The long-term goal of the proposed research is to develop multi-modality, image-based markers for? assessing breast density and parenchyma! structure that may be used alone or together with clinical? measures, as well as biomarkers, for use in determining risk of breast cancer. The general hypothesis is? that inclusion of automated analyses of the parenchyma will improve the assessment of breast cancer risk.? The specific objectives of the proposed research are (1) to perform mage-based categorization of patient? databases based on breast density, parenchyma morphology, and parenchyma kinetics [that will be? automatically extracted], (2) perform correlation and modeling of the various descriptors of breast density? and parenchymal characteristics (i.e. image-markers) with known surrogate markers of risk (such as? BRCA1 and BRCA2 heterozygotes and presence of cancer on the contralateral breast) to yield new imagebased? markers of risk, (3) perform correlation of the various descriptors of breast density and parenchymal? characteristics (i.e. image-markers) with developing biomarkers and candidate genes to yield better? understanding of breast cancer risk, and (4) perform preclinical assessment and translation of the density? and parenchymal characteristics of women at high risk using these new models. This clinical translational? component will involve quantitative comparison with the current method of risk assessment of the Gail? model and a case control study with databases from other institutions relating the image-based markers to? onset of cancer. In the future, it is expected that such image-based markers will be useful for improved? assessment of patients at high risk for breast cancer and for monitoring the response of preventive? treatments. The proposed research is novel in that other correlative research in breast cancer risk with? image-based analyses involves only breast density. However, here we incorporate two additional,? potentially complementary, analyses of the breast parenchyma into the correlative and modeling research.? The University of Chicago is extremely well-positioned to perform this correlative research on multimodality? image-based analyses for breast cancer risk because of its 20-year history of developing multimodality? computer-aided diagnosis methods for mammography, sonography, and MRI, and its integration? with the University of Chicago Cancer Risk Clinic.
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