Review of non-clinical risk models to aid prevention of breast cancer.
Review of non-clinical risk models to aid prevention of breast cancer.
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DOI：
10.1007/s10552-018-1072-6
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发表时间：
2018-10
期刊：
影响因子：
--
通讯作者：
Muir KR
中科院分区：
文献类型：
--
作者：
Al-Ajmi K;Lophatananon A;Yuille M;Ollier W;Muir KR
疾病风险模型是一种统计方法，用于评估个体在规定时间内患上一种或多种疾病的概率。这种模式考虑到是否存在与疾病有关的特定流行病学风险因素，从而有可能确定风险较高的个人。这些模型目前在临床上用于识别高风险人群，包括识别患乳腺癌风险增加的女性。许多遗传和非遗传性乳腺癌风险模型已经开发出来。我们已经评估了现有的乳腺癌非遗传/非临床模型，其中包含可改变的风险因素。本次审查的重点是风险模型，可用于妇女自己在社区中的临床风险因素表征的情况下。在这些模型中纳入可修改的因素意味着它们可以用于改善与乳腺癌相关的初级预防和健康教育。使用PubMed、ScienceDirect和科克伦系统综述数据库进行文献检索。14项研究符合审查条件，样本量范围从654到248，407例受试者。审查的所有模型都有可接受的校准措施，预期/观察（E/O）比值范围为0.79至1.17。然而，歧视措施是可变的研究与一致性统计（C-统计）范围从0.56至0.89。我们的结论是，乳腺癌的风险模型，包括可修改的风险因素已经得到很好的校准，但有较少的区分能力。后者可能是由于模型中遗漏了一些重要的风险因素，或将模型应用于样本量有限的研究。更重要的是，大多数模型都缺少外部验证。跨模型的推广也是有问题的，因为一些变量可能不被认为适用于某些人群，并且每个模型的性能都受到特定人群特征的影响。总之，很明显，仍然需要开发一种更可靠的模型来估计乳腺癌风险，该模型具有良好的校准，能够准确区分高风险，并且在人群中具有更好的普遍性。
A disease risk model is a statistical method which assesses the probability that an individual will develop one or more diseases within a stated period of time. Such models take into account the presence or absence of specific epidemiological risk factors associated with the disease and thereby potentially identify individuals at higher risk. Such models are currently used clinically to identify people at higher risk, including identifying women who are at increased risk of developing breast cancer. Many genetic and non-genetic breast cancer risk models have been developed previously. We have evaluated existing non-genetic/non-clinical models for breast cancer that incorporate modifiable risk factors. This review focuses on risk models that can be used by women themselves in the community in the absence of clinical risk factors characterization. The inclusion of modifiable factors in these models means that they can be used to improve primary prevention and health education pertinent for breast cancer. Literature searches were conducted using PubMed, ScienceDirect and the Cochrane Database of Systematic Reviews. Fourteen studies were eligible for review with sample sizes ranging from 654 to 248,407 participants. All models reviewed had acceptable calibration measures, with expected/observed (E/O) ratios ranging from 0.79 to 1.17. However, discrimination measures were variable across studies with concordance statistics (C-statistics) ranging from 0.56 to 0.89. We conclude that breast cancer risk models that include modifiable risk factors have been well calibrated but have less ability to discriminate. The latter may be a consequence of the omission of some significant risk factors in the models or from applying models to studies with limited sample sizes. More importantly, external validation is missing for most of the models. Generalization across models is also problematic as some variables may not be considered applicable to some populations and each model performance is conditioned by particular population characteristics. In conclusion, it is clear that there is still a need to develop a more reliable model for estimating breast cancer risk which has a good calibration, ability to accurately discriminate high risk and with better generalizability across populations.
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影响因子：
3.8
作者：
Banegas, Matthew P.;Gail, Mitchell H.;LaCroix, Andrea;Thompson, Beti;Martinez, Maria Elena;Wactawski-Wende, Jean;John, Esther M.;Hubbell, F. Allan;Yasmeen, Shagufta;Katki, Hormuzd A.
通讯作者：
Katki, Hormuzd A.
影响因子：
3.8
作者：
Crooke, Philip S.;Justenhoven, Christina;Parl, Fritz F.
通讯作者：
Parl, Fritz F.
影响因子：
37.8
作者：
Cook, Nancy R.
通讯作者：
Cook, Nancy R.
影响因子：
10.3
作者：
Barlow, William E.;White, Emily;Kerlikowske, Karla
通讯作者：
Kerlikowske, Karla
影响因子：
8.8
作者：
Antoniou, A. C.;Cunningham, A. P.;Peto, J.;Evans, D. G.;Lalloo, F.;Narod, S. A.;Risch, H. A.;Eyfjord, J. E.;Hopper, J. L.;Southey, M. C.;Olsson, H.;Johannsson, O.;Borg, A.;Passini, B.;Radice, P.;Manoukian, S.;Eccles, D. M.;Tang, N.;Olah, E.;Anton-Culver, H.;Warner, E.;Lubinski, J.;Gronwald, J.;Gorski, B.;Tryggvadottir, L.;Syrjakoski, K.;Kallioniemi, O-P;Eerola, H.;Nevanlinna, H.;Pharoah, P. D. P.;Easton, D. F.
通讯作者：
Easton, D. F.