Breast cancer is the most common cancer among women in Norway and worldwide. Since the cause of breast cancer is not known, mammographic screening is offered as a secondary prevention, aimed at reducing the mortality from the disease. About 500 000 women have participated in the Norwegian Breast Cancer Screening every second year since the program was made nationwide in 2005. The radiologists spend a substantial time interpreting screening mammograms of healthy women, as about 7% of the exams are discussed at consensus, 3-4% are recalled for further assessment and 20% of those recalled, 0.6% of the attending women, are diagnosed with breast cancer and additional 0.17% are diagnosed before the next screening. By exploiting machine learning in the process the aim is to reduce the recall rate, the rate of missed screen-detected and interval breast cancer and obtain knowledge which can help us reducing overdiagnosis and overtreatment, which again will reduce the disease specific mortality. By achieving this goal, we will be able to reduce the human and financial burden of mammographic screening. A realistic ambition is that 100 women will get a breast cancer diagnosis 1-4 years earlier. An on-the-fly control of the image quality may reduce the number of recalls of 1 200 women annually and also improve the image quality in the further assessment. The project take advantage of three main factors: There has been a revolution in machine learning, also on medical images where machine learning together with experts is better than only human expertise. Our database with mammograms is at least 20 times larger than any published study. This is critical for machine learning. We will focus on questions that are relevant for the Norwegian Breast Cancer Screening Program. The project will build world leading competence which is also valuable for other screening programs and other medical applications.
Project leader: Solveig Hofvind
Category: Helse Sør-Øst RHF
Institution: OSLO UNIVERSITETSSYKEHUS HF KREFTREGISTERET