Department of Bioengineering, UC-San Francisco & UC-Berkeley Joint Program, San Francisco, CA, 94143, USA.
Department of Radiology and Biomedical Imaging, UC-San Francisco, San Francisco, CA, 94143, USA.
Med Phys. 2019 Mar;46(3):1309-1316. doi: 10.1002/mp.13410. Epub 2019 Feb 14.
Women with radiographically dense or texturally complex breasts are at increased risk for interval cancer, defined as cancers diagnosed after a normal screening examination. The purpose of this study was to create masking measures and apply them to identify interval risk in a population of women who experienced either screen-detected or interval cancers after controlling for breast density.
We examined full-field digital screening mammograms acquired from 2006 to 2015. Examinations associated with 182 interval cancers were matched to 173 screen-detected cancers on age, race, exam date and time since last imaging examination. Local Image Quality Factor (IQF) values were calculated and used to create IQF maps that represented mammographic masking. We used various statistics to define global masking measures of these maps. Association of these masking measures with interval cancer vs screen-detected cancer was estimated using conditional logistic regression in a univariate and adjusted model for Breast Imaging-Reporting and Data System (BI-RADS) density. Receiver operator curves were calculated in each case to compare specificity vs sensitivity, and area under those curves were generated. Proportion of screen-detected cancer was estimated for stratifications of IQF features.
Several masking features showed significant association with interval compared to screen-detected cancers after adjusting for BI-RADS density (up to P = 2.52E-6), and the 10th percentile of the IQF value (P = 1.72E-3) showed the strongest improvement in the area under the receiver operator curve, increasing from 0.65 using only BI-RADS density to 0.69. The highest masking group had a 32% proportion of screen-detected cancers while the low masking group had a 69% proportion.
We conclude that computer vision methods using model observers may improve quantifying the probability of breast cancer detection beyond using breast density alone.
乳腺 X 线摄影表现为致密或结构复杂的女性发生间期癌的风险增加，间期癌定义为在正常筛查检查后诊断出的癌症。本研究的目的是创建掩蔽措施，并将其应用于在控制乳腺密度的情况下，识别经历筛查发现或间期癌症的女性群体中的间期风险。
我们检查了 2006 年至 2015 年采集的全视野数字化筛查乳房 X 线照片。将与 182 例间期癌相关的检查与 173 例筛查发现的癌症在年龄、种族、检查日期和上次影像学检查后的时间进行匹配。计算局部图像质量因子 (IQF) 值，并使用这些值创建代表乳腺 X 线摄影掩蔽的 IQF 图。我们使用各种统计方法来定义这些图的全局掩蔽措施。在单变量和调整后的乳腺成像报告和数据系统 (BI-RADS) 密度模型中，使用条件逻辑回归估计这些掩蔽措施与间期癌与筛查发现的癌症之间的关联。在每种情况下计算接收者操作特征曲线，以比较特异性与敏感性，并生成这些曲线下的面积。对于 IQF 特征的分层，估计筛查发现的癌症的比例。
在调整 BI-RADS 密度后，与筛查发现的癌症相比，几个掩蔽特征与间期癌显著相关（最高 P 值为 2.52E-6），IQF 值的第 10 个百分位数（P 值为 1.72E-3）在接收者操作特征曲线下的面积方面显示出最强的改善，从仅使用 BI-RADS 密度时的 0.65 增加到 0.69。最高掩蔽组的筛查发现癌症比例为 32%，而低掩蔽组的筛查发现癌症比例为 69%。
我们得出结论，使用模型观察者的计算机视觉方法可能会提高仅使用乳腺密度来定量乳腺癌检测概率。
