Mohamed Naser

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Assistant Professor

The University of Texas MD Anderson Cancer Center

United States

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About

Dr. Mohamed Naser is an Assistant Professor at The University of Texas MD Anderson Cancer Center in the United States. His research focuses on advanced imaging techniques and their applications in oncology, particularly concerning head and neck cancer. Recent publications highlight his work on computed tomography radiomics, observer reliability in diagnostic criteria, and the integration of artificial intelligence in cancer pain management.

Recent Grants

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Probabilistic Deep Learning Cervical Lymph-Node Auto-Segmentation For Imaging-enhanced Evaluation of Extracapsular Extension Risk (PDL-CLASIFIER)

Open Date: 2024-04-01

Close Date: 2026-03-31

Articles (10)

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2023

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2023

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2023

Collaborators (6)

Kimmo Kaski

Aalto University

FINLAND

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Natalie West

The University of Texas MD Anderson Cancer Center

UNITED STATES

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Michael Dohopolski

University of Texas Southwestern Medical Center

UNITED STATES

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Vivian Salama

Research Assistant Professor

West Virginia University

UNITED STATES

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Joel Jaskari

Aalto University

FINLAND

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Zaphanlene Kaffey

The University of Texas MD Anderson Cancer Center

UNITED STATES

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Mohamed Naser

Research Interests

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About

Recent Grants

Articles (10)

Evaluating observer reliability and diagnostic accuracy of CT-LEFAT criteria for post-treatment head and neck lymphedema: A prospective blinded comparative analysis

Computed tomography radiomics-based cross-sectional detection of mandibular osteoradionecrosis in head and neck cancer survivors

Detection of alteration in carotid artery volumetry using standard-of-care computed tomography surveillance scans following unilateral radiation therapy for early-stage tonsillar squamous cell carcinoma survivors: a cross-sectional internally-matched carotid isodose analysis

Optimal timing of organs-at-risk-sparing adaptive radiation therapy for head-and-neck cancer under re-planning resource constraints

Harnessing uncertainty in radiotherapy auto-segmentation quality assurance

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review

Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning

Development and implementation of optimized endogenous contrast sequences for delineation in adaptive radiotherapy on a 1.5T MR-linear-accelerator: a prospective R-IDEAL stage 0-2a quantitative/qualitative evaluation of in vivo site-specific quality-assurance using a 3D T2 fat-suppressed platform fo

Automated Sarcopenia Assessment and Outcomes in Head and Neck Cancer with Deep Learning Analysis of Cervical Neck Skeletal Muscle

Collaborators (6)

Kimmo Kaski

Natalie West

Michael Dohopolski

Vivian Salama

Joel Jaskari

Zaphanlene Kaffey

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