System-based Approaches for Radiotherapy Bioinformatics and Outcomes Modeling

1024 379 Techna

Join us for Techna Rounds as we welcome Issam El Naqa to be our speaker for the month. Issam El Naqa received his B.Sc. (1992) and M.Sc. (1995) in Electrical and Communication Engineering from the University of Jordan, Jordan, Ph.D. (2002) in Electrical Engineering from Illinois Institute of Technology, Chicago, IL, USA, and M.A. (2007) in Biology Science from Washington University in St. Louis, St. Louis, MO, USA, where he completed a post-doctoral fellowship in medical physics and was subsequently a faculty member at the departments of radiation oncology and the division of biomedical and biological sciences and an adjunct faculty at the department of Electrical engineering. He is currently an Associate Professor at McGill University Health Centre/Medical Physics Unit and associate member in the department of Biomedical Engineering and the department of Physics. He is a recognized expert in the fields of image processing, bioinformatics, computational radiobiology, and treatment outcomes modeling and has published extensively in these areas. He is an acting member of several academic and professional societies, which include IEEE, AAPM, and ASTRO. His research has been funded by several federal and private grants and serves as a peer reviewer and associate editor for several international journals in his areas of expertise. He is currently an FRSQ and CIHR scholar.

Patients who undergo radiotherapy are at risk of experiencing tumor recurrence or complications to normal tissues after treatment. Due to the inherent complexity and heterogeneity of radiobiological processes, traditional TCP/NTCP methods have fallen short of providing sufficient predictive power when applied prospectively to personalize treatment regimens, this has been especially true in cases of non-conventional fractionations regimens. Therefore, we are investigating multi-scale systems-based approaches that are able to integrate physical and biological information to adapt intra-radiotherapy changes and optimize postradiotherapy treatment outcomes using a top-bottom and bottom-top approaches. The development of such “radio-biophysiomics” systems is based on extracting high-level anatomical and physiological image features, identification of robust low-level molecular biomarkers and sophisticated computer modeling methods to quantify patient’s treatment benefit-risk ratio. If successful, this would allow patients and physicians to undertake informed clinical decision making to select treatment regimens or modalities that are appropriate to the patient’s biophysical profile and anticipated response. Examples of the application of this methodology from our work will be discussed highlighting current challenges and future potentials.

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