Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance.
Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance.
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DOI：
10.1186/bcr3352
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发表时间：
2012-11-05
期刊：
影响因子：
--
通讯作者：
Terry MB
中科院分区：
文献类型：
--
作者：
Quante AS;Whittemore AS;Shriver T;Strauch K;Terry MB
临床医生根据患者的家族史和遗传因素，对平均风险和高于平均风险的患者使用不同的乳腺癌风险模型。我们使用了来自乳腺癌风险跨越全谱的女性的纵向队列数据，以确定遗传和非遗传协变量，这些协变量区分了两种常用模型的表现，包括非遗传因素- BCRAT，也称为Gail模型，通常用于平均风险的患者，IBIS，也称为Tyrer Cuzick模型，通常用于高于平均风险的患者。我们评估了目前在临床环境中应用的BCRAT和IBIS模型10年乳腺癌绝对风险的表现，使用了来自1857名女性的前瞻性数据，平均随访时间为8.1年，其中83名发生了癌症。这个队列跨越了乳腺癌风险的连续体，其中一些受试者的风险低于平均水平。因此，个体风险的广泛差异使得在女性亚组中检查模型性能成为一个有趣的人群。为了校正模型，我们将队列划分为模型分配风险的四分位数，并使用Hosmer-Lemeshow （HL）卡方统计量比较分配风险和观察风险之间的差异。为了区分模型，我们计算了接收者操作符曲线下的面积（AUC）和病例风险百分位数（CRPs）。BCRAT和IBIS分配的10年风险不同（差异范围为0.001至79.5）。BCRAT和ibis分配的平均风险分别为3.18%和5.49%，低于该队列10年累积发生乳腺癌的概率（6.25%；95%可信区间(CI) = 5.0至7.8%）。IBIS患者（HL X42 = 7.2， P值0.13）比BCRAT患者（HL X42 = 22.0， P值<0.001）分配风险和观察风险之间的一致性更好。IBIS模型也比BCRAT模型（AUC = 63.2%, CI = 57.6% ~ 68.9%）具有更好的辨别能力（AUC = 69.5%, CI = 63.8% ~ 75.2%）。在几乎所有协变量特异性亚组中，BCRAT的平均风险显著低于观察到的风险，而IBIS的风险与观察到的风险基本一致，即使在被认为处于平均风险的女性亚组中（例如，无乳腺癌家族史，BRCA1/2突变阴性）。使用扩展家族史和遗传数据开发的模型，如IBIS模型，在被认为处于平均风险的女性（例如，没有乳腺癌家族史，BRCA1/2突变阴性）中也表现良好。将这种模型扩展到包括额外的非遗传信息可能会提高妇女在乳腺癌风险连续体中的表现。
Clinicians use different breast cancer risk models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used models that include nongenetic factors - BCRAT, also called Gail model, generally used for patients with average risk and IBIS, also called Tyrer Cuzick model, generally used for patients with above-average risk. We evaluated the performance of the BCRAT and IBIS models as currently applied in clinical settings for 10-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide variation in individual risk makes it an interesting population to examine model performance across subgroups of women. For model calibration, we divided the cohort into quartiles of model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs). The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRAT- and IBIS-assigned risks of 3.18% and 5.49%, respectively, were lower than the cohort's 10-year cumulative probability of developing breast cancer (6.25%; 95% confidence interval (CI) = 5.0 to 7.8%). Agreement between assigned and observed risks was better for IBIS (HL X42 = 7.2, P value 0.13) than BCRAT (HL X42 = 22.0, P value <0.001). The IBIS model also showed better discrimination (AUC = 69.5%, CI = 63.8% to 75.2%) than did the BCRAT model (AUC = 63.2%, CI = 57.6% to 68.9%). In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Models developed using extended family history and genetic data, such as the IBIS model, also perform well in women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Extending such models to include additional nongenetic information may improve performance in women across the breast cancer risk continuum.
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DOI：
10.1093/jnci/86.8.620
发表时间：
1994-04-20
期刊：
JOURNAL OF THE NATIONAL CANCER INSTITUTE
影响因子：
--
作者：
BONDY, ML;LUSTBADER, ED;VOGEL, VG
通讯作者：
VOGEL, VG
影响因子：
10.3
作者：
Kennedy, DO;Agrawal, M;Santella, RM
通讯作者：
Santella, RM
DOI：
10.1093/jnci/86.8.600
发表时间：
1994-04-20
期刊：
JOURNAL OF THE NATIONAL CANCER INSTITUTE
影响因子：
--
作者：
SPIEGELMAN, D;COLDITZ, GA;HERTZMARK, E
通讯作者：
HERTZMARK, E
影响因子：
3.8
作者：
Neuhausen, Susan L.;Ozcelik, Hilmi;Southey, Melissa C.;John, Esther M.;Godwin, Andrew K.;Chung, Wendy;Iriondo-Perez, Jeniffer;Miron, Alexander;Santella, Regina M.;Whittemore, Alice;Andrulis, Irene L.;Buys, Saundra S.;Daly, Mary B.;Hopper, John L.;Seminara, Daniela;Senie, Ruby T.;Terry, Mary Beth
通讯作者：
Terry, Mary Beth
影响因子：
45.3
作者：
Huo, Dezheng;Senie, Ruby T.;Olopade, Olufunmilayo I.
通讯作者：
Olopade, Olufunmilayo I.