© SPIE
RG Digital Medicine| Pro-Mics-BrCa|
Pro-Mics-BrCa
Benefit of artificial intelligence in the analysis of morphological image data to predict cancer incidence and mutation status in patients with a high-risk constellation for familial breast and ovarian cancer
Breast cancer is the most common tumor disease among women. Since there are currently no preventive measures, early detection of tumors through screenings is emphasized. During these screenings, mammography and MRI data are collected.
In Pro-Mics-BrCa, we aim to more accurately predict the risk of tumor development in the following years based on the image data. A low false prediction rate is important to avoid unnecessary procedures like biopsies and to prevent causing anxiety among patients.
Methodologically, we use both classical biomarkers (radiomics) from image data and modern neural networks. An important component is determining risk mutations in breast tissue. Our long-term goal is to provide a toolkit for better prediction of cancer development.
Project Partners
Dr. Eva Maria Fallenberg, Dr. Michael Ingrisch et. al. (LMU Munich)
Dr. Christoph Engel et. al. (University of Leipzig)
Prof. Dr. Rita Schmutzler et. al. (University Hospital Cologne)
Prof. Dr. Nico Karssemeijer et. al. (Nijmegen)
Deutsches Konsortium für Famili?ren Brust- und Eierstockkrebs
