Initial Staging 18F-FDG PET/CT for Coronary Artery Calcium Scoring to Assess Cardiovascular Risk in Women with Breast Cancer
Fleming, M. R.; Tayon, K. G.; Schneider, A.; McPherson, A. D.; Bianco, S. M.; Parent, E. E.; Sharma, A.; Lin, G.; Norton, N.; Ray, J. C.
Show abstract
Background. Cardiovascular disease is a leading cause of death among women with breast cancer, and the 2026 ACC/AHA dyslipidemia guideline endorses coronary artery calcium (CAC) scoring to guide statin therapy before cardiotoxic treatment. Breast cancer patients routinely undergo staging 18F-fluorodeoxyglucose PET/CT, whose low-dose CT visualizes the coronary arteries, thus enabling CAC quantification at no additional cost or radiation. Methods. In this single-center retrospective study, consecutive women with newly diagnosed breast cancer undergoing staging 18F-FDG PET/CT (2009?2021) had semi-automated Agatston CAC scoring performed on the low-dose CT and were stratified by CAC presence (CAC-P) versus absence (CAC-A). We assessed a composite of cardiac diagnostic testing (stress testing, coronary CT angiography, invasive angiography), clinical events, and reclassification of statin eligibility per ACC/AHA guideline thresholds in a prevention-eligible subgroup. Results. Among 276 women (mean age 55.5 years; median follow-up 7.1 years), CAC was present in 68 (25%) but was clinically reported in only 5.4%. CAC-P was associated with more cardiac testing (34% vs 12%; age-adjusted hazard ratio 2.75, 95% CI 1.43?5.28) and, though underpowered, with more atherosclerotic events (7.4% vs 1.4%), but not with the all-cause composite. In the prevention-eligible subgroup (n=39), CAC scoring would have changed statin eligibility in 64%, initiating therapy in 62% of CAC-P women and supporting de-prescribing in 67% of CAC-A women. Conclusions. CAC can be feasibly quantified from staging PET/CT in women with breast cancer and would frequently reclassify statin eligibility at no additional cost or radiation, yet is rarely reported.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- BREAst screening Tailored for HEr (BREATHE) - A Study Protocol On Personalised Risk-based Breast Cancer Screening Programme 91%
- Prognostic value of the Residual Cancer Burden index according to breast cancer subtype: validation on a cohort of BC patients treated by neoadjuvant chemotherapy. 91%
- Postmastectomy Radiotherapy in pN1 Breast Cancer: Survival Outcomes and Prognostic Factors From a Single-Institution Cohort 91%
Similar papers in this journal
- Investigating the relationship between breast cancer risk factors and an AI-generated mammographic texture feature in the Nurses' Health Study II 93%
- An updated PREDICT breast cancer prognostic model including the benefits and harms of radiotherapy 92%
- Characterization of Body Composition Dynamics Throughout Treatment in Patients with Early-Stage Breast Cancer 91%
Similar papers in this journal
- A multicenter prospective randomized controlled trial of high sensitivity cardiac troponin I-guided combination angiotensin receptor blockade and beta-blocker therapy to prevent anthracycline cardiotoxicity: the Cardiac CARE trial 90%
- High-sensitivity cardiac troponin on presentation to rule out myocardial infarction: a stepped-wedge cluster randomised controlled trial 88%
- Prevalence of Coronary Atherosclerosis in Master Female Endurance Athletes 88%
Similar papers in this journal
Similar papers in this journal
- Incorporating Polygenic Risk Scores and Nongenetic Risk Factors for Breast Cancer Risk Prediction among Asian Women, Results from Asia Breast Cancer Consortium 91%
- Venous Thromboembolism and the Effects of Statin and Hormone Therapy: A Case-Control Study of 250,000 Women 50-64 years of age 86%
- Missing data in the medical record for oncology patients: prevalence and association with outcomes 85%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.