Abstract P4-04-14: Economic analysis of germline genetic testing to assess for hereditary breast cancer: a systematic review
Bibliographic record
Abstract
Abstract Introduction: Germline genetic testing (GGT) is included in clinical guidelines and has steadily increased in use to inform treatment decisions for breast cancer patients and those with genetic predisposition. The expanding implementation has magnified the influence of GGT on the economics of breast cancer care worldwide. The objective of this systematic review was to evaluate the economic impact of GGT among adults diagnosed with breast cancer, those at increased risk of breast cancer, and in the general population. Methods: This analysis was part of a broader study following PRISMA methodology. PubMed-Medline and Embase were searched for manuscripts published after January 2013 that reported the clinical, economic, and humanistic outcomes of GGT in patients with breast cancer and those at risk of breast cancer. The present analysis summarizes a subset of articles specifically addressing the economic value of GGT. Results: The initial search, inclusive of all outcomes, identified 10,198 studies. Of the 231 articles extracted for data analysis, 30 studies evaluated economic outcomes. Twenty-four of these were cost-effectiveness analyses related to testing for hereditary breast cancer; the remaining six studies were cost comparison analyses or budget impact models. Of the 24 cost-effectiveness studies, five were multinational and evaluated the cost-effectiveness of GGT in 12 countries (US, UK, Germany, Canada, Norway, Netherlands, Spain, Israel, India, Brazil, China, and Malaysia). The US (n=10) and the UK (n=7) were studied most frequently; however, there was notable heterogeneity derived from different international healthcare systems and varying patient populations. The studies modeled the effect of testing patients diagnosed with breast cancer (n=7) or breast/ovarian cancer (n=1), testing individuals with an increased risk of breast cancer (n=6), and population-specific genetic screening (n=10). Both single-syndrome (BRCA1/2; n=17) and multigene testing strategies (n=7) were investigated. Thirteen studies employed a Markov model. Close to half of the studies (n=11/24) considered cascade testing, and almost all (n=22/24) considered risk-reducing surgery (breast and/or ovarian). All studies were analyzed from a payer perspective; six used both payer and societal perspectives. Studies evaluated cost-effectiveness against various willingness-to-pay (WTP) thresholds. Each study found at least one cost-effective strategy for all countries and perspectives. Four studies presented GGT strategies that were cost-saving. Conclusions: This analysis presents the economic impact of GGT for hereditary breast cancer syndromes with global representation and a range of testing strategies. In all settings, the analyses found GGT to be cost-effective, supporting the positive economic impact of GGT for hereditary breast cancer. Opportunities for further exploration include the added value of cascade testing and use of multigene panels, as well as the evaluation of economic impact from the patient perspective. Citation Format: Heather Johnson, Deborah Hartzfeld, Mary Linton Peters, Jade Xiao, Bhakti Mody, Carol Kirshner, Feyza Sancar, Brandie Heald, Joyce Kong, Jecinta Scott, Gebra Cuyún Carter. Economic analysis of germline genetic testing to assess for hereditary breast cancer: a systematic review [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P4-04-14.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".