Assessment of breast cancer risk: which tools to use?

Considering clonal hematopoiesis of indeterminate potential in space radiation risk analysis for hematologic cancers and cardiovascular disease

BACKGROUND

Expanding human presence in space through long-duration exploration missions and commercial space operations warrants improvements in approaches for quantifying crew space radiation health risks. Currently, risk assessment models for radiogenic cancer and cardiovascular disease consider age, sex, and tobacco use, but do not incorporate other modifiable (e.g., body weight, physical activity, diet, environment) and non-modifiable individual risk factors (e.g., genetics, medical history, race/ethnicity, family history) that may greatly influence crew health both in-mission and long-term. For example, clonal hematopoiesis of indeterminate potential (CHIP) is a relatively common age-related condition that is an emerging risk factor for a variety of diseases including cardiovascular disease and cancer. CHIP carrier status may therefore exacerbate health risks associated with space radiation exposure.

METHODS

In the present study, published CHIP hazard ratios were used to modify background hazard rates for coronary heart disease, stroke, and hematologic cancers in the National Aeronautics and Space Administration space radiation risk assessment model. The risk of radiation exposure-induced death for these endpoints was projected for a future Mars exploration mission scenario.

RESULTS

Here we show appreciable increases in the lifetime risk of exposure-induced death for hematologic malignancies, coronary heart disease, and stroke, which are observed as a function of age after radiation exposure for male and female crew members that are directly attributable to the elevated health risks for CHIP carriers.

CONCLUSIONS

We discuss the importance of evaluating individual risk factors such as CHIP as part of a comprehensive space radiation risk assessment strategy aimed at effective risk communication and disease surveillance for astronauts embarking on future exploration missions.

Psychotic disorders as a framework for precision psychiatry

People with psychotic disorders can show marked interindividual variations in the onset of illness, responses to treatment and relapse, but they receive broadly similar clinical care. Precision psychiatry is an approach that aims to stratify people with a given disorder according to different clinical outcomes and tailor treatment to their individual needs. At present, interindividual differences in outcomes of psychotic disorders are difficult to predict on the basis of clinical assessment alone. Therefore, current research in psychosis seeks to build models that predict outcomes by integrating clinical information with a range of biological measures. Here, we review recent progress in the application of precision psychiatry to psychotic disorders and consider the challenges associated with implementing this approach in clinical practice.

Approach to the patient with a palpable breast mass

ABSTRACT

Breast mass is a common finding in patients presenting to primary care, women's health, or urgent care clinics. There are multiple etiologies that can cause a palpable breast mass both benign and malignant. PAs must know how to approach a patient with a palpable breast mass as well as what appropriate diagnostic evaluation is needed.

Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening

BACKGROUND

Deep learning breast cancer risk models demonstrate improved accuracy compared with traditional risk models but have not been prospectively tested. We compared the accuracy of a deep learning risk score derived from the patient's prior mammogram to traditional risk scores to prospectively identify patients with cancer in a cohort due for screening.

METHODS

We collected data on 119 139 bilateral screening mammograms in 57 617 consecutive patients screened at 5 facilities between September 18, 2017, and February 1, 2021. Patient demographics were retrieved from electronic medical records, cancer outcomes determined through regional tumor registry linkage, and comparisons made across risk models using Wilcoxon and Pearson χ2 2-sided tests. Deep learning, Tyrer-Cuzick, and National Cancer Institute Breast Cancer Risk Assessment Tool (NCI BCRAT) risk models were compared with respect to performance metrics and area under the receiver operating characteristic curves.

RESULTS

Cancers detected per thousand patients screened were higher in patients at increased risk by the deep learning model (8.6, 95% confidence interval [CI] = 7.9 to 9.4) compared with Tyrer-Cuzick (4.4, 95% CI = 3.9 to 4.9) and NCI BCRAT (3.8, 95% CI = 3.3 to 4.3) models (P < .001). Area under the receiver operating characteristic curves of the deep learning model (0.68, 95% CI = 0.66 to 0.70) was higher compared with Tyrer-Cuzick (0.57, 95% CI = 0.54 to 0.60) and NCI BCRAT (0.57, 95% CI = 0.54 to 0.60) models. Simulated screening of the top 50th percentile risk by the deep learning model captured statistically significantly more patients with cancer compared with Tyrer-Cuzick and NCI BCRAT models (P < .001).

CONCLUSIONS

A deep learning model to assess breast cancer risk can support feasible and effective risk-based screening and is superior to traditional models to identify patients destined to develop cancer in large screening cohorts.

Towards implementation of comprehensive breast cancer risk prediction tools in health care for personalised prevention

Advances in knowledge about breast cancer risk factors have led to the development of more comprehensive risk models. These integrate information on a variety of risk factors such as lifestyle, genetics, family history, and breast density. These risk models have the potential to deliver more personalised breast cancer prevention. This is through improving accuracy of risk estimates, enabling more effective targeting of preventive options and creating novel prevention pathways through enabling risk estimation in a wider variety of populations than currently possible. The systematic use of risk tools as part of population screening programmes is one such example. A clear understanding of how such tools can contribute to the goal of personalised prevention can aid in understanding and addressing barriers to implementation. In this paper we describe how emerging models, and their associated tools can contribute to the goal of personalised healthcare for breast cancer through health promotion, early disease detection (screening) and improved management of women at higher risk of disease. We outline how addressing specific challenges on the level of communication, evidence, evaluation, regulation, and acceptance, can facilitate implementation and uptake.

Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries

BACKGROUND

Rigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk.

METHODS

Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19-75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds.

RESULTS

Among women <50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7-1.0) overall and 0.9 (0.7-1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7-1.3) and 1.2 (0.7-1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases.

CONCLUSION

Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines.

Combination of a 15-SNP Polygenic Risk Score and Classical Risk Factors for the Prediction of Breast Cancer Risk in Cypriot Women

Simple Summary

Breast cancer is the most commonly diagnosed type of cancer in women worldwide. Stratification of women based on their individual breast cancer risk could guide targeted preventative strategies and population screening. Integrated models that combine the effects of a polygenic risk score (PRS) with classical breast cancer risk factors could provide an individualized breast-cancer risk estimation. Although various studies have extensively evaluated the performance of such integrated models in populations of European ancestry, no previous studies have included individuals of Greek-Cypriot origin. To this end, we have assessed the predictive performance of a 15-SNP PRS (PRS₁₅), in combination with classical breast-cancer risk factors, in women of Greek-Cypriot origin. This proof-of-concept study suggests that models combining genetic data with classical risk factors may be used in the future for the prediction of breast-cancer risk and, therefore, supports their potential clinical utility for targeted preventative strategies in Cypriot women.

Abstract

The PRS combines multiplicatively the effects of common low-risk single nucleotide polymorphisms (SNPs) and has the potential to be used for the estimation of an individual’s risk for a trait or disease. PRS has been successfully implemented for the prediction of breast cancer risk. The combination of PRS with classical breast cancer risk factors provides a more comprehensive risk estimation and could, thus, improve risk stratification and personalized preventative strategies. In this study, we assessed the predictive performance of the combined effect of PRS₁₅ with classical breast-cancer risk factors in Cypriot women using 1109 cases and 1177 controls from the MASTOS study. The PRS₁₅ was significantly associated with an increased breast cancer risk in Cypriot women OR (95% CI) 1.66 (1.25–2.19). The integrated risk model obtained an AUC (95% CI) 0.70 (0.67–0.72) and had the ability to stratify women according to their disease status at the extreme deciles. These results provide evidence that the combination of PRS with classical risk factors may be used in the future for the stratification of Cypriot women based on their disease risk, and support its potential clinical utility for targeted preventative actions and population screening.

Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification

BACKGROUND

External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification.

METHODS

Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35-74 years. Risk projections in a target population of US white non-Hispanic women age 50-70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS).

RESULTS

The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years.

CONCLUSIONS

iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.