September 07, 2016 SEATTLE--(BUSINESS WIRE)--A coalition of oncology and technology partners led by Sage Bionetworks and DREAM Challenges today announced the opening of the training phase for the Digital Mammography DREAM Challenge, an open-science data competition designed to improve the accuracy of mammography screening. With funding from Laura and John Arnold Foundation (LJAF), the Challenge will award up to $1.2 million to data scientists, researchers, and coding experts who develop predictive algorithms that achieve milestone goals related to reducing the recall rate of mammography screening. Interested participants can sign up at https://www.synapse.org/Digital_Mammography_DREAM_Challenge.

The coalition supporting the Challenge as organizers, sponsors, partners and advisors from the health, tech, regulatory and for-profit competition sectors includes: Amazon Web Services (AWS), FDA, Group Health Cooperative, IBM, Icahn School of Medicine at Mount Sinai, Innocentive, NCI, Radish Medical, and Seattle Cancer Care Alliance.

Each year, more than 40 million women in the United States undergo routine mammogram testing to screen for breast cancer. Mammograms are widely considered to be the most accessible and cost-effective breast cancer screening method. However, the United States Preventive Services Task Force and the American Cancer Society recently issued changes to recommendations regarding start age and frequency of screening. These changes are due, in part, to the large number of false-positive mammograms. One in 10 women undergoing screening mammography are recalled for diagnostic workup, of which fewer than 5 percent will eventually be found to have cancer. Recalled patients often experience stress and additional medical costs, and some require interventions including unnecessary biopsies.

The Digital Mammography DREAM Challenge, running from June 2016 through mid-2017, will seek to attract data experts from both inside and outside the medical field to develop predictive algorithms that will reduce false-positive mammograms while maintaining or improving cancer detection. Participants will be asked to create algorithms that will help doctors determine whether a patient’s mammogram has a high or low likelihood of harboring a breast cancer, and whether or not a patient should undergo additional testing. New algorithms may allow doctors to customize screening regimens for patients and identify women who would benefit from more or less frequent screening.

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