JNCI：自动化的乳腺密度检测技术可快速鉴别女性高风险的乳腺癌

近日，刊登在国际著名杂志Journal of the National Cancer Institute上的一篇研究报告中，来自莫菲特癌症中心和梅奥诊所的研究者开发出了一种新型的计算机算法，可以基于对乳房x光筛查分析来更容易地定量乳腺癌的发病风险。在很多研究中都指出乳房x光检查密度的增加和乳腺癌发病风险的增加直接相关，但是对乳腺癌发病风险的具体定量的方法却一直由于条件限制而没有开发出来。如今这项研究就为鉴定女性乳腺癌的发病风险提供了希望和一定的研究依据。

研究者J. Heine表示，我们开发出了一种自动化的方法来估算乳房x光检查的密度从而来评估乳房x光检查中灰度值的变化情况。进行高密度乳房x光检查的女性患乳腺癌风险或许更高一些，然而乳房x光检查并不能用于临床中来进行一定量的风险评估，部分原因是由于其缺少自动化和标准化的测量方法。

这项研究中，研究者将乳腺密度测量变化的准确性以及可靠性同乳房x光检查中乳腺组织的密度等级进行比较来评估乳腺癌的风险。研究者发现测量值的变化是一种自动化的乳房x光检查测量方法，其与胶片和数字成像技术等同，或可用于临床条件下来评估癌症风险。

另外研究者也发现测量变化和乳腺癌风险之间的关系对于乳房x光检查技术来说更加优于诊断。自动化的方法可以在乳腺癌案例以及对照组之间进行清晰的区分。

研究者Thomas A. Sellers博士表示，这项研究通过进行三个精心设计的流行病学研究，对女性乳腺密度的测量进行了详细的评估和验证性研究分析。我们将新型的乳腺密度测量方法同此前已经建立好的测量方法进行了对比从而揭示了新型方法的优越性。相关研究由美国国家癌症研究所等机构提供资助。

doi:10.1093/jnci/djs254
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A Novel Automated Mammographic Density Measure and Breast Cancer Risk

John J. Heine, Christopher G. Scott, Thomas A. Sellers, Kathleen R. Brandt, Daniel J. Serie, Fang-Fang Wu, Marilyn J. Morton, Beth A. Schueler, Fergus J. Couch, Janet E. Olson, V. Shane Pankratz and Celine M. Vachon

Background Mammographic breast density is a strong breast cancer risk factor but is not used in the clinical setting, partly because of a lack of standardization and automation. We developed an automated and objective measurement of the grayscale value variation within a mammogram, evaluated its association with breast cancer, and compared its performance with that of percent density (PD). Methods Three clinic-based studies were included: a case–cohort study of 217 breast cancer case subjects and 2094 non-case subjects and two case–control studies comprising 928 case subjects and 1039 control subjects and 246 case subjects and 516 control subjects, respectively. Percent density was estimated from digitized mammograms using the computer-assisted Cumulus thresholding program, and variation was estimated from an automated algorithm. We estimated hazards ratios (HRs), odds ratios (ORs), the area under the receiver operating characteristic curve (AUC), and 95% confidence intervals (CIs) using Cox proportional hazards models for the cohort and logistic regression for case–control studies, with adjustment for age and body mass index. We performed a meta-analysis using random study effects to obtain pooled estimates of the associations between the two mammographic measures and breast cancer. All statistical tests were two-sided. Results The variation measure was statistically significantly associated with the risk of breast cancer in all three studies (highest vs lowest quartile: HR = 7.0 [95% CI = 4.6 to 10.4]; OR = 10.7 [95% CI = 7.5 to 15.3]; OR = 2.6 [95% CI = 1.6 to 4.2]; all P trend < .001). In two studies, the risk estimates and AUCs for the variation measure were greater than those for percent density (AUCs for variation = 0.71 and 0.76; AUCs for percent density = 0.65 and 0.65), whereas in the third study, these estimates were similar (AUC for variation = 0.60 and AUC for percent density = 0.61). A meta-analysis of the three studies demonstrated a stronger association between variation and breast cancer (highest vs lowest quartile: RR = 3.6, 95% CI = 1.9 to 7.0) than between percent density and breast cancer (highest vs lowest quartile: RR = 2.3, 95% CI = 1.9 to 2.9). Conclusion The association between the automated variation measure and the risk of breast cancer is at least as strong as that for percent density. Efforts to further evaluate and translate the variation measure to the clinical setting are warranted.