Breast Cancer Risk Model Requirements for Counseling, Prevention, and Screening

Abstract

Background: Incorporation of polygenic risk scores and mammographic density into models to predict breast cancer incidence can increase discriminatory accuracy (area under the receiver operating characteristic curve [AUC]) from 0.6 for models based only on epidemiologic factors to 0.7. It is timely to assess what impact these improvements will have on individual counseling and on public health prevention and screening strategies, and to determine what further improvements are needed.

Methods: We studied various clinical and public health applications using a log-normal distribution of risk.

Results: Provided they are well calibrated, even risk models with AUCs of 0.6 to 0.7 provide useful perspective for individual counseling and for weighing the harms and benefits of preventive interventions in the clinic. At the population level, they are helpful for designing preventive intervention trials, for assessing reductions in absolute risk from reducing exposure to modifiable risk factors, and for resource allocation (although a higher AUC would be desirable for risk-based allocation). Other public health applications require higher AUCs that can only be achieved with risk predictors 1.6 to 8.8 times as strong as all those yet discovered combined. Such applications are preventing an appreciable proportion of population disease when employing a high-risk prevention strategy and deciding who should be screened for subclinical disease.

Conclusions: Current and foreseeable risk models are useful for counseling and some prevention activities, but given the daunting challenge of achieving, for example, an AUC of 0.8, considerable effort should be put into finding effective preventive interventions and screening strategies with fewer adverse effects.

Figure 1.

Illustration of discriminatory accuracy (area under the curve [AUC]) and plot of AUC against the log-normal variance. A) Normal densities of log (absolute risk) for case patients (dashed line) and noncase subjects (solid line) for an AUC of 0.6 (green upper A panel) and an AUC of 0.8 (blue lower A panel). B) AUC is plotted against the variance, ${\sigma }^2$, of lognormally distributed risk in the population. AUC = area under the curve; BCRAT = Breast Cancer Risk Assessment Tool.

Figure 2.

Life-threatening events averted each year by breast cancer chemoprevention in 100 000 white women age 50 to 59 years as a function of area under the curve. Only a high-risk subset is given the intervention. Three intervention scenarios are tamoxifen, tamoxifen but with no increased risk of endometrial cancer (like raloxifene), tamoxifen but with no increased stroke or endometrial cancer risk. AUC = area under the curve.

Figure 3.

Fraction of lives saved by risk-based allocation of mammograms, compared with giving screening mammograms to all women, as a function of area under the curve when there is only enough money to give mammograms to half the population. Results are shown for various ratios k of the cost of risk assessment to the cost of a mammographic screen. AUC = area under the curve.

Figure 4.

Proportion of cases followed, $PCF(p),$ plotted as a function of AUC for p values of .1, .2, .3, .5, and .9. AUC = area under the curve.

Figure 5.

Number needed to screen to detect one prevalent breast cancer plotted against area under the curve. Four loci are shown for women age 40 to 44 or 50 to 54 years and based on screening either the top 10% or top 30% at highest risk. AUC = area under the curve.