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Abstract
Introduction Cardiovascular 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.
- breast cancer
- cardiovascular disease
- coronary artery calcifications
- prediction
- deep learning algorithm
- radiotherapy planning CT-scan
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Footnotes
Contributors MJE, II, SGMV, HJGDB, SAMG, NL, MGAS, AJT, JP, HM, J-PP, HMV set up the study and protocols. MJE, II, SGMV, SAMG and HMV drafted the manuscript. All authors read and approved the final manuscript.
Funding The work is supported by the Dutch Cancer Society (grant number UU 2015-7947).
Competing interests II: disclosures not related to the present article: II received research grants from 1) PIE Medical Imaging BV; 2) the Netherlands Organization for Health Research and Development (ZonMw) with participation of PIE Medical Imaging BV; 3) the Dutch Technology Foundation (STW) within Deep Learning for Medical Image Analysis (DLMedIA) with participation of PIE Medical Imaging BV and Philips Healthcare; 4) Dutch Technology Foundation (STW) with participation of PIE Medical Imaging BV and 3mensio Medical Imaging. In addition, II has a patent, US Patent Application Number 15/933854, with royalties paid and she is scientific founder and a shareholder of Quantib-U BV. JP: disclosures not related to the present article: JP received grants from 1) Accuray, Sunnyvale, California, USA; 2) Elekta AB,Stockholm, Sweden.
Ethics approval The study protocol has been reviewed by the Institutional Review Boards of the University Medical Center Utrecht (reference number: 16– 721 /C), Erasmus MC Cancer Institute Rotterdam (MEC-2017–1125) and Radboudumc (2017–3847).
Provenance and peer review Not commissioned; externally peer reviewed.
Collaborators Bragatston study group: R M Bijlsma; E L A Blezer; M L Bots; H J Bretveld; M J Hooning; L Incrocci; P A de Jong; T Leiner; J J van Tol-Geerdink; I Vaartjes; W B Veldhuis; J Verloop; M A Viergever; F L J Visseren; H Wessels.
Patient consent for publication Not required.