新型工具模型或可精确评估个体患乳腺癌的风险

  精确估计女性患乳腺癌风险的国家风险模型或许可以经过改进来给出一个更为准确的评估，而这项基于100多万病人的修正后数据或将帮助揭示，大约有3%甚至更多女性5年期患乳腺癌的风险将增加3倍。
  刊登在国际杂志the Journal of Clinical Oncology上的一篇研究论文中，来自美国加州大学旧金山分校（UC San Francisco）的科学家就更新了当前的乳腺癌风险模型，更新密度包括对乳腺密度的分类，该因子可以帮助决定个体患乳腺癌的可能性，同时研究者还更新了良性活检组织的结果。
  首个乳腺癌监督协会的风险模型并不能通过活检来说明肿瘤非恶性增殖的状况，这就包括乳腺导管上皮非典型增生(Atypical Ductal Hyperplasia)，相比没有该状况的患者而言，这种状况会增加个体3.5倍至5倍患乳腺癌的风险；而乳房小叶原位癌（Lobular carcinoma in situ）则会增加个体7-11倍患乳腺癌的风险。
  研究者Jeffrey Tice博士表示，文章中我们对110万年龄在35至74岁间的多种族女性个体进行研究分析，让这些这些女性都进行乳房x线摄影术分析，而且这些个体都没有乳腺癌历史；在接下来平均6.9年的研究时间段内，有将近1.8万名女性都被诊断为乳腺浸润性癌，这种恶性癌症将扩散到输乳管及机体其它健康组织中。
  当鉴别出癌细胞增殖的女性，研究者就表示，原本乳腺癌风险为3%及以上的患者数量或将从第一个风险模型中人数的9.3%增加至更新模型后的27.8%。Tice说道，这种修饰后的模型将帮助我们更加精确地鉴别出那些可以利用化学预防乳腺癌发生的女性，而对于这些患者而言，抑制乳腺癌发生的药物的疗效或将优于药物对机体的损伤；化学预防需要利用选择性的雌激素受体调节剂，比如他莫昔芬和雷洛昔芬，其都可以阻断供养乳腺癌肿瘤生长的激素，而这些药物同时也会减少个体患乳腺癌风险至少三分之一。
  研究者认为本文研究数据或将增加当前模型的对患者的益处，因为该模型可以通过个体的年龄、种族、乳腺癌家族史及乳腺密度来计算个体患乳腺癌的风险。最后Tice说道，本文研究将加强医生和患者之间的协作，实现乳腺癌的最优化筛查及实施风险降低措施来降低个体患癌风险。2014年在美国就有超过23.2万女性被诊断为乳腺癌，每年将近有4万名患者因乳腺癌死亡。（转化医学网360zhyx.com）
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转化医学网推荐的原文摘要：

Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer.
JCO    
Tice JA1, Miglioretti DL2, Li CS2, Vachon CM2, Gard CC2, Kerlikowske K2.
PURPOSE:
Women with proliferative breast lesions are candidates for primary prevention, but few risk models incorporate benign findings to assess breast cancer risk. We incorporated benign breast disease (BBD) diagnoses into the Breast Cancer Surveillance Consortium (BCSC) risk model, the only breast cancer risk assessment tool that uses breast density.
METHODS:
We developed and validated a competing-risk model using 2000 to 2010 SEER data for breast cancer incidence and 2010 vital statistics to adjust for the competing risk of death. We used Cox proportional hazards regression to estimate the relative hazards for age, race/ethnicity, family history of breast cancer, history of breast biopsy, BBD diagnoses, and breast density in the BCSC.
RESULTS:
We included 1,135,977 women age 35 to 74 years undergoing mammography with no history of breast cancer; 17% of the women had a prior breast biopsy. During a mean follow-up of 6.9 years, 17,908 women were diagnosed with invasive breast cancer. The BCSC BBD model slightly overpredicted risk (expected-to-observed ratio, 1.04; 95% CI, 1.03 to 1.06) and had modest discriminatory accuracy (area under the receiver operator characteristic curve, 0.665). Among women with proliferative findings, adding BBD to the model increased the proportion of women with an estimated 5-year risk of 3% or higher from 9.3% to 27.8% (P < .001).
CONCLUSION:
The BCSC BBD model accurately estimates women's risk for breast cancer using breast density and BBD diagnoses. Greater numbers of high-risk women eligible for primary prevention after BBD diagnosis are identified using the BCSC BBD model.