Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Cancer Epidemiol Biomarkers Prev. 2019 Jul;28(7):1187-1194. doi: 10.1158/1055-9965.EPI-18-1039. Epub 2019 Apr 23.
The Gail model and the model developed by Tyrer and Cuzick are two questionnaire-based approaches with demonstrated ability to predict development of breast cancer in a general population.
We compared calibration, discrimination, and net reclassification of these models, using data from questionnaires sent every 2 years to 76,922 participants in the Nurses' Health Study between 1980 and 2006, with 4,384 incident invasive breast cancers identified by 2008 (median follow-up, 24 years; range, 1-28 years). In a random one third sample of women, we also compared the performance of these models with predictions from the Rosner-Colditz model estimated from the remaining participants.
Both the Gail and Tyrer-Cuzick models showed evidence of miscalibration (Hosmer-Lemeshow < 0.001 for each) with notable ( < 0.01) overprediction in higher-risk women (2-year risk above about 1%) and underprediction in lower-risk women (risk below about 0.25%). The Tyrer-Cuzick model had slightly higher C-statistics both overall ( < 0.001) and in age-specific comparisons than the Gail model (overall C, 0.63 for Tyrer-Cuzick vs. 0.61 for the Gail model). Evaluation of net reclassification did not favor either model. In the one third sample, the Rosner-Colditz model had better calibration and discrimination than the other two models. All models had C-statistics <0.60 among women ages ≥70 years.
Both the Gail and Tyrer-Cuzick models had some ability to discriminate breast cancer cases and noncases, but have limitations in their model fit.
Refinements may be needed to questionnaire-based approaches to predict breast cancer in older and higher-risk women.
Gail 模型和 Tyrer 和 Cuzick 开发的模型是两种基于问卷的方法，已被证明能够预测普通人群中乳腺癌的发展。
我们使用 1980 年至 2006 年间向参加护士健康研究的 76922 名参与者每两年发送一次的问卷中的数据，比较了这些模型的校准、区分度和净重新分类，共确定了 4384 例浸润性乳腺癌病例，到 2008 年（中位随访时间为 24 年；范围为 1-28 年）。在女性的随机三分之一样本中，我们还比较了这些模型的性能与剩余参与者中估计的 Rosner-Colditz 模型的预测结果。
Gail 和 Tyrer-Cuzick 模型都显示出校准错误的证据（每个模型的 Hosmer-Lemeshow ＜0.001），在高风险女性（2 年风险高于约 1%）中存在明显的过度预测（＜0.01），而在低风险女性（风险低于约 0.25%）中存在预测不足。总体而言（＜0.001），Tyrer-Cuzick 模型的 C 统计量略高于 Gail 模型，并且在年龄特异性比较中也略高于 Gail 模型（总体 C，Tyrer-Cuzick 为 0.63，Gail 模型为 0.61）。对净重新分类的评估并不支持任何一种模型。在三分之一的样本中，Rosner-Colditz 模型的校准和区分度均优于其他两种模型。所有模型在年龄≥70 岁的女性中 C 统计量均＜0.60。
Gail 和 Tyrer-Cuzick 模型都具有一定的区分乳腺癌病例和非病例的能力，但在模型拟合方面存在一些局限性。
可能需要对基于问卷的方法进行改进，以预测年龄较大和风险较高的女性的乳腺癌。
