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My lab focuses on biomedical data fusion: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. Previously we pioneered data fusion work using Bayesian and kernel methods studying breast and ovarian cancer. Additionally, we developed computational algorithms for the identification of driver genes using multi-omics data. Furthermore, we are working on multi-scale biomedical data fusion methods, bridging the molecular using omics data, cellular using pathology data and tissue using medical imaging data.
Liquid Biopsy With PET/CT Versus PET/CT Alone in Diagnosis of Small Lung Nodules
The purpose of this study is to determine if a liquid biopsy, a method of detecting cancer
from a blood draw, combined with a PET/CT scan, a type of radiological scan, is better at
determining whether a lung nodule is cancerous when compared to a PET/CT scan alone. A PET/CT
scan is already used for diagnosis of lung nodules, but its efficacy is uncertain in nodules
6-20 mm in size. Therefore, the PET/CT will be evaluated for its diagnostic ability in
lesions this size alone and in combination with a liquid biopsy. Secondarily, a machine
learning model will be created to see if the combination of the PET/CT imaging data and the
liquid biopsy data can predict the presence of cancer.
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