Publications by authors named "M Naser"

Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)-driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge, a satellite event of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention.

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Background And Purpose: Prior work on adaptive organ-at-risk (OAR)-sparing radiation therapy has typically reported outcomes based on fixed-number or fixed-interval re-planning, which represent one-size-fits-all approaches and do not account for the variable progression of individual patients' toxicities. The purpose of this study was to determine the personalized optimal timing for re-planning in adaptive OAR-sparing radiation therapy, considering limited re-planning resources, for patients with head and neck cancer (HNC).

Materials And Methods: A novel Markov decision process (MDP) model was developed to determine optimal timing of re-planning based on the patient's expected toxicity, characterized by normal tissue complication probability (NTCP), for four toxicities.

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Article Synopsis
  • The study focuses on using radiomic features from contrast-enhanced CT scans to distinguish between osteoradionecrosis (ORN) and normal mandibular bone in head and neck cancer patients treated with radiotherapy.
  • Data from 150 patients was analyzed, with feature extraction performed using PyRadiomics and a Random Forest classifier used to identify key features, resulting in an accuracy of 88%.
  • The findings highlight specific radiomic features that can differentiate ORN from healthy tissue, paving the way for future research on early detection and intervention strategies.
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Background/purpose: The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. The purpose of this study was to scope existing literature related to UQ in RT, identify areas of improvement, and determine future directions.

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Vitamin C (VIT C) is an antioxidant that prevents skin aging. Although dermal delivery is one of the most effective routes to transport VIT C to the skin, the impact of this route can be limited by the barrier function of the (SC). Additionally, VIT C rapidly oxidized and degraded under light and temperature.

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