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Measurement challenge: protocol for international case–control comparison of mammographic measures that predict breast cancer risk
  1. Evenda Dench1,
  2. Daniela Bond-Smith2,
  3. Ellie Darcey1,
  4. Grant Lee3,
  5. Ye K Aung3,
  6. Ariane Chan4,
  7. Jack Cuzick5,
  8. Ze Y Ding6,
  9. Chris F Evans3,
  10. Jennifer Harvey7,
  11. Ralph Highnam4,
  12. Meng-Kang Hsieh8,
  13. Despina Kontos8,
  14. Shuai Li3,
  15. Shivaani Mariapun9,10,
  16. Carolyn Nickson3,11,
  17. Tuong L Nguyen3,
  18. Said Pertuz12,13,
  19. Pietro Procopio3,11,
  20. Nadia Rajaram9,10,
  21. Kathy Repich7,
  22. Maxine Tan6,14,
  23. Soo-Hwang Teo9,
  24. Nhut Ho Trinh3,
  25. Giske Ursin15,
  26. Chao Wang16,
  27. Isabel dos-Santos-Silva17,
  28. Valerie McCormack18,
  29. Mads Nielsen19,
  30. John Shepherd20,
  31. John L Hopper3,
  32. Jennifer Stone1
  1. 1 Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia
  2. 2 School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia
  3. 3 Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
  4. 4 Science and Technology, Volpara Health Technologies, Wellington, New Zealand
  5. 5 Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
  6. 6 Electrical and Computer Systems Engineering, School of Engineering, Monash University - Malaysia Campus, Bandar Sunway, Selangor, Malaysia
  7. 7 Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
  8. 8 Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
  9. 9 Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
  10. 10 Department of Applied Mathematics, Faculty of Engineering, University of Nottingham - Malaysia Campus, Semenyih, Selangor, Malaysia
  11. 11 Cancer Research Division, Cancer Council New South Wales, Sydney, New South Wales, Austalia
  12. 12 Laboratory of Signal Processing, Tampere University of Technology, Tampere, Pirkanmaa, Finland
  13. 13 Connectivity and Signal Processing group, Universidad Industrial de Santander, Bucaramanga, Colombia
  14. 14 School of Electrical and Computer Engineering, University of Oklahoma Norman Campus, Norman, Oklahoma, USA
  15. 15 Cancer Registry of Norway, Oslo, Norway
  16. 16 Faculty of Health, Social Care and Education, Kingston University and St George's, University of London, Kingston-Upon-Thames, London, UK
  17. 17 Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, London, UK
  18. 18 Section of Environment and Radiation, International Agency for Research on Cancer, IARC, Lyon, France
  19. 19 University of Copenhagen, Kobenhavns, Denmark
  20. 20 University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
  1. Correspondence to Dr Jennifer Stone; jennifer.stone{at}uwa.edu.au

Abstract

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).

  • mammogram
  • mammographic density
  • breast cancer
  • Breast imaging
  • Breast tumours

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Twitter @excel_wang, @DrJenniferStone

  • Contributors JeS, JLH, JoS, IdSS, MN and VM are the Organizing Committee of the measurement challenge and made substantial contributions to the conception or design of the work. JeH, SM, NR, KR, S-HT, GU, IdSS and JLH are phase 1 contributors of images and data who made substantial contributions to the acquisition of data for the work. GL facilitates the capture, renaming, security and distribution of images in the challenge, and is involved in design and conduct of the study. DB-S and ElD performed the statistical analyses and made a substantial contribution to analysis of the work. YKA, AC, JC, ZYD, CFE, RH, M-KH, DK, SL, CN, TLN, SP, PP, MT, NHT, CW, MN and JLH are phase 1 challengers who have made a substantial contribution to interpretation of data for the work. EvD and JeS prepared the manuscript and all authors revised it critically for important intellectual content. All authors provided final approval of the version to be published and agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval Ethics approval for the Challenge is held at University of Melbourne (HREC ID 0931343.3).

  • Provenance and peer review Not commissioned; externally peer reviewed.

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