Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort
Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort
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DOI：
10.1093/jnci/djz177
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发表时间：
2020-05-01
影响因子：
10.3
通讯作者：
Hughes, Kevin S.
中科院分区：
文献类型：
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作者：
McCarthy, Anne Marie;Guan, Zoe;Hughes, Kevin S.
背景：存在几种乳腺癌风险评估模型。很少有研究评估预测准确性的多个模型在大型screeningpopulation.Methods：我们评估了BRCAPRO，盖尔，克劳斯，乳腺癌监测联盟（BCSC），和Tyrer-Cuzick模型在预测乳腺癌的风险超过6年的35 921名妇女年龄在40-84岁谁接受了乳腺X线摄影筛查在牛顿-韦尔斯利医院从2007年至2009年的性能。我们使用受试者工作特征曲线下面积（AUC）评估模型区分度，并通过比较预测与预期（O/E）病例的比率评估校准。我们计算的平方根的Brier评分和阳性和阴性预测值的每个model.Results：我们的研究结果证实了良好的校准和可比的中等歧视的BRCAPRO，盖尔，Tyrer-Cuzick，和BCSC模型。Gail模型的O/E比和AUC略好（O/E = 0.98，95%置信区间[CI] = 0.91 - 1.06，AUC = 0.64，95% CI = 0.61 - 0.65）与BRCAPRO相比（O/E = 0.94，95% CI = 0.88至1.02，AUC = 0.61，95% CI = 0.59至0.63）和Tyrer-Cuzick（版本8，O/E = 0.84，95% CI = 0.79至0.91，AUC = 0.62，95% 0.60至0.64），BCSC模型在具有可用乳腺密度信息的女性中具有最高AUC（0/E = 0.97，95%CI = 0.89至1.05，AUC = 0.64，95%CI = 0.62至0.66）。所有模型的预测准确性较差的人表皮生长因子受体2阳性和三阴性乳腺癌比激素受体阳性的人表皮生长因子受体2阴性乳腺cancer.Conclusions：在一个大的队列进行乳腺X线摄影筛查的患者，现有的风险预测模型有相似的，中等的预测准确性和良好的校准整体。结合其他遗传和非遗传风险因素并估计肿瘤亚型风险的模型可能会进一步改善乳腺癌风险预测。
Background: Several breast cancer risk-assessment models exist. Few studies have evaluated predictive accuracy of multiple models in large screening populations.Methods: We evaluated the performance of the BRCAPRO, Gail, Claus, Breast Cancer Surveillance Consortium (BCSC), and Tyrer-Cuzick models in predicting risk of breast cancer over 6 years among 35 921 women aged 40-84 years who underwent mammography screening at Newton-Wellesley Hospital from 2007 to 2009. We assessed model discrimination using the area under the receiver operating characteristic curve (AUC) and assessed calibration by comparing the ratio of observed-to-expected (O/E) cases. We calculated the square root of the Brier score and positive and negative predictive values of each model.Results: Our results confirmed the good calibration and comparable moderate discrimination of the BRCAPRO, Gail, Tyrer-Cuzick, and BCSC models. The Gail model had slightly better O/E ratio and AUC (O/E = 0.98, 95% confidence interval [CI] = 0.91 to 1.06, AUC = 0.64, 95% CI = 0.61 to 0.65) compared with BRCAPRO (O/E = 0.94, 95% CI = 0.88 to 1.02, AUC = 0.61, 95% CI = 0.59 to 0.63) and Tyrer-Cuzick (version 8, O/E = 0.84, 95% CI = 0.79 to 0.91, AUC = 0.62, 95% 0.60 to 0.64) in the full study population, and the BCSC model had the highest AUC among women with available breast density information (O/E = 0.97, 95% CI = 0.89 to 1.05, AUC = 0.64, 95% CI = 0.62 to 0.66). All models had poorer predictive accuracy for human epidermal growth factor receptor 2 positive and triple-negative breast cancers than hormone receptor positive human epidermal growth factor receptor 2 negative breast cancers.Conclusions: In a large cohort of patients undergoing mammography screening, existing risk prediction models had similar, moderate predictive accuracy and good calibration overall. Models that incorporate additional genetic and nongenetic risk factors and estimate risk of tumor subtypes may further improve breast cancer risk prediction.