Associate Professor of Radiation Oncology
About
Biography
Dr. El Naqa lab's research interests are in the areas of oncology bioinformatics, multimodality image analysis, and treatment outcome modeling. He operates at the interface of physics, biology, and engineering with the primary motivation to design and develop novel approaches to unravel cancer patients’ response to chemoradiotherapy treatment by integrating physical, biological, and imaging information into advanced mathematical models using combined top-bottom and bottom-top approaches that apply techniques of machine learning and complex systems analysis to first principles and evaluating their performance in clinical and preclinical data. These models could be then used to personalize a cancer patient's chemoradiotherapy treatment based on predicted benefit/risk and help understand the underlying biological response to disease.
Areas of Interest
- Bioinformatics: design and develop large-scale datamining methods and software tools to identify robust biomarkers (pan-Omics) of chemoradiotherapy treatment outcomes from clinical and preclinical data.
- Multimodality image-guided targeting and adaptive radiotherapy: design and develop hardware tools and software algorithms for multimodality image analysis and understanding, feature extraction for outcome prediction (radiomics), real-time treatment optimization and targeting.
- Radiobiology: design and develop predictive models of tumor and normal tissue response to radiotherapy. Investigate the application of these methods to develop therapeutic interventions for protection of normal tissue toxicities.
Clinical Interests
- Therapeutic medical physics
Honors & Awards
2017 AAPM Fellowship
2016 IEEE Senior member, 2016
2015 PMB Editor’s choice publication (Vallieres et al.)
2014 Medical Physics Author’s choice publication (Lee et al.)
2014 Basic Science Abstract Award, ASTRO (Zlateva et al.)
2014 Best in Physics at ASTRO
2014 Young investigator symposium, AAPM (Hickling et al, First place). Selection in 2015 (Markel et al.)
2014 Best in Physics at AAPM. Selection as well in 2015
2014 Radiotherapy and Oncology “Outstanding Reviewer Award”
2012 Red journal Outstanding reviewer
2012 Medical Physics journal “Outstanding Reviewer of 2012”
2012-2015 Canadian Institutes of Health Research (CIHR) scholars
2012-2015 Fonds de la recherche en santé du Québec (FRSQ) scholars
2007 Medical Physics Journal cover
2006 AAPM special recognition at Science Council Research Symposium, also in (2008, 2010, 2013)
2002 Highest Standards of Academic Achievement award, IIT
1998 Young Investigator Award (1st place), Amman, Jordan
1996 Deutscher Akademischer Austauschdienst (DAAD) scholarship
Credentials
- Postdoctoral Fellowship, Washington University, St. Louis, MO, 2005
- MA, Biology, Washington University, St Louis, 2007
- PhD, Electrical Engineering, Illinois Institute of Technology, Chicago, IL, 2002
- Board Certification: Therapeutic Medical Physics
Published Articles via PubMed
VIEW MORE AT PUBMED
Published Articles or Reviews
Selected from 132 publications
- El Naqa, I. Perspectives on making big data analytics work for oncology. Methods. 111:32-44, 2016.
- Hickling S, Lei H, Hobson M, Leger P, Wang X, El Naqa I. Experimental evaluation of X-ray acoustic computed tomography for radiotherapy dosimetry applications. Medical Physics. (In Press)
- Perez J, Ybarra N, Chagnon F, Serban S, Lee S, Seuntjens J, Lesur O, and El Naqa I. Tracking of mesenchymal stem cells with fluorescence endomicroscopy imaging in radiotherapy-induced lung injury, Scientific Reports. (in press)
- Ohri N, Duan F, Snyder BS, Wei B, Machtay M, Alavi A, Siegel BA, Johnson DW, Bradley JD, DeNittis A, Werner-Wasik M, El Naqa I. Pretreatment 18FDG-PET textural features in locally advanced non-small cell lung cancer: Secondary analysis of ACRIN 6668/RTOG 0235. J Nucl Med. 2016.
Books
- El Naqa: A Guide to Outcome Modeling In Radiotherapy and Oncology: Listening to the Data, CRC Press, US, 2018
- El Naqa: Emerging Developments and Practices in Oncology, IGI Global, US, 2018
- El Naqa, Li, Murphy: Machine Learning in Radiation Oncology: Theory and Applications, Springer, Switzerland, 2015.
Web Sites
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