From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands.
Radiology. 2023 Aug;308(2):e222841. doi: 10.1148/radiol.222841.
Background Automated identification of quantitative breast parenchymal enhancement features on dynamic contrast-enhanced (DCE) MRI scans could provide added value in assessment of breast cancer risk in women with extremely dense breasts. Purpose To automatically identify quantitative properties of the breast parenchyma on baseline DCE MRI scans and assess their association with breast cancer occurrence in women with extremely dense breasts. Materials and Methods This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. MRI was performed in eight hospitals between December 2011 and January 2016. After segmentation of fibroglandular tissue, quantitative features (including volumetric density, volumetric morphology, and enhancement characteristics) of the parenchyma were extracted from baseline MRI scans. Principal component analysis was used to identify parenchymal measures with the greatest variance. Multivariable Cox proportional hazards regression was applied to assess the association between breast cancer occurrence and quantitative parenchymal features, followed by stratification of significant features into tertiles. Results A total of 4553 women (mean age, 55.7 years ± 6 [SD]) with extremely dense breasts were included; of these women, 122 (3%) were diagnosed with breast cancer. Five principal components representing 96% of the variance were identified, and the component explaining the greatest independent variance (42%) consisted of MRI features relating to volume of enhancing parenchyma. Multivariable analysis showed that volume of enhancing parenchyma was associated with breast cancer occurrence (hazard ratio [HR], 1.09; 95% CI: 1.01, 1.18; = .02). Additionally, women in the high tertile of volume of enhancing parenchyma showed a breast cancer occurrence twice that of women in the low tertile (HR, 2.09; 95% CI: 1.25, 3.61; = .005). Conclusion In women with extremely dense breasts, a high volume of enhancing parenchyma on baseline DCE MRI scans was associated with increased occurrence of breast cancer as compared with a low volume of enhancing parenchyma. © RSNA, 2023 . See also the editorial by Grimm in this issue.
背景 自动识别动态对比增强（DCE）MRI 扫描中定量乳腺实质增强特征可在评估致密乳腺女性乳腺癌风险方面提供附加价值。
目的 自动识别基线 DCE MRI 扫描中乳腺实质的定量特征，并评估其与致密乳腺女性乳腺癌发生的相关性。
材料与方法 本研究为 Dense Tissue and Early Breast Neoplasm Screening 试验的二次分析。MRI 于 2011 年 12 月至 2016 年 1 月在 8 家医院进行。在分割纤维腺体组织后，从基线 MRI 扫描中提取实质的定量特征（包括容积密度、容积形态和增强特征）。主成分分析用于识别具有最大方差的实质测量值。多变量 Cox 比例风险回归用于评估乳腺癌发生与定量实质特征之间的相关性，然后将显著特征分层为三分位。
结果 共纳入 4553 名（平均年龄 55.7 岁±6[标准差]）致密乳腺女性；其中 122 名（3%）被诊断为乳腺癌。确定了代表 96%方差的 5 个主成分，解释最大独立方差（42%）的成分由与增强实质体积相关的 MRI 特征组成。多变量分析显示，增强实质体积与乳腺癌发生相关（危险比 [HR]，1.09；95%置信区间：1.01，1.18； =.02）。此外，增强实质体积高三分位的女性乳腺癌发生风险是低三分位女性的两倍（HR，2.09；95%置信区间：1.25，3.61； =.005）。
结论 在致密乳腺女性中，与低增强实质体积相比，基线 DCE MRI 扫描中增强实质体积较高与乳腺癌发生增加相关。
