摘要
目的建立一种多参数乳腺MRI检查与诊断方式,与美国放射学院乳腺影像报告和数据系统(BI-RADS)分类对应,改进乳腺疾病的处理建议。资料与方法回顾分析278例乳腺疾病患者301个经病理证实的病灶,使用1 mm×1 mm×1 mm空间分辨率、120 s时间分辨率的动态增强扫描(DCE)序列和b=1000 s/mm2的扩散加权成像(DWI)序列进行扫描,将DCE显示早期病灶形态学恶性征象、时间-信号强度曲线(TIC)II型或III型、小于良恶性表观扩散系数(ADC)阈值的3个诊断因素各计1分,肿块和非肿块样强化病灶区别对待,积分≥2分诊断为BIRADS 5类,积分=1分诊断为BI-RADS 4类,积分<1分诊断为BI-RADS 3类,其他特异性良性发现诊断为BI-RADS 2类,DCE和DWI无异常发现评价为BI-RADS1类,并与病理学的良性(B)-高危(HR)-恶性(M)病灶分级进行对照,评价其对病灶处理的建议。结果以HR作为恶性时(M+HR),得到的ROC曲线下面积为0.860;以HR作为良性时(B+HR),得到的ROC曲线下面积为0.876,两者很接近。经过ROC曲线优化,在病理上将HR作为良性、在MRI上将BI-RADS 5类作为恶性,获得敏感度为85.3%,特异度为86.8%,准确度为85.1%,高于其他组合。如果将病理上HR病灶的处理原则定义为局部切除或短期随访,则BI-RADS 5类对M+HR病灶(可切除病灶)阳性预测值为93.2%;BI-RADS 4类病灶对M+HR病灶的阳性预测值为46.9%,必须活检以决定局部切除或短期随访;BI-RADS 3类及以下对B+HR病灶的阳性预测值(随访观察)为90.4%。结论本研究建立了一个简单的诊断模型,动态增强显示的形态学特征、动态时间-信号强度曲线和DWIADC值取相同的权重进行BI-RADS分类,可以很好地预测乳腺病灶良性、高危和恶性特征,对指导乳腺疾病的处理方式有实用价值。
PurposeTo investigate a multi-parametric protocol for breast MRI examination and lesions assessment correlated to the American College of Radiology （ACR） breast imaging reporting and data system （BI-RADS） categorization, and to improve the management of the breast lesions.Materials and Methods 301 pathologically confirmed lesions on 278 patients were retrospectively included. The scan protocol used a dynamic contrast enhancement sequence （DCE） of 1 mm×1 mm×1 mm spatial resolution, 120 temporal resolution and a diffusion weighted imaging （DWI） of b=1000 s/mm2. The malignant morphological features on the early-enhanced images, type II or III time intensity curve and the apparent diffusion coefficient （ADC） value less than benign/malignant threshold was equally weighted. Each was given 1 point when present malignant features and treated different on mass and non-mass-like enhancement lesions. When the sum of score was ≥2 points, the lesion was categorized as BI-RADS 5. When the sum of score was 1 point, the lesion was categorized as BI-RADS 4. When the sum of score was 〈1 point, the lesion was categorized as BI-RADS 3. The other specific benign findings were categorized as BI-RADS 2. No abnormality on DWI, DCE, T2WI and T1WI was categorized as BI-RADS 1. The final categories were correlated to the pathological grades as benign （B）, high risk （HR） and malignant （M）.Results When grouped HR as malignant （M＋HR）, the area under curve （AUC） of the ROC was 0.860. When grouped HR as benign （B＋HR）, the AUC of the ROC was 0.876, and the optimized sensitivity, specificity and accuracy was 85.3%, 86.8% and 85.1%, respectively, which were better than the other grouping. If the management of HR lesions could be lumptoectomy or short-term follow-up, the positive predictive value （PPV） of BI-RADS 5 for excisable lesions （M＋HR） was 93.2%, the PPV of BI-RADS 4 for excisable lesions （M＋HR） was 46.9% and the biopsy was essential. The PPV of BI-RADS 3 and below for follow-up lesions （B＋HR） was 90.4%.Conclusion A simple diagnosis algorithm was established, which equally weighted the DCE morphological feature, DCE-TIC and DWI-ADC. The diagnosis protocol was well consistent with BI-RADS categorization and could predict the benign, high risk and malignant lesions in pathology as well as the proper management.
出处
《中国医学影像学杂志》
CSCD
北大核心
2015年第3期176-182,共7页
Chinese Journal of Medical Imaging
关键词
乳腺疾病
磁共振成像
图像增强
扩散加权成像
表观扩散系数
乳腺影像报告和数据系统
病理学
外科
诊断
鉴别
Breast diseases
Magnetic resonance imaging
Image enhancement
Diffusion weighted imaging
Apparent diffusion coefficient
Breast imaging reporting anddata system
Pathology, surgical
Diagnosis, differential
