Measurement challenge: protocol for international case-control comparison of mammographic measures that predict breast cancer risk.

Autor:	Dench E; Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia., Bond-Smith D; School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia., Darcey E; Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia., Lee G; Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia., Aung YK; Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia., Chan A; Science and Technology, Volpara Health Technologies, Wellington, New Zealand., Cuzick J; Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK., Ding ZY; Electrical and Computer Systems Engineering, School of Engineering, Monash University - Malaysia Campus, Bandar Sunway, Selangor, Malaysia., Evans CF; Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia., Harvey J; Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA., Highnam R; Science and Technology, Volpara Health Technologies, Wellington, New Zealand., Hsieh MK; Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA., Kontos D; Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA., Li S; Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia., Mariapun S; Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia.; Department of Applied Mathematics, Faculty of Engineering, University of Nottingham - Malaysia Campus, Semenyih, Selangor, Malaysia., Nickson C; Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia.; Cancer Research Division, Cancer Council New South Wales, Sydney, New South Wales, Austalia., Nguyen TL; Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia., Pertuz S; Laboratory of Signal Processing, Tampere University of Technology, Tampere, Pirkanmaa, Finland.; Connectivity and Signal Processing group, Universidad Industrial de Santander, Bucaramanga, Colombia., Procopio P; Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia.; Cancer Research Division, Cancer Council New South Wales, Sydney, New South Wales, Austalia., Rajaram N; Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia.; Department of Applied Mathematics, Faculty of Engineering, University of Nottingham - Malaysia Campus, Semenyih, Selangor, Malaysia., Repich K; Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA., Tan M; Electrical and Computer Systems Engineering, School of Engineering, Monash University - Malaysia Campus, Bandar Sunway, Selangor, Malaysia.; School of Electrical and Computer Engineering, University of Oklahoma Norman Campus, Norman, Oklahoma, USA., Teo SH; Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia., Trinh NH; Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia., Ursin G; Cancer Registry of Norway, Oslo, Norway., Wang C; Faculty of Health, Social Care and Education, Kingston University and St George's, University of London, Kingston-Upon-Thames, London, UK., Dos-Santos-Silva I; Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, London, UK., McCormack V; Section of Environment and Radiation, International Agency for Research on Cancer, IARC, Lyon, France., Nielsen M; University of Copenhagen, Kobenhavns, Denmark., Shepherd J; University of Hawai'i Cancer Center, Honolulu, Hawaii, USA., Hopper JL; Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia., Stone J; Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia jennifer.stone@uwa.edu.au.
Jazyk:	angličtina
Zdroj:	BMJ open [BMJ Open] 2019 Dec 31; Vol. 9 (12), pp. e031041. Date of Electronic Publication: 2019 Dec 31.
DOI:	10.1136/bmjopen-2019-031041
Abstrakt:	Introduction: For women of the same age and body mass index, increased mammographic density is one of the strongest predictors of breast cancer risk. There are multiple methods of measuring mammographic density and other features in a mammogram that could potentially be used in a screening setting to identify and target women at high risk of developing breast cancer. However, it is unclear which measurement method provides the strongest predictor of breast cancer risk. Methods and Analysis: The measurement challenge has been established as an international resource to offer a common set of anonymised mammogram images for measurement and analysis. To date, full field digital mammogram images and core data from 1650 cases and 1929 controls from five countries have been collated. The measurement challenge is an ongoing collaboration and we are continuing to expand the resource to include additional image sets across different populations (from contributors) and to compare additional measurement methods (by challengers). The intended use of the measurement challenge resource is for refinement and validation of new and existing mammographic measurement methods. The measurement challenge resource provides a standardised dataset of mammographic images and core data that enables investigators to directly compare methods of measuring mammographic density or other mammographic features in case/control sets of both raw and processed images, for the purposes of the comparing their predictions of breast cancer risk. Ethics and Dissemination: Challengers and contributors are required to enter a Research Collaboration Agreement with the University of Melbourne prior to participation in the measurement challenge. The Challenge database of collated data and images are stored in a secure data repository at the University of Melbourne. Ethics approval for the measurement challenge is held at University of Melbourne (HREC ID 0931343.3). Competing Interests: Competing interests: None declared. (© 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
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