摘要
目的:探讨应变弹性成像(strain elastography,SE)和彩色多普勒技术(color Doppler flow image,CDFI)对二维超声(two-dimensional ultrasound,2D US)诊断的乳腺非肿块型病变(non-mass-like lesions,NML)的诊断价值,提高乳腺非肿块型病变的检出率。方法:回顾性分析102例经2D US、CDFI及SE诊断、并经病理学证实的乳腺非肿块型病变,计算2D US、CDFI及SE的诊断效能,来区分乳腺非肿块型病变的良恶性。结果:102例中,良性46个,恶性56个。2D US诊断敏感度75.00%、特异度56.67%、准确率66.7%、AUC 0.662;SE诊断敏感度86.11%、特异度83.33%、准确率84.85%、AUC 0.902;CDFI诊断敏感度80.56%、特异度66.67%、准确率74.24%、AUC 0.828;2D US+SE+CDFI诊断敏感度91.67%、特异度90.00%、准确率90.91%、AUC 0.974。结论:2D US联合SE和CDFI可以提高乳腺非肿块型病变的诊断价值,有助于预测非肿块型乳腺恶性病变。
Objective:To investigate the diagnostic value of strain elastography(SE)and color Doppler Flow Image(CDFI)for non-mass-like lesions(NML)diagnosed by two-dimensional ultrasound(2D US).The diagnostic value of two-dimensional ultrasound(2D US)in the diagnosis of non-mass-like lesions(NML)in the breast,and to improve the detection rate of non-mass-like lesions in the breast.Methods:102 cases of non-mass-like lesions diagnosed by 2D US,CDFI and SE and confirmed by pathology were retrospectively analyzed,and the diagnostic efficacies of 2D US,CDFI and SE were calculated to differentiate the benignness and malignancy of non-mass-like lesions.Results:Among the 102 cases,46 were benign and 56 were malignant.2D US had a diagnostic sensitivity of 75.00%,a specificity of 56.67%,an accuracy of 66.7%,and an AUC of 0.662.SE had a diagnostic sensitivity of 86.11%,a specificity of 83.33%,an accuracy of 84.85%,and an AUC of 0.902.CDFI had a diagnostic sensitivity of 80.56%,a specificity of 66.67%,an accuracy of 74.24%,and an AUC of 0.828.2D US+SE+CDFI diagnostic had a sensitivity of 91.67%,a specificity of 90.00%,an accuracy of 90.91%,and an AUC of 0.974.Conclusion:2D US in combination with SE and CDFI can increase the diagnostic value of non-mass-type lesions of the breast,and can help to predict non-mass-type breast malignant lesions.
作者
李琳
孙俊旗
肖雨雄
单菲菲
LI Lin;SUN Jun-qi;XIAO Yu-xiong;SHAN Fei-fei(Department of Ultrasound Diagnosis,Yuebei People's Hospital Affiliated to Shantou University Medical College,Guangdong 512000,China;Department of Imaging Diagnosis,Yuebei People's Hospital Affiliated to Shantou University Medical College,Guangdong 512000,China)
出处
《影像技术》
CAS
2024年第3期70-75,共6页
Image Technology
基金
2023年度韶关市社会发展科技协同创新体系建设项目(支持科研工作者项目)(230331008037084)
韶关市卫生健康科研项目(Y23028)。
