About Dr. Mohamed Naser
Dr. Naser is a faculty member in the Department of Radiation Oncology at the University of Texas MD Anderson Cancer Center. His research focuses on improving cancer treatment efficacy and quality of life for cancer survivors through the development of novel machine learning and deep learning tools and imaging biomarkers for quantitative medical imaging in head and neck cancer (HNC) patients. Dr. Naser has an established portfolio of work focused on developing deep learning approaches for automated segmentation of normal tissues and target volumes, as well as predicting treatment outcomes and associated normal tissue injury. His deep learning-based approaches for outcome prediction achieved the first rank in the HECKTOR challenge 2021, a medical imaging competition for outcome prediction in HNC patients. Dr. Naser has also been awarded an NIH Administrative Supplement grant (DE028290) with Dr. Clifton Fuller for head and neck cancer data curation and organizing segmentation and outcome prediction challenge. Dr. Naser is leading the artificial intelligence (AI) group, which includes postdoctoral fellows and graduate students in PI Fuller's lab, to develop state-of-the-art deep learning auto-segmentation tools for tumors, lymph nodes, organs at risk, and skeletal muscle, as well as deep learning models for toxicity and outcome prediction. Dr. Naser has a wide background in different research areas, including electronics and communication engineering, engineering physics, medical physics, and radiation oncology, with advanced skills in software programming and advanced computational and artificial intelligence methods. Dr. Naser has an excellent publication record in top internationally recognized journals and has contributed to establishing research infrastructure for multiple successful grant applications.
Present Title & Affiliation
Instructor, Department of Radiation Oncology - Research, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
Education & Training
|2010||McMaster University, Hamilton, CAN, PHD, Engineering Physics|
|2005||Cairo University, Cairo, EGY, MA, Engineering Physics|
|2000||Cairo University, Cairo, EGY, BA, Electronics and Communication|
|2009-2013||Research Fellowship, Medical Physics, McMaster University, Hamilton|
Experience & Service
Research Scientist, Department of Radiation Oncology - Research, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 2020 - 2022
Assistant Professor, Department of Electrical and Computer Engineering, McMaster University, Hamilton, 2019 - 2019
Research Engineer, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 2016 - 2019
Assistant Professor, Department of Electrical and Computer Engineering, McMaster University, Hamilton, 2016 - 2016
Assistant Professor, Department of Electrical and Computer Engineering, McMaster University, Hamilton, 2014 - 2014
Assistant Professor, Department of Electrical Engineering, King Faisal University, Hofuf, 2014 - 2016
Honors & Awards
|2021||MICCAI HECKTOR Challenge Award 2021, Bioemission technology solutions|
|2009||Ontario Ministry of Research and Innovation Postdoctoral Fellowship, The Ministry of Research and Innovation of Ontario|
- Naser MA, Wahid KA, Ahmed S, Salama V, Dede C, Edwards BW, Lin R, McDonald B, Salzillo TC, He R, Ding Y, Abdelaal MA, Thill D, O'Connell N, Willcut V, Christodouleas JP, Lai SY, Fuller CD, Mohamed ASR. Quality assurance assessment of intra-acquisition diffusion-weighted and T2-weighted magnetic resonance imaging registration and contour propagation for head and neck cancer radiotherapy. Med Phys. e-Pub 2022. PMID: 36519973.
- Wahid KA, Glerean E, Sahlsten J, Jaskari J, Kaski K, Naser MA, He R, Mohamed ASR, Fuller CD. Artificial Intelligence for Radiation Oncology Applications Using Public Datasets. Semin Radiat Oncol 32(4):400-414, 2022. PMID: 36202442.
- Taku N, Wahid KA, van Dijk LV, Sahlsten J, Jaskari J, Kaski K, Fuller CD, Naser MA. Auto-detection and segmentation of involved lymph nodes in HPV-associated oropharyngeal cancer using a convolutional deep learning neural network. Clin Transl Radiat Oncol 36:47-55, 2022. e-Pub 2022. PMID: 35782963.
- Wahid KA, Olson B, Jain R, Grossberg AJ, El-Habashy D, Dede C, Salama V, Abobakr M, Mohamed ASR, He R, Jaskari J, Sahlsten J, Kaski K, Fuller CD, Naser MA. Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer. Sci Data 9(1):470, 2022. e-Pub 2022. PMID: 35918336.
- Oreiller V, Andrearczyk V, Jreige M, Boughdad S, Elhalawani H, Castelli J, Vallières M, Zhu S, Xie J, Peng Y, Iantsen A, Hatt M, Yuan Y, Ma J, Yang X, Rao C, Pai S, Ghimire K, Feng X, Naser MA, Fuller CD, Yousefirizi F, Rahmim A, Chen H, Wang L, Prior JO, Depeursinge A. Head and neck tumor segmentation in PET/CT: The HECKTOR challenge. Med Image Anal 77:102336, 2022. e-Pub 2021. PMID: 35016077.
- Naser MA, Wahid KA, Mohamed ASR, Abdelaal MA, He R, Dede C, van Dijk LV, Fuller CD. Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data. Head Neck Tumor Segm Chall (2021) 13209:287-299, 2022. e-Pub 2022. PMID: 35399868.
- Naser MA, Wahid KA, van Dijk LV, He R, Abdelaal MA, Dede C, Mohamed ASR, Fuller CD. Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PET/CT Images. Head Neck Tumor Segm Chall (2021) 13209:121-132, 2022. e-Pub 2022. PMID: 35399869.
- Wahid KA, He R, Dede C, Mohamed ASR, Abdelaal MA, van Dijk LV, Fuller CD, Naser MA. Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma. Head Neck Tumor Segm Chall (2021) 13209:300-307, 2022. e-Pub 2022. PMID: 35399870.
- Naser MA, Wahid KA, Grossberg AJ, Olson B, Jain R, El-Habashy D, Dede C, Salama V, Abobakr M, Mohamed ASR, He R, Jaskari J, Sahlsten J, Kaski K, Fuller CD. Deep learning auto-segmentation of cervical skeletal muscle for sarcopenia analysis in patients with head and neck cancer. Front Oncol 12:930432, 2022. e-Pub 2022. PMID: 35965493.
- Wahid KA, Xu J, El-Habashy D, Khamis Y, Abobakr M, McDonald B, O' Connell N, Thill D, Ahmed S, Sharafi CS, Preston K, Salzillo TC, Mohamed ASR, He R, Cho N, Christodouleas J, Fuller CD, Naser MA. Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy. Front Oncol 12:975902, 2022. e-Pub 2022. PMID: 36425548.
- Wahid KA, Ahmed S, He R, van Dijk LV, Teuwen J, McDonald BA, Salama V, Mohamed ASR, Salzillo T, Dede C, Taku N, Lai SY, Fuller CD, Naser MA. Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry. Clin Transl Radiat Oncol 32:6-14, 2022. e-Pub 2021. PMID: 34765748.
- van Dijk LV, Abusaif AA, Rigert J, Naser MA, Hutcheson KA, Lai SY, Fuller CD, Mohamed ASR, on behalf on the MD Anderson Symptom Working Group. Normal Tissue Complication Probability (NTCP) Prediction Model for Osteoradionecrosis of the Mandible in Patients With Head and Neck Cancer After Radiation Therapy: Large-Scale Observational Cohort. Int J Radiat Oncol Biol Phys 111(2):549-558, 2021. e-Pub 2021. PMID: 33965514.
- Wahid KA, He R, McDonald BA, Anderson BM, Salzillo T, Mulder S, Wang J, Sharafi CS, McCoy LA, Naser MA, Ahmed S, Sanders KL, Mohamed ASR, Ding Y, Wang J, Hutcheson K, Lai SY, Fuller CD, van Dijk LV. Intensity standardization methods in magnetic resonance imaging of head and neck cancer. Phys Imaging Radiat Oncol 20:88-93, 2021. e-Pub 2021. PMID: 34849414.
- Naser MA, van Dijk LV, He R, Wahid KA, Fuller CD. Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET/CT Images. Head Neck Tumor Segm (2020) 12603:85-98, 2021. e-Pub 2021. PMID: 33724743.
- Naser MA, Deen MJ. Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images. Comput Biol Med 121:103758, 2020. e-Pub 2020. PMID: 32568668.
- Naser MA, Sampaio DRT, Munoz NM, Wood CA, Mitcham TM, Stefan W, Sokolov KV, Pavan TZ, Avritscher R, Bouchard RR. Improved Photoacoustic-Based Oxygen Saturation Estimation With SNR-Regularized Local Fluence Correction. IEEE Trans Med Imaging 38(2):561-571, 2019. e-Pub 2018. PMID: 30207951.
- Alayed M, Naser MA, Aden-Ali I, Deen MJ. Time-resolved diffuse optical tomography system using an accelerated inverse problem solver. Opt Express 26(2):963-979, 2018. PMID: 29401984.
- Naser MA, Patterson MS, Wong JW. Algorithm for localized adaptive diffuse optical tomography and its application in bioluminescence tomography. Phys Med Biol 59(8):2089-109, 2014. e-Pub 2014. PMID: 24694875.
- Naser MA, Patterson MS, Wong JW. Self-calibrated algorithms for diffuse optical tomography and bioluminescence tomography using relative transmission images. Biomed Opt Express 3(11):2794-808, 2012. e-Pub 2012. PMID: 23162719.
- Naser MA, Patterson MS. Bioluminescence tomography using eigenvectors expansion and iterative solution for the optimized permissible source region. Biomed Opt Express 2(11):3179-93, 2011. e-Pub 2011. PMID: 22076277.
- Naser MA, Patterson MS. Improved bioluminescence and fluorescence reconstruction algorithms using diffuse optical tomography, normalized data, and optimized selection of the permissible source region. Biomed Opt Express 2(1):169-84, 2010. e-Pub 2010. PMID: 21326647.
- Naser MA, Patterson MS. Algorithms for bioluminescence tomography incorporating anatomical information and reconstruction of tissue optical properties. Biomed Opt Express 1(2):512-526, 2010. e-Pub 2010. PMID: 21258486.
Grant & Contract Support
|Title:||Statistical Learning Modeling to Improve Head and Neck Dysphagia Prediction (STOP HN Dysphagia)|
|Title:||Probabilistic Deep Learning Cervical Lymph-Node Auto-Segmentation for Imaging-enhanced Evaluation of Extracapsular Nodal Extension Risk (PDL-CLASIFIER)|
|Title:||Administrative Supplement: Development of functional magnetic resonance imaging-guided adaptive radiotherapy for head and neck cancer|
|Title:||Development of functional magnetic resonance imaging-guided adaptive radiotherapy for head and neck cancer patients using novel MR-Linac device|
|Title:||Imaging-based quantitative analysis of vascular perfusion and tissue oxygenation to improve therapy of hepatocellular carcinoma|
|Funding Source:||Cancer Prevention & Research Institute of Texas (CPRIT)|