Loubrie Stephane, Zou Jingjing, Rodriguez-Soto Ana E, Lim Jihe, Andreassen Maren M S, Cheng Yuwei, Batasin Summer J, Ebrahimi Sheida, Fang Lauren K, Conlin Christopher C, Seibert Tyler M, Hahn Michael E, Dialani Vandana, Wei Catherine J, Karimi Zahra, Kuperman Joshua, Dale Anders M, Ojeda-Fournier Haydee, Pisano Etta, Rakow-Penner Rebecca
Department of Radiology, University of California San Diego, La Jolla, California, USA.
Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA.
J Magn Reson Imaging. 2025 Apr;61(4):1876-1887. doi: 10.1002/jmri.29599. Epub 2024 Sep 18.
Breast cancer screening with dynamic contrast-enhanced MRI (DCE-MRI) is recommended for high-risk women but has limitations, including variable specificity and difficulty in distinguishing cancerous (CL) and high-risk benign lesions (HRBL) from average-risk benign lesions (ARBL). Complementary non-invasive imaging techniques would be useful to improve specificity.
To evaluate the performance of a previously-developed breast-specific diffusion-weighted MRI (DW-MRI) model (BS-RSI3C) to improve discrimination between CL, HRBL, and ARBL in an enriched screening population.
Prospective.
Exactly 187 women, either with mammography screening recommending additional imaging (N = 49) or high-risk individuals undergoing routine breast MRI (N = 138), before the biopsy.
FIELD STRENGTH/SEQUENCE: Multishell DW-MRI echo planar imaging sequence with a reduced field of view at 3.0 T.
A total of 72 women had at least one biopsied lesion, with 89 lesions categorized into ARBL, HRBL, CL, and combined CLs and HRBLs (CHRLs). DW-MRI data were processed to produce apparent diffusion coefficient (ADC) maps, and estimate signal contributions (C, C, and C-restricted, hindered, and free diffusion, respectively) from the BS-RSI3C model. Lesion regions of interest (ROIs) were delineated on DW images based on suspicious DCE-MRI findings by two radiologists; control ROIs were drawn in the contralateral breast.
One-way ANOVA and two-sided t-tests were used to assess differences in signal contributions and ADC values among groups. P-values were adjusted using the Bonferroni method for multiple testing, P = 0.05 was used for the significance level. Receiver operating characteristics (ROC) curves and intra-class correlations (ICC) were also evaluated.
C, √CC, and were significantly different in HRBLs compared with ARBLs (P-values < 0.05). The had the highest AUC (0.821) in differentiating CHRLs from ARBLs, performing better than ADC (0.696), especially in non-mass enhancement (0.776 vs. 0.517).
This study demonstrated the BS-RSI3C could differentiate HRBLs from ARBLs in a screening population, and separate CHRLs from ARBLs better than ADC.
推荐对高危女性采用动态对比增强磁共振成像（DCE-MRI）进行乳腺癌筛查，但该方法存在局限性，包括特异性可变，以及难以将癌性病变（CL）和高危良性病变（HRBL）与平均风险良性病变（ARBL）区分开来。辅助性非侵入性成像技术有助于提高特异性。
评估先前开发的乳腺特异性扩散加权磁共振成像（DW-MRI）模型（BS-RSI3C）在富集筛查人群中区分CL、HRBL和ARBL的性能。
前瞻性研究。
活检前，共有187名女性，其中49名因乳腺钼靶筛查建议进一步成像，138名高危个体接受常规乳腺MRI检查。
场强/序列：3.0 T时采用多壳DW-MRI回波平面成像序列，视野缩小。
共有72名女性至少有一个活检病变，89个病变分为ARBL、HRBL、CL以及CL和HRBL合并病变（CHRL）。对DW-MRI数据进行处理以生成表观扩散系数（ADC）图，并根据BS-RSI3C模型估计信号贡献（分别为C、C以及C受限、受阻和自由扩散）。两名放射科医生根据可疑的DCE-MRI结果在DW图像上勾勒出病变感兴趣区（ROI）；在对侧乳腺绘制对照ROI。
采用单因素方差分析和双侧t检验评估各组间信号贡献和ADC值的差异。使用Bonferroni方法对多重检验的P值进行校正，显著性水平设定为P = 0.05。还评估了受试者操作特征（ROC）曲线和组内相关性（ICC）。
与ARBL相比，HRBL中的C、√CC和 有显著差异（P值<0.05）。在区分CHRL和ARBL方面， 的曲线下面积（AUC）最高（0.821），优于ADC（0.696），在非肿块强化方面表现更佳（0.776对0.517）。
本研究表明，BS-RSI3C能够在筛查人群中区分HRBL和ARBL，并且在区分CHRL和ARBL方面比ADC表现更好。
2级。
