Zhang Bing, Guo Zhuanzhuan, Chen Xin, Zhang Yuelang, Yang Quanxin, Tan Lingjie, Liang Wenbin, Lei Zhe, Liu Le, Guo Baobin, Zhou Xiaoqian, Qu Kai
Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Department of Radiology, The Affiliated Hospital of Northwest University Xi'an No.3 Hospital, Xi'an, China.
Br J Radiol. 2025 Jul 1;98(1171):1080-1089. doi: 10.1093/bjr/tqaf084.
To investigate the diagnostic performance of the predefined breast apparent diffusion coefficient (ADC-B) category system in differentiating malignant from benign breast lesions and to compare with the Breast Imaging Reporting and Data System (BI-RADS).
This was a single-institution retrospective study of patients who underwent breast MRI between April 2019 and May 2023. Dynamic contrast-enhanced (DCE) MRI and diffusion weighted imaging (DWI) were performed using a 3-T MRI system. Data on lesion morphology (mass, non-mass), size, and ADC were collected. Histology was the standard of reference. The analysis assessed the inter-reader agreement in measuring ADC and ADC-B category using the intraclass correlation coefficient (ICC), as well as the diagnostic performance based on the receiver operating characteristic (ROC) curve.
A total of 376 lesions in 358 women (mean age, 46.29 years; SD, 11.03) with pathologic results (236 malignant and 140 benign) were included. The inter-reader agreement was excellent in measuring ADC (ICC = 0.991) and assessing ADC-B category (ICC = 0.967). Overall diagnostic performance for ADC-B category (area under the curve [AUC], 0.858; 95% CI: 0.816-0.894) was higher than for BI-RADS (AUC, 0.805; 95% CI: 0.759-0.846; P = 0.029). The AUC of ADC-B category combined with BI-RADS reached 0.870 (95% CI: 0.829-0.904) for ADC-measurable lesions and 0.861 (95% CI: 0.822-0.894) for all lesions. The diagnostic combination significantly improves the specificity of BI-RADS (from 17.1% to 49.5% for ADC measurable lesions and from 20% to 45.6% for all lesions; P < 0.001) while maintaining sensitivity.
The combination of predefined ADC-B category and BI-RADS has the potential to enable classification of breast lesion types with high accuracy.
While DWI has been incorporated into clinical MRI protocols at numerous medical centres, it has not been included in the official BI-RADS criteria. Adding ADC-B category system to BI-RADS classification significantly improves the specificity of breast lesion classification without decreasing sensitivity compared to the BI-RADS alone.
研究预定义的乳腺表观扩散系数(ADC-B)分类系统在鉴别乳腺良恶性病变中的诊断性能,并与乳腺影像报告和数据系统(BI-RADS)进行比较。
这是一项单机构回顾性研究,研究对象为2019年4月至2023年5月期间接受乳腺MRI检查的患者。使用3-T MRI系统进行动态对比增强(DCE)MRI和扩散加权成像(DWI)。收集病变形态(肿块、非肿块)、大小和ADC的数据。组织学为参考标准。分析使用组内相关系数(ICC)评估读者间在测量ADC和ADC-B分类方面的一致性,以及基于受试者操作特征(ROC)曲线的诊断性能。
纳入了358名女性(平均年龄46.29岁;标准差11.03)的376个病变,均有病理结果(236个恶性和140个良性)。读者间在测量ADC(ICC = 0.991)和评估ADC-B分类(ICC = 0.96)方面的一致性极佳。ADC-B分类的总体诊断性能(曲线下面积[AUC],0.858;95%可信区间:0.816 - 0.894)高于BI-RADS(AUC,0.805;95%可信区间:0.759 - 0.)。对于可测量ADC的病变,ADC-B分类与BI-RADS联合的AUC达到0.870(95%可信区间:0.829 - 0.904),对于所有病变为0.861(95%可信区间:0.822 - 0.894)。这种诊断组合显著提高了BI-RADS的特异性(可测量ADC的病变从17.1%提高到49.5%,所有病变从20%提高到45.6%;P < 0.001),同时保持了敏感性。
预定义的ADC-B分类与BI-RADS相结合有潜力实现乳腺病变类型的高精度分类。
虽然DWI已被纳入众多医疗中心的临床MRI检查方案,但尚未纳入官方的BI-RADS标准。与单独的BI-RADS相比,在BI-RADS分类中加入ADC-B分类系统可显著提高乳腺病变分类的特异性,且不降低敏感性。