Radka Stoyanova, PhD

Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA

Radka Stoyanova, PhD is Associate Professor at the Department of Radiation Oncology at the Sylvester Cancer Center, Miller School of Medicine, University of Miami. She is also Director of Imaging and Biomarkers Research at the Department. Dr. Stoyanova received her Master’s Degree in Mathematics from the University of Sofia, Bulgaria. She obtained her doctoral training and Ph.D. degree at the Imperial College London, under the mentorship of Profs. J.K. Nicholson and J.C. Lindon, who pioneered the application of pattern recognition techniques to NMR.

Dr. Stoyanova has extensive background in developing approaches to best utilize imaging techniques in cancer research, diagnosis and treatment, as well as in developing approaches for the analysis, mining, and interpretation of “big data” generated by high-throughput approaches such as genomics, proteomics, metabolomics, and radiomics. Her main research focus is the implementation of new imaging approaches to improve outcomes from radiation therapy. The precise dose delivery to the malignant tissue increases the likelihood of tumor eradication by maximizing response while minimizing toxicity of surrounding normal tissue. Dr. Stoyanova utilizes volumetric MRI spectroscopy imaging to determine the margins and extent of tumor infiltration in the treatment planning of brain cancers. In prostate cancer, multiparametric (mp)MRI is routinely incorporated in the treatment planning. Dr. Stoyanova was fundamental in the initiation of several on-going clinical trials where the delineated tumor volumes in the prostate are targeted with a radiation boost. Her recent findings about the ability to decipher the characteristics of hypoxia contrast-versus-time pattern in DCE-MRI in a preclinical model are being tested in clinical setting. Dr. Stoyanova’s overarching goal is to develop a multifaceted approach, whereby combining data about biopsy location, biopsy content (biomarkers), and in vivo imaging (functional and metabolic) can lead to the informed design of effective treatment for improved tumor control and reduced toxicity.