JCO：可精准预测乳腺癌风险的新模型

  近日，梅奥诊所的研究人员开发出了一种新型的乳腺癌风险预测模型，其可以将良性乳腺疾病患者的乳腺活组织检查特性同单一病人的人口统计信息相结合，来更精确地对乳腺癌风险进行分类，这种新型预测模型优于当前的乳腺癌筛选标准，相关研究刊登于国际杂志the Journal of Clinical Oncology上。

  研究者Amy Degnim表示，正常情况下医生们通过活组织检查来对个体的乳腺组织进行评估，要么是进行测试要么就是进行乳房x光检查来筛选乳腺癌；然而大约四分之三的活组织检查都是良性结果且都和良性乳房疾病相关，每年有超过100万美国女性的活组织检查结果都为良性，但她们想知道是否她们后期会进展为乳腺癌。

  研究人员假设，当特定的乳腺组织为良性结果时或许可以帮助预测哪些女性患乳腺癌的风险会增加，而新型预测模型可以在个体活组织检查确定为良性结果后对女性患乳腺癌的风险进行精确预测。为了检测这种新型模型，研究者对大约10000万名乳腺活组织检查结果为良性及进行后期随访的女性进行研究分析，结果确定了各年龄段乳腺癌的发病率及死亡率；结合377名后期发展为乳腺癌及734名对照个体的相对风险数据，研究人员证实了这种新型乳腺癌预测模型的准确性和效率。

  这种新型模型在一系列模型开发中的一致性统计值为0.665，在验证系列中的一致性统计值则为0.629，这些值相比BCRAT预测模型的值要高一些，BCRAT并不足以在活组织检查结果良性后来预测乳腺癌的风险。

  最后研究者Degnim说道，有些患良性乳腺癌疾病的女性往往患乳腺癌的风险较高，因此进行早期的检查非常关键；处于高风险乳腺癌的女性应当被及早鉴别出来，以便研究者可以给予一定的监督及治疗；但BCRAT风险预测模型却并不能在单一水平下对女性的患癌风险进行精确预测。（转化医学网360zhyx.com）

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转化医学网推荐的原文摘要：

Model for Individualized Prediction of Breast Cancer Risk After a Benign Breast Biopsy
JCO doi: 10.1200/JCO.2014.55.4865
V. Shane Pankratz, Amy C. Degnim, Ryan D. Frank, Marlene H. Frost, Daniel W. Visscher, Robert A. Vierkant, Tina J. Hieken, Karthik Ghosh, Yaman Tarabishy, Celine M. Vachon, Derek C. Radisky and Lynn C. Hartmann⇑

Purpose Optimal early detection and prevention for breast cancer depend on accurate identification of women at increased risk. We present a risk prediction model that incorporates histologic features of biopsy tissues from women with benign breast disease (BBD) and compare its performance to the Breast Cancer Risk Assessment Tool (BCRAT).

Methods We estimated the age-specific incidence of breast cancer and death from the Mayo BBD cohort and then combined these estimates with a relative risk model derived from 377 patient cases with breast cancer and 734 matched controls sampled from the Mayo BBD cohort to develop the BBD–to–breast cancer (BBD-BC) risk assessment tool. We validated the model using an independent set of 378 patient cases with breast cancer and 728 matched controls from the Mayo BBD cohort and compared the risk predictions from our model with those from the BCRAT.

Results The BBD-BC model predicts the probability of breast cancer in women with BBD using tissue-based and other risk factors. The concordance statistic from the BBD-BC model was 0.665 in the model development series and 0.629 in the validation series; these values were higher than those from the BCRAT (0.567 and 0.472, respectively). The BCRAT significantly underpredicted breast cancer risk after benign biopsy (P = .004), whereas the BBD-BC predictions were appropriately calibrated to observed cancers (P = .247).

Conclusion We developed a model using both demographic and histologic features to predict breast cancer risk in women with BBD. Our model more accurately classifies a woman's breast cancer risk after a benign biopsy than the BCRAT.