Utilization of breast cancer risk prediction models by cancer genetic counselors in clinical practice predominantly in the United States

Performance of LFSPRO TP53 germline carrier risk predictions compared to standard genetic counseling practice on prospectively collected probands

Purpose

Current clinical guidelines for genetic testing for Li-Fraumeni Syndrome (LFS) have many limitations, primarily the criteria don't consider detailed personal and family history information and may miss many individuals with LFS. A personalized risk assessment tool, LFSPRO, was created to estimate a proband's risk for LFS based on personal and family history information. The purpose of this study is to compare LFSPRO to existing clinical criteria to determine if LFSPRO can outperform these tools. Additionally, we gauged genetic counselors' (GCs) experience using LFSPRO for their patients.

Methods

Between December 2021 and March 2024, GCs identified patients concerning for LFS based on the patients' personal and family history information. This information was entered into LFSPRO to predict the risk to have a pathogenic/pathogenic (LP/P) germline TP53 variant. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) was compared between LFSPRO and Chompret criteria. Select GCs were asked to fill out surveys regarding their experience using LFSPRO following their genetic counseling appointments.

Results

LFSPRO's sensitivity and specificity were 0.529 and 0.781 compared to Chompret's respective 0.235 and 0.677. Additionally, LFSPRO had a positive predictive value (PPV) of 0.30 compared to Chompret's 0.114. LFSPRO's risk prediction was concordant with genetic testing results in 75% of probands. Eighty-one percent of GC surveys reported LFSPRO being concordant with the GC's expectations and 75% would feel comfortable sharing the results with patients.

Conclusion

LFSPRO showed improved sensitivity and specificity compared to Chompret criteria and GCs report a positive experience with LFSPRO. LFSPRO can be used to increase access to genetic testing for patients at risk for LFS and could help healthcare providers give more direct risk assessments regarding LFS testing and management for patients.

A Scoping Review of Personalized, Interactive, Web-Based Clinical Decision Tools Available for Breast Cancer Prevention and Screening in the United States

Introduction. Personalized web-based clinical decision tools for breast cancer prevention and screening could address knowledge gaps, enhance patient autonomy in shared decision-making, and promote equitable care. The purpose of this review was to present evidence on the availability, usability, feasibility, acceptability, quality, and uptake of breast cancer prevention and screening tools to support their integration into clinical care. Methods. We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews Checklist to conduct this review. We searched 6 databases to identify literature on the development, validation, usability, feasibility, acceptability testing, and uptake of the tools into practice settings. Quality assessment for each tool was conducted using the International Patient Decision Aid Standard instrument, with quality scores ranging from 0 to 63 (lowest-highest). Results. We identified 10 tools for breast cancer prevention and 9 tools for screening. The tools included individual (e.g., age), clinical (e.g., genomic risk factors), and health behavior (e.g., alcohol use) characteristics. Fourteen tools included race/ethnicity, but no tool incorporated contextual factors (e.g., insurance, access) associated with breast cancer. All tools were internally or externally validated. Six tools had undergone usability testing in samples including White (median, 71%; range, 9%-96%), insured (99%; 97%-100%) women, with college education or higher (60%; 27%-100%). All of the tools were developed and tested in academic settings. Seven (37%) tools showed potential evidence of uptake in clinical practice. The tools had an average quality assessment score of 21 (range, 9-39). Conclusions. There is limited evidence on testing and uptake of breast cancer prevention and screening tools in diverse clinical settings. The development, testing, and integration of tools in academic and nonacademic settings could potentially improve uptake and equitable access to these tools.

Highlights

There were 19 personalized, interactive, Web-based decision tools for breast cancer prevention and screening.Breast cancer outcomes were personalized based on individual clinical characteristics (e.g., age, medical history), genomic risk factors (e.g., BRCA1/2), race and ethnicity, and health behaviors (e.g., smoking). The tools did not include contextual factors (e.g., insurance status, access to screening facilities) that could potentially contribute to breast cancer outcomes.Validation, usability, acceptability, and feasibility testing were conducted mostly among White and/or insured patients with some college education (or higher) in academic settings. There was limited evidence on testing and uptake of the tools in nonacademic clinical settings.

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.