宫洁,陈琴,罗俊,唐丽娜,杨丽春,吕志华,程印蓉,袁丽君,程艳,王胜利,韩小容.乳腺CEUS预测模型诊断乳腺恶性病灶的观察者间一致性:多中心研究[J].中国医学影像技术,2018,34(6):874~878
乳腺CEUS预测模型诊断乳腺恶性病灶的观察者间一致性:多中心研究
Consistency of different physicians in diagnosis of malignant breast lesions with breast CEUS predictive model: A multicenter study
投稿时间:2017-10-30  修订日期:2018-03-20
DOI:10.13929/j.1003-3289.201710142
中文关键词:  超声检查  乳腺肿瘤  诊断,鉴别
英文关键词:Ultrasonography  Breast neoplasms  Diagnosis, differential
基金项目:
作者单位E-mail
宫洁 电子科技大学医学院, 四川 成都 610054  
陈琴 四川省医学科学院 四川省人民医院超声科, 四川成都 610072 17186868103@qq.com 
罗俊 四川省医学科学院 四川省人民医院超声科, 四川成都 610072  
唐丽娜 福建省肿瘤医院 福建医科大学附属肿瘤医院超声科, 福建 福州 350014  
杨丽春 云南省肿瘤医院超声科, 云南昆明 650118  
吕志华 黄石市中心医院超声科, 湖北 黄石 435000  
程印蓉 成都市第一人民医院超声科, 四川 成都 610094  
袁丽君 空军军医大学唐都医院超声科, 陕西 西安 710038  
程艳 曲靖市第一人民医院超声科, 云南 曲靖 655099  
王胜利 延安大学附属医院超声科, 陕西延安 716099  
韩小容 重庆市第九人民医院超声科, 重庆 400799  
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中文摘要:
      目的 探讨拉伸指数模型DWI鉴别诊断乳腺良恶性病变的价值。方法 收集58例乳腺病变患者,共63个病灶(良性33个,恶性30个),行多b值DWI及动态增强MRI (DCE-MRI)扫描。计算ADC、扩散分布指数(DDC)和扩散异质性指数(α)值,并生成时间-信号强度曲线(TIC)。比较良恶性病变间各参数差异,采用ROC曲线评价各参数诊断效能。结果 恶性病变ADC、DDC和α分别为(1.01±0.19)×10-3 mm2/s、(0.89±0.23)×10-3 mm2/s和0.75±0.09,良性病变分别为(1.41±0.27)×10-3 mm2/s、(1.49±0.29)×10-3 mm2/s和0.87±0.07,恶性病变均低于良性病变(P均<0.01)。各参数中DDC曲线下面积(AUC)最大(AUC=0.958),最佳诊断界值1.22×10-3 mm2/s,敏感度和特异度分别为96.67%、81.82%,DDC与TIC联合所得AUC为0.976,对应敏感度和特异度分别为93.33%、93.94%。结论 拉伸指数模型DWI参数DDC、α能够鉴别诊断乳腺良恶性病变,DDC与TIC联合的诊断效能高于ADC和DCE。
英文摘要:
      Objective To explore the consistency of different physicians in diagnosis of malignant breast lesions with breast CEUS predictive model. Methods Totally 953 patients with solitary breast nodule from multicenter who underwent ultrasound and CEUS were collected. The research team was composed by the initial group (one junior physician from each hospital), check group (one or two physicians who had at least two-year experience of CEUS examination from each hospital), research group (two senior physicians from Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital) and cross-blinded group (one or two vice directors or chief physicians from each hospital). At first, the lesions were classified according to the breast imaging reporting and data system (BI-RADS) by the initial group and the check group, then new BI-RADS classifications were performed by research group and cross-blind group with breast CEUS predictive model. The consistency of different physicians in diagnosis of malignant breast lesions was analyzed. Results Among 953 patients, benign lesions were found in 451 patients (451/953, 47.32%), malignant lesions were found in 435 patients (435/953, 45.65%), and precancerous lesions were found in 67 patients (67/953, 7.03%). The accuracy of the initial group, check group, research group and cross-blinded group was 71.67%(683/953), 74.92%(714/953), 80.17%(764/953) and 83.42%(795/953), respectively. The consistency of different physicians for diagnosis of malignant breast lesions between initial group and check group was good (Kappa=0.82, P<0.001), while between initial group and cross-blinded group, initial group and research group were both moderate (Kappa=0.56, 0.41; all P<0.001). The consistency of different physicians for diagnosis of malignant breast lesions between check group and cross-blinded group, between check group and research group were both moderate(Kappa=0.68, 0.51; all P<0.001). The consistency between research group and cross-blinded group with breast CEUS predictive model was moderate (Kappa=0.74, P<0.001). Conclusion The consistency of different physicians in diagnosis of malignant breast lesions with breast CEUS predictive model was moderate.
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