# DREAM Challenge results: Can machine learning help improve accuracy in breast cancer screening?

Breast Cancer is the most common cancer in women. It is estimated that one out of eight women will be diagnosed with breast cancer in their lifetime. The good news is that 99 percent of women whose breast cancer was detected early (stage 1 or 0) survive beyond five years after diagnosis 1 , leading countries around the world to implement breast cancer screening programs for early detection.

Mammography screening, however, is not a perfect procedure. Of the 40 million women undergoing annual mammogram testing to screen for breast cancer in the United States, an estimated 4 million women are unnecessarily called back for further testing. This high rate of false-positive exams leads to added anxiety, unnecessary biopsies for the individual, and may lead to overtreatment such as surgical excision. There is also an estimated rate of 13 percent false negatives — that is, cases of breast cancer that radiologists fail to detect2.

At IBM Research, we’ve made strides in harnessing cognitive computing and machine learning to examine medical images for insights that help health professionals treat patients. We therefore wondered whether these technologies could be used to increase the accuracy of mammography screening. To find out, we co-organized a coalition of oncology and technology partners to pose this challenge to the science community.