Ganesh Narayanasamy

Assistant Professor

University of Arkansas for Medical Sciences

faculty

15 h-index 138 pubs 872 cited

Research Areas

Medical Imaging Techniques and Applications Advanced Neural Network Applications Radiation Therapy and Dosimetry Cancer Treatment and Pharmacology Radiomics and Machine Learning in Medical Imaging Health and Medical Research Impacts Meta-analysis and systematic reviews Computational Drug Discovery Methods

Links

ORCID Lab Website

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OverviewAI-generated summary

Ganesh Narayanasamy's research focuses on the application of advanced computational methods and statistical techniques to radiation oncology and medical imaging. His work includes the development and validation of convolutional neural networks for detecting vertebral body misalignments in radiographic images, aiming to improve treatment accuracy. Narayanasamy has investigated the clinical impact of discrepancies between planned and delivered radiation doses in prostate cancer radiotherapy, utilizing patient-reported outcomes. He has also explored the use of the normal tissue non-complication probability (NTCP0) methodology as an alternative for assessing side effects in brachytherapy and other radiation oncology treatments. His publications also address the application of Bayesian statistics in the field of ionizing radiation and risk factors for radiation necrosis following cranial radiation. Narayanasamy has co-authored 27 publications, with a total of 149 citations and an h-index of 6. He collaborates with researchers at the University of Arkansas for Medical Sciences, including Caleb J. Nissen.

Metrics

h-index: 15
Publications: 138
Citations: 872

Selected Publications

AAPM task group report 210: Conventional linear accelerator acceptance testing (2025) DOI
Producing high quality cranial SRS plans with 4Pi planning technique in a commercial clinical solution (2025) DOI
The effective normal tissue non-complication probability (E0): a probabilistic methodology in the representation of the stochastic health safety effects of low levels of ionizing radiation on the human tissues or organs (2025) DOI
Use of the Bayesian statistics and the product of probabilities in the ionizing radiation field (2024) DOI
Cleaning the dose falloff with low modulation in SBRT lung plans (2023) DOI
Development and multi‐institutional validation of a convolutional neural network to detect vertebral body mis‐alignments in 2D x‐ray setup images (2023) DOI
Use of the Bayesian statistics and the product of probabilities in the ionizing radiation field (2023) DOI
Evaluation of the clinical impact of the differences between planned and delivered dose in prostate cancer radiotherapy based on CT‐on‐rails IGRT and patient‐reported outcome scores (2022) DOI
The use of the normal tissue non-complication probability (NTCP0) in the safety evaluations as a new alternative of assessing the side-effects of the radiation oncology treatments (2022) DOI
A phantom-based study and clinical implementation of brainlab’s treatment planning system for radiosurgical treatments of arteriovenous malformations (2022) DOI
The use of the normal tissue non-complication probability (NTCP0) methodology as a new alternative of assessing side-effects in brachytherapy treatments (2022) DOI
A dosimetric comparative analysis of Brainlab elements and Eclipse RapidArc for spine SBRT treatment planning (2022) DOI
A treatment planning system with new paradigms in the effectiveness and side-effect evaluation sections (2021) DOI
Evaluation of the Dose Delivery Consistency and Its Dependence on Imaging Modality and Deformable Image Registration Algorithm in Prostate Cancer Patients (2021) DOI
Output factor measurements with multiple detectors in CyberKnife® Robotic Radiosurgery System (2021) DOI