Review of non-clinical risk models to aid prevention of breast cancer

Abstract

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.

An independent external validation of melanoma risk prediction models using the Australian Melanoma Family Study

“…The discriminative performance of these 12 models is generally high and comparable with other melanoma and cancer risk‐prediction models, where AUC ranges were 0·73–0·93 for melanoma, 11 0·56–0·89 for breast cancer, 12 0·63–0·70 for colorectal cancer 13 and 0·61–0·81 for lung cancer; 14 some of these models included age as a predictor. Similar to external validation studies of other cancer types, there is lower discrimination on external validation than on internal validation 13,14 .…”

A way forward for cancer prevention therapy: personalized risk assessment

“…In the past two decades, several breast cancer risk prediction models have been developed to evaluate a woman’s potential risk for this disease. In addition to the National Cancer Institute (NCI)’s summary on risk assessment models on breast cancer [13], three other systematic reviews have been done on this topic [14–16]. Lee et al conducted a systematic review and identified 13 risk factors that were consistent throughout models [15].…”

“…how predicted probabilities agree with observed proportions) but have less ability to discriminate (i.e. the ability to separate individuals into different classes) [14].…”

Emerging nanomedicines for effective breast cancer immunotherapy

“…Breast cancer remains the most frequently diagnosed malignancy and leading cause of cancer-associated deaths among females worldwide [1,2]. Despite significant declines in mortality, the incidence of breast cancer has risen more than 30% in the last 25 years [3,4]. Sex, genetic factors, use of hormone therapy, lifestyle and dietary habits, and age are the main risk factors for breast cancer [5].…”