Project Details
Projekt
Potential of Radiomics and AI in the prediction of breast cancer risk and mutation status in high risk patients with confirmed mutation or calculated high risk status (PRo-mics-BrCa)
Applicants
Privatdozent Dr. Christoph Engel; Privatdozentin Dr. Eva Maria Fallenberg; Professor Dr.-Ing. Horst Karl Hahn; Professor Dr. Michael Ingrisch; Professorin Dr. Rita Katharina Schmutzler
Subject Area
Nuclear Medicine, Radiotherapy, Radiobiology
Term
from 2019 to 2024
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 428224258
Pathogenic gene mutations were identified that significantly increase the lifetime risk of developing breast cancer, most prominently in either of the genes BRCA1 and BRCA2. Harmful mutations in these genes produce a hereditary breast-ovarian cancer syndrome in affected families. Mutations in BRCA1/2 are uncommon, and breast cancer is relatively common, so these mutations account for only five to ten percent of all breast cancer cases in women. As full-digital breast imaging, both mammography and MRI, has been introduced, an extended imaging based phenotyping and subsequently multivariate extended phenotype-genotype correlation studies come into reach in order to reveal new specific imaging based indicators for breast cancer risk. This application aims at providing a powerful set of tools for extracting multiscale tissue characterization based on breast imaging with particular focus on temporal change and asymmetry analysis, applied to a subpopulation of a large-scale breast imaging dataset acquired over the last ten years in a high-risk population in Germany covering individual imaging volumes (>6,000 women with on average >4 examinations). All of these women were offered DNA testing and a part received additional clinical testing due to suspicious findings. We will access and statistically analyze all of this data in collaboration with the German Consortium for Hereditary Breast and Ovarian Cancer. Since DNA testing cannot be completely realized in a larger screening population, and the known breast cancer risk genes only explain 1 in 4 of the identified hereditary breast cancer risk cases, we will test a number of quantitative imaging biomarkers from dynamic contrast-enhanced breast MRI and from full-field digital mammography (FFDM), and correlate those imaging biomarkers and their temporal developments with breast cancer incidence, with the presence of pathogenic gene mutations as well as with the identified hereditary breast cancer risk. This correlation will produce an imaging-based subphenotype classification with potential implications for extended stratification in future surveillance programs. Imaging biomarkers will include, among other parameters, morphological descriptors, volumetric breast tissue composition and density, tissue heterogeneity and asymmetry, contrast enhancement patterns, as well as the longitudinal changes thereof. We will perform the Radiomics analysis as well as state-of-the-art convolution neural network deep learning approaches extraction for morphological features of breast tissue composition and density as well as background enhancement and the relationship to cancer incidence in patients with BRCA 1 or 2 mutation, less frequent mutations and calculated elevated risk for breast cancer. Overall, we aim at developing an advanced toolset for improved early detection of breast cancer and at deriving novel hypotheses with respect to extended breast cancer risk.
DFG Programme
Priority Programmes
