by CAD machine diagnosis system as well as the results by combined application of the two approaches.Results(1)The area under the ROC curve of the diagnosis results of the doctor’s image reading was 0.837,and the sensitivity,specificity,and accuracy were 91.65%,75.67%,and 81.98%,respectively.(2)The area under the ROC curve of the CAD system diagnosis results was 0.880,and the sensitivity,specificity,and accuracy are 87.41%,88.64%,and 88.16%.(3)The misdiagnosed cases by the physicians’image reading and the CAD system were analyzed to adjust the level of some BI-RADS classifications.After the adjustment,the area under the ROC curve with the combined application of the two approaches reached 0.957,and the sensitivity,specificity,and accuracy were 96.91%,88.41%,and 91.56%,respectively.(4)There were significant differences in the ROC curves between the three methods(all P<0.05).The physicians’image reading was significantly different in the specificity and accuracy from the combined application of the two methods(P<0.05).The CAD system was significantly in the sensitivity from the combined application of the two methods(P<0.05).The CAD system was significantly in the specificity from the combined application of the two methods(P<0.05).Conclusion Compared with the physicians’image reading,the CAD machine diagnosis system alone and the combined application of the two methods after optimizing BI-RADS classification are both better at the diagnosis of benign and malignant breast cancer.
作者
杨磊
唐灿
YANG Lei;TANG Can(Shuangliu Hospital of Traditional Chinese Medicine,Chengdu 610000,China;不详)
出处
《实用医学杂志》
CAS
北大核心
2022年第1期106-110,共5页
The Journal of Practical Medicine
关键词
人工智能
乳腺病变
良恶性
BI-RADS分类
CAD
artificial intelligence
breast lesions
benign and malignant
BI-RADS classification
CAD
