Comparison of Performance Between a Short Categorized Lifestyle Exposure-based Colon Cancer Risk Prediction Tool and a Model Using Continuous Measures

Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk

PURPOSE

We compared a simple breast cancer risk prediction model, BRISK (which includes mammographic density, polygenic risk and clinical factors), against a similar model with more risk factors (simplified Rosner) and against two commonly used clinical models (Gail and IBIS).

METHODS

Using nested case-control data from the Nurses' Health Study, we compared the models' association, discrimination and calibration. Classification performance was compared between Gail and BRISK for 5-year risks and between IBIS and BRISK for remaining lifetime risk.

RESULTS

The odds ratio per standard deviation was 1.43 (95% CI 1.32, 1.55) for BRISK 5-year risk, 1.07 (95% CI 0.99, 1.14) for Gail 5-year risk, 1.72 (95% CI 1.59, 1.87) for simplified Rosner 10-year risk, 1.51 (95% CI 1.41, 1.62) for BRISK remaining lifetime risk and 1.26 (95% CI 1.16, 1.36) for IBIS remaining lifetime risk. The area under the receiver operating characteristic curve (AUC) was improved for BRISK over Gail for 5-year risk (AUC = 0.636 versus 0.511, P < 0.0001) and for BRISK over IBIS for remaining lifetime risk (AUC = 0.647 versus 0.571, P < 0.0001). BRISK was well calibrated for the estimation of both 5-year risk (expected/observed [E/O] = 1.03; 95% CI 0.73, 1.46) and remaining lifetime risk (E/O = 1.01; 95% CI 0.86, 1.17). The Gail 5-year risk (E/O = 0.85; 95% CI 0.58, 1.24) and IBIS remaining lifetime risk (E/O = 0.73; 95% CI 0.60, 0.87) were not well calibrated, with both under-estimating risk. BRISK improves classification of risk compared to Gail 5-year risk (NRI = 0.31; standard error [SE] = 0.031) and IBIS remaining lifetime risk (NRI = 0.287; SE = 0.035).

CONCLUSION

BRISK performs better than two commonly used clinical risk models and no worse compared to a similar model with more risk factors.

Essentialism and Exclusion: Racism in Cancer Risk Prediction Models

Cancer risk prediction models have the potential to revolutionize the science and practice of cancer prevention and control by identifying the likelihood that a patient will develop cancer at some point in the future, likely experience more benefit than harm from a given intervention, and survive their cancer for a certain number of years. The ability of risk prediction models to produce estimates that are valid and reliable for people from diverse socio-demographic backgrounds-and consequently their utility for broadening the reach of precision medicine to marginalized populations-depends on ensuring that the risk factors included in the model are represented as thoroughly and as accurately as possible. However, cancer risk prediction models created in the United States have a critical limitation, the origins of which stem from the country's earliest days: they either erroneously treat the social construct of race as an immutable biological factor (ie, they "essentialize" race), or they exclude from the model those socio-contextual factors that are associated with both race and health outcomes. Models that essentialize race and/or exclude socio-contextual factors sometimes incorporate "race corrections" that adjust a patient's risk estimate up or down based on their race. This commentary discusses the origins of race corrections, potential flaws with such corrections, and strategies for developing cohorts for developing risk prediction models that do not essentialize race or exclude key socio-contextual factors. Such models will help move the science of cancer prevention and control towards its goal of eliminating cancer disparities and achieving health equity.

A user-friendly objective prediction model in predicting colorectal cancer based on 234 044 Asian adults in a prospective cohort

BACKGROUND

Prediction models of colorectal cancer (CRC) had limited application for not being user-friendly. Whether fecal immunochemical tests (FITs) can help predict CRC has been overlooked.

PATIENTS AND METHODS

With 1972 CRCs identified, 234 044 adults aged ≥40 years were successively enrolled between 1994 and 2008. Prediction models were developed by questionnaire/medical screening and quantitative FIT. NNS (number needed to scope to find one cancer) is time dependent, spanning entire study period. Significant 'risk factors' were family history, body mass index, smoking, drinking, inactivity, hypertension, diabetes, carcinoembryonic antigen, and C-reactive protein.

RESULTS

Positive FIT (≥20 μg/g hemoglobin/feces) had cancer risk 10-fold larger than negative FIT, and within each age group, another 10-fold difference. The C statistic of FIT (0.81) with age and sex alone was superior to the 'common risk-factors' model (0.73). NNS, stratified by age and by FIT values, demonstrated a scorecard of cancer risks, like 1/15 or 1/25, in 5 years. When FIT was negative, cancer risk was small (1/750-1/3000 annually). The larger the FIT, the sooner the appearance of CRC. For every 80-μg/g increase of FIT, there were 1.5-year earlier development of CRC incidence and 1-year earlier development of CRC mortality, respectively. Given the same FIT value, CRC events appeared in the proximal colon sooner than the distal colon.

CONCLUSIONS

A simple user-friendly model based on a single FIT value to predict CRC risk was developed. When positive, NNS offered a simple quantitative value, with a better precision than most risk factors, even combined. When FIT is negative, risk is very small, but requiring a repeat every other year to rule out false negative. FIT values correlated well with CRC prognosis, with worst for proximal CRC.

Adherence of Internet-Based Cancer Risk Assessment Tools to Best Practices in Risk Communication: Content Analysis

BACKGROUND

Internet-based risk assessment tools offer a potential avenue for people to learn about their cancer risk and adopt risk-reducing behaviors. However, little is known about whether internet-based risk assessment tools adhere to scientific evidence for what constitutes good risk communication strategies. Furthermore, their quality may vary from a user experience perspective.

OBJECTIVE

This study aims to understand the extent to which current best practices in risk communication have been applied to internet-based cancer risk assessment tools.

METHODS

We conducted a search on August 6, 2019, to identify websites that provided personalized assessments of cancer risk or the likelihood of developing cancer. Each website (N=39) was coded according to standardized criteria and focused on 3 categories: general website characteristics, accessibility and credibility, and risk communication formats and strategies.

RESULTS

Some best practices in risk communication were more frequently adhered to by websites. First, we found that undefined medical terminology was widespread, impeding comprehension for those with limited health literacy. For example, 90% (35/39) of websites included technical language that the general public may find difficult to understand, yet only 23% (9/39) indicated that medical professionals were their intended audience. Second, websites lacked sufficient information for users to determine the credibility of the risk assessment, making it difficult to judge the scientific validity of their risk. For instance, only 59% (23/39) of websites referenced the scientific model used to calculate the user's cancer risk. Third, practices known to foster unbiased risk comprehension, such as adding qualitative labels to quantitative numbers, were used by only 15% (6/39) of websites.

CONCLUSIONS

Limitations in risk communication strategies used by internet-based cancer risk assessment tools were common. By observing best practices, these tools could limit confusion and cultivate understanding to help people make informed decisions and motivate people to engage in risk-reducing behaviors.

Simplified Breast Risk Tool Integrating Questionnaire Risk Factors, Mammographic Density, and Polygenic Risk Score: Development and Validation

BACKGROUND

Clinical use of breast cancer risk prediction requires simplified models. We evaluate a simplified version of the validated Rosner-Colditz model and add percent mammographic density (MD) and polygenic risk score (PRS), to assess performance from ages 45-74. We validate using the Mayo Mammography Health Study (MMHS).

METHODS

We derived the model in the Nurses' Health Study (NHS) based on: MD, 77 SNP PRS and a questionnaire score (QS; lifestyle and reproductive factors). A total of 2,799 invasive breast cancer cases were diagnosed from 1990-2000. MD (using Cumulus software) and PRS were assessed in a nested case-control study. We assess model performance using this case-control dataset and evaluate 10-year absolute breast cancer risk. The prospective MMHS validation dataset includes 21.8% of women age <50, and 434 incident cases identified over 10 years of follow-up.

RESULTS

In the NHS, MD has the highest odds ratio (OR) for 10-year risk prediction: OR_{per SD} = 1.48 [95% confidence interval (CI): 1.31-1.68], followed by PRS, OR_{per SD} = 1.37 (95% CI: 1.21-1.55) and QS, OR_{per SD} = 1.25 (95% CI: 1.11-1.41). In MMHS, the AUC adjusted for age + MD + QS 0.650; for age + MD + QS + PRS 0.687, and the NRI was 6% in cases and 16% in controls.

CONCLUSION

A simplified assessment of QS, MD, and PRS performs consistently to discriminate those at high 10-year breast cancer risk.

IMPACT

This simplified model provides accurate estimation of 10-year risk of invasive breast cancer that can be used in a clinical setting to identify women who may benefit from chemopreventive intervention.See related commentary by Tehranifar et al., p. 587.

Risk Ladder, Table, or Bulleted List? Identifying Formats That Effectively Communicate Personalized Risk and Risk Reduction Information for Multiple Diseases

BACKGROUND

Personalized medicine may increase the amount of probabilistic information patients encounter. Little guidance exists about communicating risk for multiple diseases simultaneously or about communicating how changes in risk factors affect risk (hereafter "risk reduction").

PURPOSE

To determine how to communicate personalized risk and risk reduction information for up to 5 diseases associated with insufficient physical activity in a way laypeople can understand and that increases intentions.

METHODS

We recruited 500 participants with <150 min weekly of physical activity from community settings. Participants completed risk assessments for diabetes, heart disease, stroke, colon cancer, and breast cancer (women only) on a smartphone. Then, they were randomly assigned to view personalized risk and risk reduction information organized as a bulleted list, a simplified table, or a specialized vertical bar graph ("risk ladder"). Last, they completed a questionnaire assessing outcomes. Personalized risk and risk reduction information was presented as categories (e.g., "very low"). Our analytic sample (N = 372) included 41.3% individuals from underrepresented racial/ethnic backgrounds, 15.9% with vocational-technical training or less, 84.7% women, 43.8% aged 50 to 64 y, and 71.8% who were overweight/obese.

RESULTS

Analyses of covariance with post hoc comparisons showed that the risk ladder elicited higher gist comprehension than the bulleted list (P = 0.01). There were no significant main effects on verbatim comprehension or physical activity intentions and no moderation by sex, race/ethnicity, education, numeracy, or graph literacy (P > 0.05). Sequential mediation analyses revealed a small beneficial indirect effect of risk ladder versus list on intentions through gist comprehension and then through perceived risk (b_{IndirectEffect} = 0.02, 95% confidence interval: 0.00, 0.04).

CONCLUSION

Risk ladders can communicate the gist meaning of multiple pieces of risk information to individuals from many sociodemographic backgrounds and with varying levels of facility with numbers and graphs.

Commentary: 20 years online with "Your Disease Risk"

The Your Disease Risk tool (yourdiseaserisk.wustl.edu) went live to the public in January 2000 and was one of the first personalized health risk assessment sites on the Internet. Its launch marked the culmination of years of work by a large, multi-disciplinary university team whose primary goal was to translate the science on cancer prevention into accurate, engaging, and useful messages for the public. Today, 20 years on, Your Disease Risk has expanded from its initial four cancers to include 18 different tools designed for today’s users. This commentary reviews important moments and lessons learned in the first two decades of Your Disease Risk.

Translating Cancer Risk Prediction Models into Personalized Cancer Risk Assessment Tools: Stumbling Blocks and Strategies for Success

Cancer risk prediction models such as those published in Cancer Epidemiology, Biomarkers, and Prevention are a cornerstone of precision medicine and public health efforts to improve population health outcomes by tailoring preventive strategies and therapeutic treatments to the people who are most likely to benefit. However, there are several barriers to the effective translation, dissemination, and implementation of cancer risk prediction models into clinical and public health practice. In this commentary, we discuss two broad categories of barriers. Specifically, we assert that the successful use of risk-stratified cancer prevention and treatment strategies is particularly unlikely if risk prediction models are translated into risk assessment tools that (i) are difficult for the public to understand or (ii) are not structured in a way to engender the public's confidence that the results are accurate. We explain what aspects of a risk assessment tool's design and content may impede understanding and acceptance by the public. We also describe strategies for translating a cancer risk prediction model into a cancer risk assessment tool that is accessible, meaningful, and useful for the public and in clinical practice.