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.

Autor:	Emaus MJ; Imaging Division, University Medical Center Utrecht, Utrecht, The Netherlands., Išgum I; Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands., van Velzen SGM; Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands., van den Bongard HJGD; Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands., Gernaat SAM; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands., Lessmann N; Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands., Sattler MGA; Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands., Teske AJ; Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands., Penninkhof J; Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands., Meijer H; Department of Radiation Oncology, Radboudumc, Nijmegen, The Netherlands., Pignol JP; Department of Radiation Oncology, Dalhousie University, Halifax, Nova Scotia, Canada., Verkooijen HM; Imaging Division, University Medical Center Utrecht, Utrecht, The Netherlands.; Utrecht University, Utrecht, The Netherlands.
Jazyk:	angličtina
Zdroj:	BMJ open [BMJ Open] 2019 Jul 27; Vol. 9 (7), pp. e028752. Date of Electronic Publication: 2019 Jul 27.
DOI:	10.1136/bmjopen-2018-028752
Abstrakt:	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. Competing Interests: 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. (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu