Barlow William E, White Emily, Ballard-Barbash Rachel, Vacek Pamela M, Titus-Ernstoff Linda, Carney Patricia A, Tice Jeffrey A, Buist Diana S M, Geller Berta M, Rosenberg Robert, Yankaskas Bonnie C, Kerlikowske Karla
Cancer Research and Biostatistics, 1730 Minor Avenue, Suite 1900, Seattle, WA 98101, USA.
J Natl Cancer Inst. 2006 Sep 6;98(17):1204-14. doi: 10.1093/jnci/djj331.
Risk prediction models for breast cancer can be improved by the addition of recently identified risk factors, including breast density and use of hormone therapy. We used prospective risk information to predict a diagnosis of breast cancer in a cohort of 1 million women undergoing screening mammography.
There were 2,392,998 eligible screening mammograms from women without previously diagnosed breast cancer who had had a prior mammogram in the preceding 5 years. Within 1 year of the screening mammogram, 11,638 women were diagnosed with breast cancer. Separate logistic regression risk models were constructed for premenopausal and postmenopausal examinations by use of a stringent (P<.0001) criterion for the inclusion of risk factors. Risk models were constructed with 75% of the data and validated with the remaining 25%. Concordance of the predicted with the observed outcomes was assessed by a concordance (c) statistic after logistic regression model fit. All statistical tests were two-sided.
Statistically significant risk factors for breast cancer diagnosis among premenopausal women included age, breast density, family history of breast cancer, and a prior breast procedure. For postmenopausal women, the statistically significant factors included age, breast density, race, ethnicity, family history of breast cancer, a prior breast procedure, body mass index, natural menopause, hormone therapy, and a prior false-positive mammogram. The model may identify high-risk women better than the Gail model, although predictive accuracy was only moderate. The c statistics were 0.631 (95% confidence interval [CI] = 0.618 to 0.644) for premenopausal women and 0.624 (95% CI = 0.619 to 0.630) for postmenopausal women.
Breast density is a strong additional risk factor for breast cancer, although it is unknown whether reduction in breast density would reduce risk. Our risk model may be able to identify women at high risk for breast cancer for preventive interventions or more intensive surveillance.
通过添加最近确定的风险因素，包括乳腺密度和激素疗法的使用，可以改进乳腺癌风险预测模型。我们利用前瞻性风险信息，对100万名接受乳腺钼靶筛查的女性队列中乳腺癌的诊断进行预测。
共有2392998例符合条件的乳腺钼靶筛查，来自于在之前5年内未曾诊断出乳腺癌且之前进行过乳腺钼靶检查的女性。在乳腺钼靶筛查的1年内，11638名女性被诊断出患有乳腺癌。通过使用严格的（P<0.0001）风险因素纳入标准，为绝经前和绝经后检查构建了单独的逻辑回归风险模型。风险模型使用75%的数据构建，并使用其余25%的数据进行验证。在逻辑回归模型拟合后，通过一致性（c）统计量评估预测结果与观察结果的一致性。所有统计检验均为双侧检验。
绝经前女性中，乳腺癌诊断的统计学显著风险因素包括年龄、乳腺密度、乳腺癌家族史和既往乳腺手术史。对于绝经后女性，统计学显著因素包括年龄、乳腺密度、种族、族裔、乳腺癌家族史、既往乳腺手术史、体重指数、自然绝经、激素疗法和既往乳腺钼靶假阳性结果。该模型可能比盖尔模型能更好地识别高危女性，尽管预测准确性仅为中等。绝经前女性的c统计量为0.631（95%置信区间[CI]=0.618至0.644），绝经后女性为0.624（95%CI=0.619至0.630）。
乳腺密度是乳腺癌的一个强有力的额外风险因素，尽管尚不清楚降低乳腺密度是否会降低风险。我们的风险模型或许能够识别出乳腺癌高危女性，以便进行预防性干预或更密切的监测。
