BACKGROUND Women with dense breasts face a double risk for breast cancer; they are at a higher risk for development of breast cancer than those with less dense breasts, and there is a greater chance that mammography will miss detection of a cancer in dense breasts due to the masking effect of surrounding fibroglandular tissue. These women may be candidates for supplemental screening. In this study, a masking risk model that was previously developed is tested on a cohort of cancer-free women to assess potential efficiency of stratification. METHODS Three masking risk models based on (1) BI-RADS density, (2) volumetric breast density (VBD), and (3) a combination of VBD and detectability were applied to stratify the mammograms of 1897 cancer-free women. The fraction of cancer-free women whose mammograms were deemed by the algorithm to be masked and who would be considered for supplemental imaging was computed as was the corresponding fraction in a screened population of interval (masked) cancers that would be potentially detected by supplemental imaging. RESULTS Of the models tested, the combined VBD/detectability model offered the highest efficiency for stratification to supplemental imaging. It predicted that 725 supplemental screens would be performed per interval cancer potentially detected, at an operating point that allowed detection of 64% of the interval cancers. In comparison, stratification based on the upper two BI-RADS density categories required 1117 supplemental screenings per interval cancer detected to capture 64% of interval cancers. CONCLUSION The combined VBD/detectability models perform better than BI-RADS and offer a continuum of operating points, suggesting that this model may be effective in guiding a stratified screening environment.

中文翻译：

---

研究使用掩蔽风险预测器进行分层乳腺癌筛查的可行性。

背景：乳房致密的女性面临着患乳腺癌的双重风险；与乳房密度较低的人相比，她们患乳腺癌的风险更高，而且由于周围纤维腺组织的掩蔽效应，乳房 X 光检查更有可能漏掉致密乳房中的癌症。这些女性可能是补充筛查的候选人。在这项研究中，在一组无癌症女性身上测试了先前开发的掩蔽风险模型，以评估分层的潜在效率。方法 基于 (1) BI-RADS 密度、(2) 乳腺体积密度 (VBD) 和 (3) VBD 与可检测性组合的三种掩蔽风险模型应用于对 1897 名无癌症女性的乳房 X 光检查进行分层。计算出其乳房 X 光检查被算法视为被掩盖并被考虑进行补充成像的无癌女性的比例，以及可能通过补充成像检测到的间隔（被掩盖）癌症筛查人群中的相应比例。 。结果 在测试的模型中，VBD/可检测性组合模型提供了最高的分层到补充成像效率。它预测，每个可能检测到的间隔癌症将进行 725 次补充筛查，操作点允许检测出 64% 的间隔癌症。相比之下，基于上面两个 BI-RADS 密度类别的分层需要对每个检测到的间隔癌症进行 1117 次补充筛查，才能捕获 64% 的间隔癌症。结论 组合的 VBD/可检测性模型比 BI-RADS 表现更好，并提供连续的操作点，表明该模型可能有效指导分层筛查环境。