个人简历

Experiences
- Ph.D. (University of Lorraine, France).
- 7 years of research experience at the Lady Davis Institute for Medical Research, McGill University, Canada; the
University of Texas MD Anderson Cancer Center, USA; Villanova University, USA, and etc.

Biography
Ahmad Chaddad is a Project Director at the Lady Davis Institute for Medical Research, McGill University. He received his B.E. and M. Eng. degrees in 2007 and 2008, respectively, and his Ph.D. degree in engineering systems from the University of Lorraine, France in 2012. His previous experience includes working as an adjunct professor at Ecole de Technologie Superieure (ETS, 2017-2019), and as a postdoctoral research fellow at the University of Texas MD Anderson Cancer Center (2013-2015), and at ETS and McGill University Health Centre (2015-2017). He is a member of the LCOMS laboratory in France. His current research interests include AI and radiomics analysis in order to improve personalized medicine strategies, by allowing clinicians to monitor disease in real time as patients move through treatment. He is a member of several international technical and organizational committees. Additionally, he has authored more than 63 research papers.
Current projects (Collaborators)
Radiomic analysis for prostate cancer ( Prof. Tamim Niazi, McGill University, Canada)
Deep radiomic analysis + neuroimaging (Prof. Christian Desrosiers, ETS, Canada)
Texture analysis of colorectal cancer using multispectral imagery (Prof. Camel Tanougast, University of Lorraine, France)
Education
2009-2012: PhD., Engineering Systems, University of Lorraine, Metz, France
2007-2008: Master-DEA.,Bio-mechanical & Biomedical Engineering, University of Technology of Compiegne, Compiegne, France
2002-2007: B. Eng., Biomedical Engineering, Islamic University of Lebanon, Beirut-Khalde, Lebanon
Course development and lecturing
2009-2018: Electronic circuits
2016-2018: Computer vision
2017-2018: Biometric system

研究领域

Research Interests
Artificial intelligence and radiomics analysis in biomedical engineering.
- Radiomics/radiogenomics analysis for predicting the recurrent glioblastoma (GBM); Machine learning techniques
applied on medical data: GBM, autism, alzheimer, lung cancer and colorectal cancer, etc.""

近期论文

Ji, L., Zhang, R., Han, H., Chaddad, A., 2020 “Image Magnification Based on Bicubic Approximation with Edge as Constraint”, Appl. Sci., 10, 1865. https://doi.org/10.3390/app10051865
Saima Rathore, Tamim Niazi, Aksam Iftikhar, Ashish Singh, Batool Rathore, Michel Bilello, Chaddad, A., 2020 “Multi-modal ensemble-based segmentation of white matter lesions and analysis of their differential characteristics across major brain regions”, Appl. Sci. 2020, 10(6), 1903; https://doi.org/10.3390/app10061903

Rathore S., Niazi T., Iftikhar A., Chaddad, A., 2020 “Glioma grading via analysis of digital pathology images using machine learning”, Cancers 2020, 12, 578. https://doi.org/10.3390/cancers12030578

Kucharczyk MJ, Tsui JMG, Khosrow-Khavar F, Bahoric B, Souhami L, Anidjar M, Probst S, Chaddad, A., Sargos P and Niazi T (2020) Combined Long-Term Androgen Deprivation and Pelvic Radiotherapy in the Post-operative Management of Pathologically Defined High-Risk Prostate Cancer Patients: Results of the Prospective Phase II McGill 0913 Study. Front. Oncol. 10:312. doi: 10.3389/fonc.2020.00312

Rathore S., Iftikhar A., Chaddad, A., et al., “Segmentation and grade prediction of colon cancer digital pathology images across multiple institutions”. Cancers, 2019, 11, 1700. https://doi.org/10.3390/cancers11111700

Chaddad, A., Daniel P., Sabri S., Desrosiers C., Abdulkarim B., 2019. “Integration of Radiomic and Multi-omic Analyses Predicts Survival of Newly Diagnosed IDH1 Wild-Type Glioblastoma”, Cancers, 2019, 11, 1148. https://doi.org/10.3390/cancers11081148.

Chaddad, A., Toews, M., Desrosiers C., Niazi, T., 2019. “Deep Radiomic analysis Based on Modeling Information flow in Convolutional Neural Networks”, IEEE Access, 10.1109/ACCESS.2019.2930238
Chaddad, A., Desrosiers, C., Abdulkarim, B., Niazi, T., 2019. “Multimodal radiomic features for predicting the gene status and survival outcome of lower-grade glioma patients”, IEEE Access, vol.7, 75976-75984, 10.1109/ACCESS.2019.2920396
Chaddad, A., Daniel, P., 2019. Novel Radiomic Features Based on Joint Intensity Matrices for Predicting Glioblastoma Patient Survival Time. IEEE Journal of Biomedical and Health Informatics 23, 795–804. https://doi.org/10.1109/JBHI.2018.2825027

Chaddad, A., Michael, J.K., Daniel, P., Sabri, S, Jean-Claude, B., Niazi, T., Abdulkarim, B., 2019. Radiomics in glioblastoma: current status and challenges facing clinical implementation. Frontiers in oncology, DOI: 10.3389/fonc.2019.00374

Elakshar, S., James, M.G.T., Michael, J.K., Tomic, N., Fawaz, Z.S., Bahoric, B., Papayanatos, J., Chaddad, A., Niazi, T., 2019. Does interfraction cone beam computed tomography improve target localization in prostate bed radiotherapy? Technology in Cancer Research and Treatment 18. https://doi.org/10.1177/1533033819831962

Daniel, P., Sabri, S., Chaddad, A., Meehan, B., Jean-Claude, B., Rak, J., Abdulkarim, B.S., 2019. Temozolomide induced hypermutation in glioma: Evolutionary mechanisms and therapeutic opportunities. Frontiers in Oncology 9. https://doi.org/10.3389/fonc.2019.00041

Chaddad, A., Desrosiers, C., Niazi, T., 2018b. Deep radiomic analysis of MRI related to Alzheimer’s disease. IEEE Access 6, 58213–58221. https://doi.org/10.1109/ACCESS.2018.2871977

Chaddad, A., Niazi, T., Probst, S., Bladou, F., Anidjar, M., Bahoric, B., 2018d. Predicting gleason score of prostate cancer patients using radiomic analysis. Frontiers in Oncology 8. https://doi.org/10.3389/fonc.2018.00630

Chaddad, A., Kucharczyk, M.J., Niazi, T., 2018c. Multimodal radiomic features for the predicting gleason score of prostate cancer. Cancers 10. https://doi.org/10.3390/cancers10080249

Chaddad, A., Sabri, S., Niazi, T., Abdulkarim, B., 2018e. Prediction of survival with multi-scale radiomic analysis in glioblastoma patients. Medical and Biological Engineering and Computing 56, 2287–2300. https://doi.org/10.1007/s11517-018-1858-4

Chaddad, A., Daniel, P., Niazi, T., 2018a. Radiomics evaluation of histological heterogeneity using multiscale textures derived from 3D wavelet transformation of multispectral images. Frontiers in Oncology 8. https://doi.org/10.3389/fonc.2018.00096

Chaddad, A., Desrosiers, C., Toews, M., Abdulkarim, B., 2017c. Predicting survival time of lung cancer patients using radiomic analysis. Oncotarget 8, 104393–104407. https://doi.org/10.18632/oncotarget.22251

Chaddad, A., Desrosiers, C., Hassan, L., Tanougast, C., 2017a. Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder. BMC Neuroscience 18. https://doi.org/10.1186/s12868-017-0373-0

Chaddad, A., Desrosiers, C., Toews, M., 2017b. Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age. Scientific Reports 7. https://doi.org/10.1038/srep45639

Haj-Hassan, H., Chaddad, A., Harkouss, Y., Desrosiers, C., Toews, M., Tanougast, C., 2017. Classifications of multispectral colorectal cancer tissues using convolution neural network. Journal of Pathology Informatics 8. https://doi.org/10.4103/jpi.jpi_47_16

Chaddad, A., Tanougast, C., 2017. Texture Analysis of Abnormal Cell Images for Predicting the Continuum of Colorectal Cancer. Analytical Cellular Pathology 2017. https://doi.org/10.1155/2017/8428102

Chaddad, A., Desrosiers, C., Hassan, L., Tanougast, C., 2016b. A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome. British Journal of Radiology 89. https://doi.org/10.1259/bjr.20160575

Chaddad, A., Tanougast, C., 2016b. Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients. Medical and Biological Engineering and Computing 54, 1707–1718. https://doi.org/10.1007/s11517-016-1461-5

Chaddad, A., Desrosiers, C., Bouridane, A., Toews, M., Hassan, L., Tanougast, C., 2016a. Multi texture analysis of colorectal cancer continuum using multispectral imagery. PLoS ONE 11. https://doi.org/10.1371/journal.pone.0149893

Chaddad, A., Tanougast, C., 2016a. Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images. Brain Informatics 3, 53–61. https://doi.org/10.1007/s40708-016-0033-7

Chaddad, A., Tanougast, C., 2015a. Real-time abnormal cell detection using a deformable snake model. Health and Technology 5, 179–187. https://doi.org/10.1007/s12553-015-0115-1

Chaddad, A., Tanougast, C., 2015b. High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features. Advances in Bioinformatics 2015. https://doi.org/10.1155/2015/728164

Chaddad, A., 2015. Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models. International Journal of Biomedical Imaging 2015. https://doi.org/10.1155/2015/868031

Chaddad, A., 2014. Low-Noise Front-End Receiver Dedicated to Biomedical Devices: NIRS Acquisition System. Circuits and Systems 05, 191. https://doi.org/10.4236/cs.2014.58021

Chaddad, A., 2014. Brain Function Diagnosis Enhanced Using Denoised fNIRS Raw Signals. Journal of Biomedical Science and Engineering 07, 218. https://doi.org/10.4236/jbise.2014.74025

Chaddad, A., Tanougast, C., Golato, A., Dandache, A., 2013. Carcinoma cell identification via optical microscopy and shape feature analysis. Journal of Biomedical Science and Engineering 06, 1029. https://doi.org/10.4236/jbise.2013.611128

Chaddad, A., Tanougast, C., Dandache, A., Bouridane, A., 2011c. Extracted haralick’s texture features and morphological parameters from segmented multispectrale texture bio-images for classification of colon cancer cells. WSEAS Transactions on Biology and Biomedicine 8, 39–50.
Peer-reviewed article published in a conference proceeding

Chaddad, A., Zhang, M., Desrosiers, C., and Niazi, T., “Deep radiomic features from MRI scans predict survival outcome of recurrent glioblastoma” MICCAI RNO-AI 2019. Pages 36-43, https://rd.springer.com/chapter/10.1007/978-3-030-40124-5_4

Rathore, S., Chaddad, A., et al., “Imaging signature of 1p/19q co-deletion status derived via machine learning in low-grade glioma” MICCAI RNO-AI 2019. Pages 61-69, https://www.springer.com/gp/book/9783030401238

Chaddad, A., Niazi, T., 2018. Radiomics analysis of subcortical brain regions related to Alzheimer disease, in: 2018 IEEE Life Sciences Conference, LSC 2018. pp. 203–206. https://doi.org/10.1109/LSC.2018.8572264

Kumar, K., Desrosiers, C., Chaddad, A., Toews, M., 2017. Spatially constrained sparse regression for the data-driven discovery of Neuroimaging biomarkers, in: Proceedings - International Conference on Pattern Recognition. pp. 2162–2167. https://doi.org/10.1109/ICPR.2016.7899956

Chaddad, A., Desrosiers, C., Toews, M., 2016d. Radiomic analysis of multi-contrast brain MRI for the prediction of survival in patients with glioblastoma multiforme, in: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. pp. 4035–4038. https://doi.org/10.1109/EMBC.2016.7591612

Chaddad, A., Desrosiers, C., Hassan, L., Toews, M., 2016c. Multispectral texture analysis of histopathological abnormalities in colorectal tissues, in: Proceedings - International Conference on Image Processing, ICIP. pp. 2628–2632. https://doi.org/10.1109/ICIP.2016.7532835

Chaddad, A., Desrosiers, C., Toews, M., 2016f. Local discriminative characterization of MRI for Alzheimer’s disease, in: Proceedings - International Symposium on Biomedical Imaging. pp. 1–5. https://doi.org/10.1109/ISBI.2016.7493197

Chaddad, A., Desrosiers, C., Toews, M., 2016g. GBM heterogeneity characterization by radiomic analysis of phenotype anatomical planes, in: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. https://doi.org/10.1117/12.2214491

Chaddad, A., Desrosiers, C., Toews, M., 2016e. Phenotypic characterization of glioblastoma identified through shape descriptors, in: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. https://doi.org/10.1117/12.2209121

Chaddad, A., Bouridane, A., Hassan, L., Tanougast, C., 2015a. Wavelet based radiomics for brain tumour phenotypes discrimination, in: Proceedings - CIE 45: 2015 International Conference on Computers and Industrial Engineering. pp. 1167-1174. ISBN 978-1-5108-1745-6.

Chaddad, A., Bouridane, A., Tanougast, C., 2015b. Continuum analysis of colorectal cancer using texture feature extraction, in: Proceedings - CIE 45: 2015 International Conference on Computers and Industrial Engineering. pp. 1423-1430. ISBN 978-1-5108-1745-6.

Haj-Hassan, H., Chaddad, A., Tanougast, C., Harkouss, Y., 2015a. Hybrid segmentation of bio-images, in: Proceedings - CIE 45: 2015 International Conference on Computers and Industrial Engineering. pp. 1431-1437. ISBN 978-1-5108-1745-6.

Chaddad, A., Zinn, P.O., Colen, R.R., 2015c. Radiomics texture feature extraction for characterizing GBM phenotypes using GLCM, in: Proceedings - International Symposium on Biomedical Imaging. pp. 84–87. https://doi.org/10.1109/ISBI.2015.7163822

Haj-Hassan, H., Chaddad, A., Tanougast, C., Harkouss, Y., 2015b. Comparison of segmentation techniques for histopathological images, in: 2015 5th International Conference on Digital Information and Communication Technology and Its Applications, DICTAP 2015. pp. 80–85. https://doi.org/10.1109/DICTAP.2015.7113175

Chaddad, A., Tanougast, C., 2014. Low-noise transimpedance amplifier dedicated to biomedical devices: Near infrared spectroscopy system, in: Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014. pp. 601–604. https://doi.org/10.1109/CoDIT.2014.6996963

Chaddad, A., Zinn, P.O., Colen, R.R., 2014c. Quantitative texture analysis for Glioblastoma phenotypes discrimination, in: Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014. pp. 605–608. https://doi.org/10.1109/CoDIT.2014.6996964

Haj-Hassan, H., Chaddad, A., Tanougast, C., Harkouss, Y., 2014. Segmentation of abnormal cells by using level set model, in: Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014. pp. 770–773. https://doi.org/10.1109/CoDIT.2014.6996994

Wangaryattawanich, P., Wang, J., Thomas, G.A., Chaddad, A., Zinn, P.O., Colen, R.R., 2014. Survival analysis of pre-operative GBM patients by using quantitative image features, in: Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014. pp. 625–627. https://doi.org/10.1109/CoDIT.2014.6996968

Chaddad, A., Colen, R.R., 2014. Statistical feature selection for enhanced detection of brain tumor, in: Proceedings of SPIE - The International Society for Optical Engineering. https://doi.org/10.1117/12.2062143

Chaddad, A., Zinn, P.O., Colen, R.R., 2014d. Brain tumor identification using Gaussian Mixture Model features and Decision Trees classifier, in: 2014 48th Annual Conference on Information Sciences and Systems, CISS 2014. https://doi.org/10.1109/CISS.2014.6814077

Chaddad, A., 2014. Brain function evaluation using enhanced fNIRS signals extraction, in: 2014 48th Annual Conference on Information Sciences and Systems, CISS 2014. https://doi.org/10.1109/CISS.2014.6814079

Chaddad, A., Ahmad, F., Amin, M.G., Sevigny, P., Difilippo, D., 2014a. Textural feature selection for enhanced detection of stationary humans in through-The-wall radar imagery, in: Proceedings of SPIE - The International Society for Optical Engineering. https://doi.org/10.1117/12.2049416

Chaddad, A., Tanougast, C., Dandache, A., 2014b. Snake method enhanced using canny approach implementation for cancer cells detection in real time, in: BIODEVICES 2014 - 7th Int. Conference on Biomedical Electronics and Devices, Proceedings; Part of 7th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014. pp. 187–192.

Boutalbi, M., Frihi, M., Toumi, S., Tanougast, C., Killian, C., Chaddad, A., Dandache, A., 2013. Reliable router for accurate online error detection in dynamic Network on Chip, in: 2013 25th International Conference on Microelectronics, ICM 2013. https://doi.org/10.1109/ICM.2013.6735021

Chaddad, A., Maamoun, M., Tanougast, C., Dandache, A., 2013b. Hardware implementation of active contour algorithm for fast cancer cells detection, in: Proceedings - 29th Southern Biomedical Engineering Conference, SBEC 2013. pp. 129–130. https://doi.org/10.1109/SBEC.2013.73

Chaddad, A., Kamrani, E., Le Lan, J., Sawan, M., 2013a. Denoising fNIRS signals to enhance brain imaging diagnosis, in: Proceedings - 29th Southern Biomedical Engineering Conference, SBEC 2013. pp. 33–34. https://doi.org/10.1109/SBEC.2013.25

Kamrani, E., Chaddad, A., Lesage, F., Sawan, M., 2013. Integrated transimpedance amplifiers dedicated to low-noise and low-power biomedical applications, in: Proceedings - 29th Southern Biomedical Engineering Conference, SBEC 2013. pp. 5–6. https://doi.org/10.1109/SBEC.2013.11

Chaddad, A., Tanougast, C., Dandache, A., Bouridane, A., 2011b. Extraction of Haralick features from segmented texture multispectral bio-images for detection of colon cancer cells, in: Proceedings - 1st International Conference on Informatics and Computational Intelligence, ICI 2011. pp. 55–59. https://doi.org/10.1109/ICI.2011.20

Chaddad, A., Tanougast, C., Dandache, A., Al Houseini, A., Bouridane, A., 2011a. Improving of colon cancer cells detection based on Haralick’s features on segmented histopathological images, in: ICCAIE 2011 - 2011 IEEE Conference on Computer Applications and Industrial Electronics. pp. 87–90. https://doi.org/10.1109/ICCAIE.2011.6162110

Chaddad, A., Tanougast, C., Dandache, A., Bouridane, A., 2011d. Classification of cancer cells based on morphological features from segmented multispectral bio-images, in: Recent Advances in Applied and Biomedical Informatics and Computational Engineering in Systems Applications - AIC’11, BEBI’11. pp. 92–97. Publisher Site
Peer-reviewed abstract

Leduc N, Giraud N, Gandaglia G, Mathieu R, Ploussard G, Niazi T, Chaddad A., Vinh-Hung V, Sargos P, Beauval J-B, “Predicting biochemical recurrence after prostatectomy: can Machine Learning beat CAPRA score? Results of a multicentric retrospective analysis on 4700 patients”. https://ascopubs.org/doi/abs/10.1200/JCO.2020.38.6_suppl.343

Daniel P, Meehan B, Sabri S, Chaddad, A, et al. “Exploiting Molecular Subtype Cell Plasticity as Novel Strategy for Targeting Glioma Stem Cells Through Alternating Therapy”. https://www.redjournal.org/article/S0360-3016(18)32197-7/fulltext

Chaddad, A., Radiomic analysis of GBM patients: a preliminary study for predicting overall survival. First International Summit in Radiation Oncology. 2016.

Zinn PO, Luedi MM, Singh SK, Gumin J, Chaddad, A., Hatami M, Shojaee Bakhtiari A, Lang FF, Colen RR. Functional validation of radiogenomics with a pre-clinical orthotopic glioblastoma model. Congress of Neurological Surgeons 2015 Annual Meeting Proceedings (#17275), 9/2015.

Zinn PO, Chaddad, A., Colen RR. Texture based computational models of glioblastoma phenotypes in radiological images. Congress of Neurological Surgeons 2015 Annual Meeting Proceedings (#17451), 9/2015.

Zinn P, Luedi M, Singh S, Chaddad, A., Bakhtiari A, Sulman E, Lang F, Colen R. Targeting radiogenomics-derived core periostin correlated gene networks in glioblastoma - a novel treatment approach. American Association of Neurological Surgeons 83rd Annual Scientific Meeting Proceedings, 5/2015.

Colen R, Luedi M, Singh S, Chaddad, A., Bakhtiari A, Zinn P. Identification of gene specific MRI texture features in first radiogenomic model. American Society of Neuroradiology 53rd Annual Meeting Proceedings (#O-430), 4/2015.

Colen R, Bakhtiari A, Chaddad, A., Luedi, Zinn P. Radiomic subclassification of glioblastoma. American Society of Neuroradiology 53rd Annual Meeting Proceedings (#O-435), 4/2015.

Chaddad, A., Luedi M, Zinn P, Colen R. Texture analysis for assessing of Glioblastoma heterogeneity. American Society of Neuroradiology 53rd Annual Meeting Proceedings (#1589), 4/2015.

Chaddad, A., Zinn PO, Colen RR. Texture feature selection for enhanced assessing of glioblastoma heterogeneous. GAP 2015 Conference Proceedings, 4/2015.

Chaddad, A., Zinn PO, Colen RR. Extraction of phenotype texture features by waveletbased method in glioblastoma. American Society of Functional Neuroradiology 9th Annual Meeting Proceedings, 3/2015.

Chaddad, A., Zinn P, Colen RR. Wavelet based feature approach for radiomic texture extraction from glioblastoma phenotypes. J Nucl Med 56(2 (Suppl)):3, 2/2015.

Chaddad, A., Zinn PO, Colen RR. Abnormal cells discrimination using the different shape parameters. American Society of Neuroradiology 52nd Annual Meeting Proceedings (#EP-94), 5/2014.
Chaddad, A., Zinn PO, Colen RR. Brain tumor identification using gaussian mixture model features and decision trees classifier. American Society of Neuroradiology 52nd Annual Meeting Proceedings (#O-829), 5/2014.

Chaddad, A., Colen RR, Zinn PO. Carcinoma cells type identification based on the texture analysis. American Society of Neuroradiology 52nd Annual Meeting Proceedings (#EP-228), 5/2014.

Colen RR, Chaddad, A., Zinn PO. Integrated imageomic analysis identifies clinically relevant imaging subtypes of glioblastoma. American Society of Neuroradiology 52nd Annual Meeting Proceedings (#O-371), 5/2014.

Ashour OZ, Chaddad, A., Zinn PO, Colen RR. Introduction to segmentation, registration and volume analysis for imaging genomics. American Society of Neuroradiology 52nd Annual Meeting Proceedings (#eEdE-08), 5/2014.