Measurement challenge: protocol for international case–control comparison of mammographic measures that predict breast cancer risk
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...
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2019-12-01
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| author | Jennifer Stone John Shepherd Giske Ursin John L Hopper Chao Wang Evenda Dench Daniela Bond-Smith Ellie Darcey Grant Lee Ye K Aung Ariane Chan Jack Cuzick Ze Y Ding Chris F Evans Jennifer Harvey Ralph Highnam Meng-Kang Hsieh Despina Kontos Shuai Li Shivaani Mariapun Carolyn Nickson Tuong L Nguyen Said Pertuz Pietro Procopio Nadia Rajaram Kathy Repich Maxine Tan Soo-Hwang Teo Nhut Ho Trinh Isabel dos-Santos-Silva Valerie McCormack Mads Nielsen |
| author_facet | Jennifer Stone John Shepherd Giske Ursin John L Hopper Chao Wang Evenda Dench Daniela Bond-Smith Ellie Darcey Grant Lee Ye K Aung Ariane Chan Jack Cuzick Ze Y Ding Chris F Evans Jennifer Harvey Ralph Highnam Meng-Kang Hsieh Despina Kontos Shuai Li Shivaani Mariapun Carolyn Nickson Tuong L Nguyen Said Pertuz Pietro Procopio Nadia Rajaram Kathy Repich Maxine Tan Soo-Hwang Teo Nhut Ho Trinh Isabel dos-Santos-Silva Valerie McCormack Mads Nielsen |
| author_sort | Jennifer Stone |
| collection | DOAJ |
| description | 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). |
| format | Article |
| id | doaj-art-3dc509e398bb4a458ddb1ba60ffd337a |
| institution | DOAJ |
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| language | English |
| publishDate | 2019-12-01 |
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| spelling | doaj-art-3dc509e398bb4a458ddb1ba60ffd337a2025-08-20T02:50:55ZengBMJ Publishing GroupBMJ Open2044-60552019-12-0191210.1136/bmjopen-2019-031041Measurement challenge: protocol for international case–control comparison of mammographic measures that predict breast cancer riskJennifer Stone0John Shepherd1Giske Ursin2John L Hopper3Chao Wang4Evenda Dench5Daniela Bond-Smith6Ellie Darcey7Grant Lee8Ye K Aung9Ariane Chan10Jack Cuzick11Ze Y Ding12Chris F Evans13Jennifer Harvey14Ralph Highnam15Meng-Kang Hsieh16Despina Kontos17Shuai Li18Shivaani Mariapun19Carolyn Nickson20Tuong L Nguyen21Said Pertuz22Pietro Procopio23Nadia Rajaram24Kathy Repich25Maxine Tan26Soo-Hwang Teo27Nhut Ho Trinh28Isabel dos-Santos-Silva29Valerie McCormack30Mads Nielsen311 JBI, The University of Adelaide Faculty of Health and Medical Sciences, Adelaide, South Australia, Australia1 Emergency Department, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK15 Cancer Registry of Norway, Oslo, Norway3 Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, AustraliaMedical Insurance Office, The First Affiliated Hospital of Harbin Medical University, Harbin, China1 Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia2 School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia1 Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia3 Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia3 Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia4 Science and Technology, Volpara Health Technologies, Wellington, New Zealand5 Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK6 Electrical and Computer Systems Engineering, School of Engineering, Monash University - Malaysia Campus, Bandar Sunway, Selangor, Malaysia3 Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, AustraliaHarefield Pulmonary Rehabilitation Unit, Guy`s and St Thomas` Hospitals NHS Trust, London, UK4 Science and Technology, Volpara Health Technologies, Wellington, New Zealand8 Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA8 Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USADepartment of Neonatology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China1 Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia3 Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia3 Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia13 Connectivity and Signal Processing group, Universidad Industrial de Santander, Bucaramanga, Colombia3 Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia9 Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia7 Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA6 Electrical and Computer Systems Engineering, School of Engineering, Monash University - Malaysia Campus, Bandar Sunway, Selangor, Malaysia9 Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia3 Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia17 Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, London, UK2 Branches of Environment and Lifestyle Epidemiology, Cancer Surveillance and Genomics, International Agency for Research on Cancer, Lyon, France19 University of Copenhagen, Kobenhavns, DenmarkIntroduction 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).https://bmjopen.bmj.com/content/9/12/e031041.full |
| spellingShingle | Jennifer Stone John Shepherd Giske Ursin John L Hopper Chao Wang Evenda Dench Daniela Bond-Smith Ellie Darcey Grant Lee Ye K Aung Ariane Chan Jack Cuzick Ze Y Ding Chris F Evans Jennifer Harvey Ralph Highnam Meng-Kang Hsieh Despina Kontos Shuai Li Shivaani Mariapun Carolyn Nickson Tuong L Nguyen Said Pertuz Pietro Procopio Nadia Rajaram Kathy Repich Maxine Tan Soo-Hwang Teo Nhut Ho Trinh Isabel dos-Santos-Silva Valerie McCormack Mads Nielsen Measurement challenge: protocol for international case–control comparison of mammographic measures that predict breast cancer risk BMJ Open |
| title | Measurement challenge: protocol for international case–control comparison of mammographic measures that predict breast cancer risk |
| title_full | Measurement challenge: protocol for international case–control comparison of mammographic measures that predict breast cancer risk |
| title_fullStr | Measurement challenge: protocol for international case–control comparison of mammographic measures that predict breast cancer risk |
| title_full_unstemmed | Measurement challenge: protocol for international case–control comparison of mammographic measures that predict breast cancer risk |
| title_short | Measurement challenge: protocol for international case–control comparison of mammographic measures that predict breast cancer risk |
| title_sort | measurement challenge protocol for international case control comparison of mammographic measures that predict breast cancer risk |
| url | https://bmjopen.bmj.com/content/9/12/e031041.full |
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