Cirrus: An Automated Mammography-Based Measure of Breast Cancer Risk Based on Textural Features
Cirrus: An Automated Mammography-Based Measure of Breast Cancer Risk Based on Textural Features
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DOI：
10.1093/jncics/pky057
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发表时间：
2018-10-01
影响因子：
4.4
通讯作者：
Hopper, John L.
中科院分区：
文献类型：
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作者：
Schmidt, Daniel F.;Makalic, Enes;Hopper, John L.
背景：我们应用机器学习来寻找一种新的基于乳房X光照片信息的乳腺癌预测因子。方法：使用图像处理技术，自动处理来自澳大利亚女性队列和病例对照研究以及日裔美国女性队列研究的1345例和4235名对照的46 158个模拟乳房X光照片，提取20个不基于像素亮度阈值的纹理特征。我们使用贝叶斯套索回归来创建个体和乳房X光检查特定的乳腺癌风险测量方法。我们在不同的研究中对测量方法进行了培训和测试。我们使用Logistic回归将Cirrus与传统的乳腺X线密度测量方法进行了拟合，并计算了根据年龄和体重指数调整后的每标准差的优势比(OR)。结果：综合研究，几乎所有的纹理特征都与病例对照状态有关。在一项研究中训练并在另一项研究中测试的Cirrus测量的OR值从1.56到1.78(均为P<；10(-6))。对于来自联合研究的Cirrus测量，OR为190(95%可信区间[CI]=1.73至2.09)，相当于四个四分位数间的风险比，在调整传统测量后几乎没有减弱。与之相比，传统方法的OR值为1.34(95%CI=1.25~1.43)，调整CIRRUS因素后的OR值为1.16(95%CI=1.08~1.24；P=4×10(-5))。结论：结合纹理图像特征建立的全自动个人风险测量方法预测乳腺癌风险的效果好于传统的乳腺摄影密度风险测量方法，其风险预测能力仅为后者的一半。就在人群基础上区分受影响和未受影响的女性而言，Cirrus可能是已知的乳腺癌最强风险因素之一。
Background: We applied machine learning to find a novel breast cancer predictor based on information in a mammogram.Methods: Using image-processing techniques, we automatically processed 46 158 analog mammograms for 1345 cases and 4235 controls from a cohort and case-control study of Australian women, and a cohort study of Japanese American women, extracting 20 textural features not based on pixel brightness threshold. We used Bayesian lasso regression to create individual- and mammogram-specific measures of breast cancer risk, Cirrus. We trained and tested measures across studies. We fitted Cirrus with conventional mammographic density measures using logistic regression, and computed odds ratios (OR) per standard deviation adjusted for age and body mass index.Results: Combining studies, almost all textural features were associated with case-control status. The ORs for Cirrus measures trained on one study and tested on another study ranged from 1.56 to 1.78 (all P < 10(-6)). For the Cirrus measure derived from combining studies, the OR was 190 (95% confidence interval [CI] = 1.73 to 2.09), equivalent to a fourfold interquartile risk ratio, and was little attenuated after adjusting for conventional measures. In contrast, the OR for the conventional measure was 1.34 (95% CI = 1.25 to 1.43), and after adjusting for Cirrus it became 1.16 (95% CI = 1.08 to 1.24; P = 4 x 10(-5)).Conclusions: A fully automated personal risk measure created from combining textural image features performs better at predicting breast cancer risk than conventional mammographic density risk measures, capturing half the risk-predicting ability of the latter measures. In terms of differentiating affected and unaffected women on a population basis, Cirrus could be one of the strongest known risk factors for breast cancer.