Journal Article

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

Procopio, Pietro, Cuzick, Jack, Chan, Ariane, Aung, Ye K, Nickson, Carolyn, Li, Shuai, Evans, Chris F, Stone, Jennifer, Highnam, Ralph, Hsieh, Meng-Kang, Trinh, Nhut Ho, Ursin, Giske, Nguyen, Tuong L, Darcey, Ellie, McCormack, Valerie, Repich, Kathy, Shepherd, John, Kontos, Despina, Tan, Maxine, Harvey, Jennifer, Bond-Smith, Daniela, Teo, Soo-Hwang, Hopper, John L, Pertuz, Said, Nielsen, Mads, dos-Santos-Silva, Isabel, Ding, Ze Y, Lee, Grant, Rajaram, Nadia, Wang, Chao, Mariapun, Shivaani, Dench, Evenda

2019

Overview

IntroductionFor 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 analysisThe 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 disseminationChallengers 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).

• Publisher: British Medical Journal Publishing Group,BMJ Publishing Group LTD,BMJ Publishing Group
• Subject: Algorithms / Automation / Body mass index / Breast cancer / Breast Density / Breast imaging / Breast Neoplasms - diagnostic imaging / Breast tumours / Case-Control Studies / Clinical Protocols / Collaboration / Datasets / Female / Health risk assessment / Humans / International Cooperation / mammogram / mammographic density / Mammography / Methods / Predictive Value of Tests / Radiology and Imaging / Risk Assessment - methods