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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 t...

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Autores principales: Dench, Evenda, Bond-Smith, Daniela, Darcey, Ellie, Lee, Grant, Aung, Ye K, Chan, Ariane, Cuzick, Jack, Ding, Ze Y, Evans, Chris F, Harvey, Jennifer, Highnam, Ralph, Hsieh, Meng-Kang, Kontos, Despina, Li, Shuai, Mariapun, Shivaani, Nickson, Carolyn, Nguyen, Tuong L, Pertuz, Said, Procopio, Pietro, Rajaram, Nadia, Repich, Kathy, Tan, Maxine, Teo, Soo-Hwang, Trinh, Nhut Ho, Ursin, Giske, Wang, Chao, dos-Santos-Silva, Isabel, McCormack, Valerie, Nielsen, Mads, Shepherd, John, Hopper, John L, Stone, Jennifer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6955467/
https://www.ncbi.nlm.nih.gov/pubmed/31892647
http://dx.doi.org/10.1136/bmjopen-2019-031041
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author Dench, Evenda
Bond-Smith, Daniela
Darcey, Ellie
Lee, Grant
Aung, Ye K
Chan, Ariane
Cuzick, Jack
Ding, Ze Y
Evans, Chris F
Harvey, Jennifer
Highnam, Ralph
Hsieh, Meng-Kang
Kontos, Despina
Li, Shuai
Mariapun, Shivaani
Nickson, Carolyn
Nguyen, Tuong L
Pertuz, Said
Procopio, Pietro
Rajaram, Nadia
Repich, Kathy
Tan, Maxine
Teo, Soo-Hwang
Trinh, Nhut Ho
Ursin, Giske
Wang, Chao
dos-Santos-Silva, Isabel
McCormack, Valerie
Nielsen, Mads
Shepherd, John
Hopper, John L
Stone, Jennifer
author_facet Dench, Evenda
Bond-Smith, Daniela
Darcey, Ellie
Lee, Grant
Aung, Ye K
Chan, Ariane
Cuzick, Jack
Ding, Ze Y
Evans, Chris F
Harvey, Jennifer
Highnam, Ralph
Hsieh, Meng-Kang
Kontos, Despina
Li, Shuai
Mariapun, Shivaani
Nickson, Carolyn
Nguyen, Tuong L
Pertuz, Said
Procopio, Pietro
Rajaram, Nadia
Repich, Kathy
Tan, Maxine
Teo, Soo-Hwang
Trinh, Nhut Ho
Ursin, Giske
Wang, Chao
dos-Santos-Silva, Isabel
McCormack, Valerie
Nielsen, Mads
Shepherd, John
Hopper, John L
Stone, Jennifer
author_sort Dench, Evenda
collection PubMed
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).
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spelling pubmed-69554672020-01-27 Measurement challenge: protocol for international case–control comparison of mammographic measures that predict breast cancer risk Dench, Evenda Bond-Smith, Daniela Darcey, Ellie Lee, Grant Aung, Ye K Chan, Ariane Cuzick, Jack Ding, Ze Y Evans, Chris F Harvey, Jennifer Highnam, Ralph Hsieh, Meng-Kang Kontos, Despina Li, Shuai Mariapun, Shivaani Nickson, Carolyn Nguyen, Tuong L Pertuz, Said Procopio, Pietro Rajaram, Nadia Repich, Kathy Tan, Maxine Teo, Soo-Hwang Trinh, Nhut Ho Ursin, Giske Wang, Chao dos-Santos-Silva, Isabel McCormack, Valerie Nielsen, Mads Shepherd, John Hopper, John L Stone, Jennifer BMJ Open Radiology and Imaging 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). BMJ Publishing Group 2019-12-31 /pmc/articles/PMC6955467/ /pubmed/31892647 http://dx.doi.org/10.1136/bmjopen-2019-031041 Text en © 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. 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/.
spellingShingle Radiology and Imaging
Dench, Evenda
Bond-Smith, Daniela
Darcey, Ellie
Lee, Grant
Aung, Ye K
Chan, Ariane
Cuzick, Jack
Ding, Ze Y
Evans, Chris F
Harvey, Jennifer
Highnam, Ralph
Hsieh, Meng-Kang
Kontos, Despina
Li, Shuai
Mariapun, Shivaani
Nickson, Carolyn
Nguyen, Tuong L
Pertuz, Said
Procopio, Pietro
Rajaram, Nadia
Repich, Kathy
Tan, Maxine
Teo, Soo-Hwang
Trinh, Nhut Ho
Ursin, Giske
Wang, Chao
dos-Santos-Silva, Isabel
McCormack, Valerie
Nielsen, Mads
Shepherd, John
Hopper, John L
Stone, Jennifer
Measurement challenge: protocol for international case–control comparison of mammographic measures that predict breast cancer risk
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
topic Radiology and Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6955467/
https://www.ncbi.nlm.nih.gov/pubmed/31892647
http://dx.doi.org/10.1136/bmjopen-2019-031041
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