The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms.
The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms.
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使用GAIL模型，体重指数和SNP的使用来预测异常（BI-RADS 4）乳房X线照片的女性的乳腺癌。
DOI：
10.1186/s13058-014-0509-4
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发表时间：
2015-01-08
期刊：
影响因子：
--
通讯作者：
Armstrong K
中科院分区：
文献类型：
--
作者：
McCarthy AM;Keller B;Kontos D;Boghossian L;McGuire E;Bristol M;Chen J;Domchek S;Armstrong K
乳房X光检查会导致大量的假阳性。用预测乳腺癌危险因素指导X线片异常的随访，可提高筛查的阳性预测值。我们评估了Gail模型、体重指数(BMI)和遗传标记物在乳房X光检查异常的女性中预测癌症诊断的应用。我们还检查了试验前风险因素可以在多大程度上将未患癌症的女性重新分类到活组织检查阈值以下。根据美国放射学会的乳房影像报告和数据系统(BI-RADS)，我们招募了一组因乳腺X光检查异常(BI-RADS 4)而进行活检的女性。在活检前对乳腺癌危险因素进行评估。对与乳腺癌相关的12个单核苷酸多态(SNPs)进行了验证。用Logistic回归分析Gail危险因素、BMI和SNPs与癌症诊断(浸润性癌或导管原位癌)的关系。使用接收器操作特征曲线下的面积评估模型辨别，并使用Hosmer-Lemesow拟合优度测试评估校准。对患有和不患有乳腺癌的妇女的癌症诊断预测概率的分布进行了比较。在多因素模型中，年龄(优势比(OR) = 1.05；95%可信区间(CI)1.03~1.08；P <； 0.001)、单核苷酸多态性相对危险度(OR = 2.3；95%CI 1.06~4.99，P = 0.035)和体重指数(≥30~25 kg/m2；OR = 2.20；95%CI 1.05~4.58；P = 0.036)与乳腺癌的诊断显著相关。老年女性比年轻女性更有可能被诊断为乳腺癌。在多变量调整后，SNP小组的相对风险仍然与乳腺癌的诊断密切相关。较高的BMI也与乳腺癌诊断的几率增加密切相关。与体重指数为25千克/平方米的女性相比，肥胖女性(ORBMI 2.20；95%CI，1.05至4.58；P = 0.036)被诊断为癌症的几率是女性的两倍多。SNP小组似乎在白人和黑人女性中都具有预测能力。乳腺癌风险因素，包括BMI和遗传标记，可以预测BI-RADS 4乳房X光检查女性的癌症诊断。使用预测风险因素来指导异常乳房X光检查的后续工作，可以减轻假阳性乳房X光检查的负担。本文的在线版本(doi：10.1186/s13058-0140509-4)包含补充材料，授权用户可以使用。
Mammography screening results in a significant number of false-positives. The use of pretest breast cancer risk factors to guide follow-up of abnormal mammograms could improve the positive predictive value of screening. We evaluated the use of the Gail model, body mass index (BMI), and genetic markers to predict cancer diagnosis among women with abnormal mammograms. We also examined the extent to which pretest risk factors could reclassify women without cancer below the biopsy threshold. We recruited a prospective cohort of women referred for biopsy with abnormal (BI-RADS 4) mammograms according to the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS). Breast cancer risk factors were assessed prior to biopsy. A validated panel of 12 single-nucleotide polymorphisms (SNPs) associated with breast cancer were measured. Logistic regression was used to assess the association of Gail risk factors, BMI and SNPs with cancer diagnosis (invasive or ductal carcinoma in situ). Model discrimination was assessed using the area under the receiver operating characteristic curve, and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. The distribution of predicted probabilities of a cancer diagnosis were compared for women with or without breast cancer. In the multivariate model, age (odds ratio (OR) = 1.05; 95% confidence interval (CI), 1.03 to 1.08; P < 0.001), SNP panel relative risk (OR = 2.30; 95% CI, 1.06 to 4.99, P = 0.035) and BMI (≥30 kg/m2 versus <25 kg/m2; OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) were significantly associated with breast cancer diagnosis. Older women were more likely than younger women to be diagnosed with breast cancer. The SNP panel relative risk remained strongly associated with breast cancer diagnosis after multivariable adjustment. Higher BMI was also strongly associated with increased odds of a breast cancer diagnosis. Obese women (OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) had more than twice the odds of cancer diagnosis compared to women with a BMI <25 kg/m2. The SNP panel appeared to have predictive ability among both white and black women. Breast cancer risk factors, including BMI and genetic markers, are predictive of cancer diagnosis among women with BI-RADS 4 mammograms. Using pretest risk factors to guide follow-up of abnormal mammograms could reduce the burden of false-positive mammograms. The online version of this article (doi:10.1186/s13058-014-0509-4) contains supplementary material, which is available to authorized users.
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影响因子：
10.3
作者：
Barlow, William E.;White, Emily;Kerlikowske, Karla
通讯作者：
Kerlikowske, Karla
影响因子：
3.8
作者：
Andersen, Shaneda Warren;Trentham-Dietz, Amy;Gangnon, Ronald E.;Hampton, John M.;Figueroa, Jonine D.;Skinner, Halcyon G.;Engelman, Corinne D.;Klein, Barbara E.;Titus, Linda J.;Newcomb, Polly A.
通讯作者：
Newcomb, Polly A.
影响因子：
5.5
作者：
Armstrong, Katrina;Handorf, Elizabeth A.;Chen, Jinbo;Demeter, Mirar N. Bristol
通讯作者：
Demeter, Mirar N. Bristol
影响因子：
3.7
作者：
Burnside ES;Chhatwal J;Alagoz O
通讯作者：
Alagoz O
DOI：
10.1093/jnci/djj332
发表时间：
2006-09-06
期刊：
JOURNAL OF THE NATIONAL CANCER INSTITUTE
影响因子：
--
作者：
Chen, Jinbo;Pee, David;Gail, Mitchell H.
通讯作者：
Gail, Mitchell H.