ArticleBMJ open2019
Bragatston study protocol: a multicentre cohort study on automated quantification of cardiovascular calcifications on radiotherapy planning CT scans for cardiovascular risk prediction in patients with breast cancer.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03206333 (Automated Quantification of Coronary Artery Calcifications on Radiotherapy Planning CTs for Cardiovascular Risk Prediction in Breast Cancer Patients), which is not on this map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Automated Quantification of Coronary Artery Calcifications on Radiotherapy Planning CTs for Cardiovascular Risk Prediction in Breast Cancer Patients: the BRAGATSTON Study
Who cites it
15 citing papers in PubMed, 25 citations in OpenAlex.
- Artificial Intelligence and the Expanding Universe of Cardio-Oncology: Beyond Detection Toward Prediction and Prevention of Therapy-Related Cardiotoxicity-A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2026Review
- Evidence of different predictors for different types of cardiotoxicities after hypo-fractionated whole breast radiotherapy.Frontiers in oncology · 2026Article
- Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.Cancer medicine · 2025Article
- The Role of Advanced Cardiac Imaging in Monitoring Cardiovascular Complications in Patients with Extracardiac Tumors: A Descriptive Review.Journal of cardiovascular development and disease · 2024Review
- Cancer therapy-related cardiac dysfunction and the role of cardiovascular imaging: systemic review and opinion paper from the Working Group on Cardio-Oncology of the Korean Society of Cardiology.Journal of cardiovascular imaging · 2024Review
- Competing risk analysis of cardiovascular disease risk in breast cancer patients receiving a radiation boost.Cardio-oncology (London, England) · 2024Article
- Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline.Reviews in cardiovascular medicine · 2023Review
- Academic patterns of practice regarding CT simulation scans and radiology review.Journal of medical imaging and radiation sciences · 2022Article
- Coronary Artery Computed Tomography Angiography for Preventing Cardio-Cerebrovascular Disease: Observational Cohort Study Using the Observational Health Data Sciences and Informatics' Common Data Model.JMIR medical informatics · 2022Article
- Generative models for reproducible coronary calcium scoring.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Next Generation Risk Markers in Preventive Cardio-oncology.Current atherosclerosis reports · 2022Review
- A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- Cardiac Computed Tomography in Cardio-Oncology:JACC. CardioOncology · 2021Review
- Identification of Risk of Cardiovascular Disease by Automatic Quantification of Coronary Artery Calcifications on Radiotherapy Planning CT Scans in Patients With Breast Cancer.JAMA oncology · 2021Article
- Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey.Frontiers in cardiovascular medicine · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionCardiovascular disease (CVD) is an important cause of death in breast cancer survivors. Some breast cancer treatments including anthracyclines, trastuzumab and radiotherapy can increase the risk of CVD, especially for patients with pre-existing CVD risk factors. Early identification of patients at increased CVD risk may allow switching to less cardiotoxic treatments, active surveillance or treatment of CVD risk factors. One of the strongest independent CVD risk factors is the presence and extent of coronary artery calcifications (CAC). In clinical practice, CAC are generally quantified on ECG-triggered cardiac CT scans. Patients with breast cancer treated with radiotherapy routinely undergo radiotherapy planning CT scans of the chest, and those scans could provide the opportunity to routinely assess CAC before a potentially cardiotoxic treatment. The Bragatston study aims to investigate the association between calcifications in the coronary arteries, aorta and heart valves (hereinafter called 'cardiovascular calcifications') measured automatically on planning CT scans of patients with breast cancer and CVD risk. METHODS AND ANALYSIS: In a first step, we will optimise and validate a deep learning algorithm for automated quantification of cardiovascular calcifications on planning CT scans of patients with breast cancer. Then, in a multicentre cohort study (University Medical Center Utrecht, Utrecht, Erasmus MC Cancer Institute, Rotterdam and Radboudumc, Nijmegen, The Netherlands), the association between cardiovascular calcifications measured on planning CT scans of patients with breast cancer (n≈16 000) and incident (non-)fatal CVD events will be evaluated. To assess the added predictive value of these calcifications over traditional CVD risk factors and treatment characteristics, a case-cohort analysis will be performed among all cohort members diagnosed with a CVD event during follow-up (n≈200) and a random sample of the baseline cohort (n≈600). ETHICS AND DISSEMINATION: The Institutional Review Boards of the participating hospitals decided that the Medical Research Involving Human Subjects Act does not apply. Findings will be published in peer-reviewed journals and presented at conferences. TRIAL REGISTRATION NUMBER: NCT03206333.
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