Issam El Naqa

Associate Member

Medical Physics Unit, McGill University

Other appointments:

Associate Professor, Department of Oncology

Associate Member, Biomedical Engineering Department

Associate Member, Physics Department

Medical Scientist, Radiation Oncology Division, Montreal General Hospital

Associate Member, Experimental Medicine, McGill University


Cedars Cancer Centre, Medical Physics, DS1.7141
McGill University Health Centre - Glen Site
1001 boul. Décarie
Montréal, QC H4A 3J1


514 934 1934 x45302


Issam.elnaqa [at]


MSc, MA, PhD

Certification & honors:

  • Awarded Deutscher Akademischer Austauschdienst  (DAAD) scholarship, Germany, 1996.

  • Awarded El-Hijawii (1st place) for scientific research for young investigators. For an Article (El Naqa et al., 1996) and a computer software in Visual Basic for detection of coronary artery dimensions, Jordan, 1998.

  • Awarded Excellence in Laboratory Teaching from IIT (Fall 1999 and Spring 2000)

  • Awarded Highest Standards of Academic Achievement, IIT, 2002.

  • Nominated via internal competition to represent Washington University/ School of Medicine in Pfizer's New Scholar's Grants in Clinical Epidemiology for the 2006 Award.

  • Special recognition at the American Association of Physicists in Medicine (AAPM) by presenting at the Laughlin Science Council Research Symposium: Multi-modality image fusion entitled "Concurrent Multi-Modality Image Segmentation," Orlando, FL, 2006.

  • Featured Article by PMB (DREES) and editorial article citation on by Belle Dumé entitled “Software unravels radiation responses”, published Dec 7 2006.

  • Medical Physics cover figure from “Concurrent Multimodality Image Segmentation by Active Contours for Radiotherapy Treatment Planning” article, 2007.

  • Special recognition at the American Association of Physicists in Medicine (AAPM) by selection for presentation at the Laughlin Science Council Research Symposium: entitled "4DCT motion estimation and modeling," by Yang et al. (post-doc trainee) Houston, TX, 2008.

  • Press release in the SCIENCE HIGHLIGHTS of the 50th AAPM Meeting in Houston, 2008, about our talk for tracking tumor changes during radiotherapy entitled “New Technique to Estimate Lung Tumor Changes” written by Kathy Svitil. The article was also featured on the website:

  • Special recognition at the American Association of Physicists in Medicine (AAPM) by selection for presentation at the Laughlin Science Council Research Symposium: entitled " Image-Based Scoring of Radiation Injury in Lung for Dose-Effect Correlations: Analysis of Sources of Uncertainties," by Lee et al. (graduate student with Dr Seuntjens) Philadelphia, PA, 2010.

  • Featured editorial by Jonathan Evans entitled “Dead ends point the way: finding robust biomarkers in limited proteomics data,” ( on article “A Bioinformatics Approach for Biomarker Identification in Radiation-Induced Lung Inflammation from Limited Proteomics Data,”  Oh et al., JPR, 2011.

  • Poster award nomination (Top 30) “Integrating dosimetry and biomarkers via a Bayesian network for predicting radiotherapy response in lung cancer,” ESTRO, 2011.

  • Student first prize at the Canadian Society for Chemical Engineering (CSChE) for Non-invasive Plethysmography by James Coates et al. (undergrad with Dr Leask), 2012.

  • FRSQ (Fonds de la recherche en santé du Québec) Salary Award (2012-2016). (Ranked#1 in the competition), supplement covered.

  • CIHR (Canadian Institutes of Health Research) New Investigator Salary Award (2012-2017).

  • Red journal “Outstanding Reviewer of 2012”.

Clinical research:

Medical Physics/Radiation Oncology

Research focus:

Oncology bioinformatics, computational and systems biology, multimodality imaging, adaptive radiotherapy

Short introductory description of main research program / research streams.)

Our lab’s general research interests are in the areas of oncology bioinformatics, multimodality image analysis, and treatment outcome modeling. The primary motivation is 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. These models could be used to personalize cancer patients’ chemoradiotherapy treatment based on predicted benefit/risk.

Key recent publications:

I. El Naqa, P. Pater, J. Seuntjens, Monte Carlo role in radiobiological modeling of radiotherapy outcomes, Phys. Med. Biol. 57 R75-R97, 2012.

B. Kidd, I. El Naqa, B.A. Siegel, F. Dehdashti, P.W. Grigsby, FDG-PET-based prognostic nomograms for locally advanced cervical cancer, Gyn Onc,  2012 Oct;127(1):136-40.

C. Robinson, T. DeWees, I. El Naqa, K. Creach, J. Olsen, T. Crabtree, B. Meyers*, V. Puri, P. Parikh, J. Bradley, Patterns of failure and survival after stereotactic body radiation therapy or lobar resection for clinical stage in non-small-cell lung cancer, Journal of Thoracic Oncology (JTO), 2013 Feb;8(2):192-201

I. El Naqa, Machine learning methods for predicting tumour response in lung cancer, WIRES: Datamining and Knowledge Discovery, vol. 2, no. 2, pp 99 – 192, 2012

S. Lee, G. Stroian, N. Kopek, M. AlBahhar, J. Seuntjens, I. El Naqa, Analytical modeling of regional radiotherapy dose response of lung, Phys. Med. Biol. 57 3309–3321, 2012.

M. Vaidya, K.M. Creach, J. Frye, F. Dehdashti, J.D. Bradley, I. El Naqa, Using FDG-PET/CT image characteristics for predicting radiotherapy response in lung cancer, Radiotherapy and Oncology, 2012 Feb;102(2):239-4

H. Zaidi, C.L. Fuentes, I. El Naqa, Comparative methods for PET image segmentation in pharyngolaryngeal squamous cell carcinoma, Eur J Nucl Med Mol Imaging, 2012 May;39(5):881-91

J.H. Oh, J.M. Craft, R. Townsend, J.O. Deasy,  J.D. Bradley, I. El Naqa, A bioinformatics approach for biomarker identification in radiation-induced lung inflammation from limited proteomics data, Journal of Proteomics Research (JPR), 10(3):1406-15, 2011.

R. Al-Lozi, X.A. Li, J. White, A. Apte, A. Tai, J.M. Michalski, W.R. Bosch, I. El Naqa, Tools for consensus analysis of experts’ contours for radiotherapy structure definitions, Radiotherapy and Oncology, Dec;97(3):572-8, 2010.

H. Zaidi, I. El Naqa, PET-guided delineation of radiation therapy treatment volumes: A survey of image segmentation techniques, Eur J Nucl Med Mol Imaging, 37(11):2165-87, 2010.

D. Yang, S.R. Chaudhari, S.M. Goddu, D. Pratt, D. Khullar, J.O. Deasy, I. El Naqa, Deformable registration of abdominal kilovoltage treatment planning CT and tomotherapy daily megavoltage CT for treatment adaptation, Medical Physics, Vol. 36:2, pp. 329-338, February 2009.
J. Tang, R.M. Rangayyan, J. Xu, I. El Naqa, Y. Yang, Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances, IEEE Trans. Information Technology in Biomedicine, Mar;13(2):236-51, 2009

I. El Naqa, J.D. Bradley, P.E. Lindsay, A.J. Hope,  J.O. Deasy, Predicting radiotherapy outcomes using statistical learning techniques, Phys. Med. Biol., Vol.  54, pp. 9-30, 2009.

X. Wang, I. El Naqa, Prediction of both conserved and nonconserved microRNA targets in animals, Bioinformatics, 24(3):325-32, 2008.

I. El Naqa, D. Yang, D. Khullar, S. Mutic, J. Zheng, J.D. Bradley, P. Grigsby, J.O. Deasy, Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning, Medical Physics, Vol. 34, 12, pp. 4738-4749,  December 2007.

I. El Naqa, J. Bradley, A. Blanco, P.E. Lindsay, M. Vicic, A. Hope, J.O. Deasy, Multi-variable modeling of radiotherapy outcomes including dose-volume and clinical factors, Int. J. Radiation Oncology Biol. Phys, March 2006 , Vol. 64, Issue 4, pp 1275-1286.