I have tried to indicate where models of absolute breast cancer risk are useful in clinical management and disease prevention. In addition to giving important general perspective to patients, such models can be used to weigh risks and benefits of a preventive outcome formally (Section 3). In the context of disease prevention, these models are useful for designing intervention trials (Section 4.1), and for assessing the potential absolute reductions in disease risk that might result from reductions in modifiable exposures in the population (Section 4.2). These four applications do not require high discriminatory accuracy, but do depend on good model calibration. Absolute risk models are needed to implement “high risk strategies” for disease prevention, for which good discriminatory accuracy is needed, in addition to good calibration, in order to achieve large reductions in disease incidence (Section 4.3). Absolute risk models may also play a role in the productive allocation of prevention resources under cost constraints (Section 4.4). Here again, good discriminatory accuracy is advantageous, and risk assessment should be inexpensive in comparison with the cost of intervention.
This paper has discussed the use of absolute risk models for predicting disease risk and for disease prevention, but models of absolute risk are also very important in patient management following disease onset. For example, if a 65 year old man has a recent diagnosis of prostate cancer with favorable prognostic indicators, he may have a small absolute risk of dying of prostate cancer, because competing causes of mortality are likely to kill him first. Such a patient might be well advised to defer surgical or radiation treatment unless later indications, such as increasing prostate-specific antigen levels, suggest a need for active intervention. Thus the absolute risk of disease-specific mortality can influence management decisions following disease onset.
The disappointing results of the “high risk strategy” for breast cancer prevention reflect the twin weaknesses of risk models with limited discriminatory accuracy, and interventions that are too toxic to benefit all but those at highest breast cancer risk. As mentioned previously, it is very hard to increase discriminatory accuracy [43
] unless strong risk factors are available. The percentage of the area that is radiographically dense on a mammogram, called mammographic density, is a strong risk factor that has been used in absolute risk models [52
], as has a related radiographic feature, the Breast Imaging Reporting Data System (BIRADS) [53
]. When added to BCRAT, mammographic density increased the AUC by 0.047 [52
], which is nearly twice as much as much as the increase from adding SNPs () [3
]. Although further gains could be expected from adding SNPs to a model with mammographic density, the gains are likely to be modest [44
]. Another strong risk factor is pathology identified in breast biopsies [54
], but such information is only available from women with biopsies. However, unless a nearly painless way can be found to obtain strongly predictive pathology or biomarker data from women in the general population, the discriminatory accuracy of available models will remain a limiting factor in the “high risk” prevention strategy.
An alternative approach that relies less strongly on the risk model is to use interventions with fewer adverse effects. Such interventions can be beneficial for women with lower breast cancer risks, and if the intervention is safe enough, breast cancer risk assessment may not be needed at all. Primarily on the basis of observational studies, Cummings et al. [55
] noted that “exercise, weight reduction, low-fat diet, and reduced alcohol intake were associated with decreased risk of breast cancer” in most studies. A randomized trial in the Women’s Health Initiative [56
] indicated a 9% reduction in the incidence of invasive breast cancer in the low-fat diet group, compared to placebo, which almost attained statistical significance (95% confidence interval (CI): −1% to 17% risk reduction). Because these lifestyle changes are thought to be safe, Cummings et al. concluded that they “ should be recommended regardless of (breast cancer) risk” for post-menopausal women. The Women’s Health Initiative randomized trial showed an increase in invasive breast cancer incidence of 26% (95% CI: 0 to 59%) in post-menopausal women receiving hormone replacement with estrogen and progestin, compared to placebo [57
]. Since that trial, sales of this type of hormone replacement therapy have dropped, and breast cancer incidence rates have declined [58
]. Avoidance of this exposure is another safe way to reduce breast cancer risk, based on evidence from the randomized trial.
As more is learned about the etiology of breast cancer, there may be other safe preventive interventions that are even more effective. If a viral agent were found to be important, safe vaccines might be developed. Promising early results suggest this will be an effective approach for human papillomavirus [59
], which causes cervical cancer, and for hepatitis B [60
], which causes liver cancer. Armitage and Doll studied cancer incidence trends and proposed a multistage model of carcinogenesis to explain the exponential increase in cancer incidence with age for many cancers [61
]. Subsequent studies in cancer biology have identified several changes that a cell must undergo in order to become cancerous [62
]. If safe interventions could be found that reduced the rates of several of these transitions, the compound effect could be to substantially decrease breast cancer incidence rates and the strong dependence of incidence on age. Migration studies indicate that Asian women, whose rates of breast cancer incidence were low in Asia, have much higher rates in the U.S. a generation or two after migrating [63
], and in Shanghai, breast cancer incidence rates have been increasing rapidly during a period of changing dietary and lifestyle patterns [64
]. These facts suggest that lifestyle exposures play a prominent role in breast cancer carcinogenesis and may offer safe opportunities for prevention.
In summary, models of absolute risk currently have a useful but limited role in counseling and in breast cancer prevention. Efforts to increase discriminatory accuracy can expand that role, but increased success in disease prevention will depend on safer and more effective interventions that may or may not need to be used in conjunction with risk models.