Mohamed A. Naser

1.7k total citations
61 papers, 828 citations indexed

About

Mohamed A. Naser is a scholar working on Radiology, Nuclear Medicine and Imaging, Otorhinolaryngology and Biomedical Engineering. According to data from OpenAlex, Mohamed A. Naser has authored 61 papers receiving a total of 828 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Radiology, Nuclear Medicine and Imaging, 19 papers in Otorhinolaryngology and 16 papers in Biomedical Engineering. Recurrent topics in Mohamed A. Naser's work include Radiomics and Machine Learning in Medical Imaging (22 papers), Head and Neck Cancer Studies (19 papers) and Advanced Radiotherapy Techniques (13 papers). Mohamed A. Naser is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (22 papers), Head and Neck Cancer Studies (19 papers) and Advanced Radiotherapy Techniques (13 papers). Mohamed A. Naser collaborates with scholars based in United States, Canada and Finland. Mohamed A. Naser's co-authors include M. Jamal Deen, Michael S. Patterson, Clifton D. Fuller, D. A. Thompson, Kareem A. Wahid, Abdallah Mohamed, Renjie He, Lisanne V. van Dijk, Stephen Y. Lai and Brigid A. McDonald and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Applied Physics and International Journal of Radiation Oncology*Biology*Physics.

In The Last Decade

Mohamed A. Naser

53 papers receiving 809 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mohamed A. Naser United States	15	464	243	231	187	134	61	828
Zhe Guo China	10	217 0.5×	80 0.3×	50 0.2×	134 0.7×	38 0.3×	36	576
Michael Perkuhn Germany	16	630 1.4×	185 0.8×	113 0.5×	97 0.5×	163 1.2×	18	958
Hesheng Wang United States	18	627 1.4×	255 1.0×	71 0.3×	163 0.9×	341 2.5×	77	1.1k
Amirhossein Sanaat Switzerland	17	786 1.7×	356 1.5×	33 0.1×	84 0.4×	174 1.3×	57	928
Keh‐Shih Chuang Taiwan	15	352 0.8×	187 0.8×	17 0.1×	171 0.9×	111 0.8×	78	704
Floris F. Berendsen Netherlands	8	490 1.1×	235 1.0×	53 0.2×	432 2.3×	85 0.6×	13	859
Walter G. O’Dell United States	18	709 1.5×	332 1.4×	40 0.2×	191 1.0×	217 1.6×	46	1.2k
A. Isambert France	12	307 0.7×	138 0.6×	16 0.1×	83 0.4×	435 3.2×	43	678
Renjie He United States	13	246 0.5×	85 0.3×	19 0.1×	32 0.2×	103 0.8×	48	440
Joseph Harms United States	15	590 1.3×	288 1.2×	17 0.1×	157 0.8×	468 3.5×	54	916

Countries citing papers authored by Mohamed A. Naser

Since Specialization

Citations

This map shows the geographic impact of Mohamed A. Naser's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mohamed A. Naser with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohamed A. Naser more than expected).

Fields of papers citing papers by Mohamed A. Naser

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohamed A. Naser. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mohamed A. Naser. The network helps show where Mohamed A. Naser may publish in the future.

Co-authorship network of co-authors of Mohamed A. Naser

This figure shows the co-authorship network connecting the top 25 collaborators of Mohamed A. Naser. A scholar is included among the top collaborators of Mohamed A. Naser based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mohamed A. Naser. Mohamed A. Naser is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Mohamed, Abdallah, Jacob G. Scott, James E. Bates, et al.. (2025). Optimal timing of organs-at-risk-sparing adaptive radiation therapy for head-and-neck cancer under re-planning resource constraints. Physics and Imaging in Radiation Oncology. 33. 100715–100715.

Naser, Mohamed A., Steven J. Frank, Brad G. Smith, et al.. (2025). Evaluating observer reliability and diagnostic accuracy of CT-LEFAT criteria for post-treatment head and neck lymphedema: A prospective blinded comparative analysis. Oral Oncology. 164. 107265–107265.

Sahlsten, Jaakko, Joel Jaskari, Kareem A. Wahid, et al.. (2024). Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning. SHILAP Revista de lepidopterología. 4(1). 110–110. 6 indexed citations

Koutroumpakis, Efstratios, Abdallah Mohamed, David I. Rosenthal, et al.. (2024). Longitudinal changes in the carotid arteries of head and neck cancer patients following radiation therapy: Results from a prospective serial imaging biomarker characterization study. Radiotherapy and Oncology. 195. 110220–110220. 2 indexed citations

Wahid, Kareem A., et al.. (2024). Deep Learning-Based Auto Contouring of Mandibular Sub-Volumes Based on the ClinRad System for Spatial Localization of Osteoradionecrosis of the Jaw. International Journal of Radiation Oncology*Biology*Physics. 120(2). e141–e142.

Farris, Dominic J., Amy C. Moreno, Mohamed A. Naser, et al.. (2024). Uncertainty Quantification in Machine Learning Radiotherapy Applications - A Scoping Review. International Journal of Radiation Oncology*Biology*Physics. 120(2). e631–e631.

Wahid, Kareem A., Carlos Cárdenas, Tucker Netherton, et al.. (2024). Evolving Horizons in Radiation Therapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification. Advances in Radiation Oncology. 9(7). 101521–101521. 3 indexed citations

Mohamed, Abdallah, Renjie He, Kareem A. Wahid, et al.. (2023). Prospective validation of diffusion-weighted MRI as a biomarker of tumor response and oncologic outcomes in head and neck cancer: Results from an observational biomarker pre-qualification study. Radiotherapy and Oncology. 183. 109641–109641. 13 indexed citations

Wahid, Kareem A., et al.. (2023). Deep Learning Based Prognostic Prediction in Oropharyngeal Cancer Patients Using Multiparametric MRI Inputs. International Journal of Radiation Oncology*Biology*Physics. 117(2). e631–e631. 1 indexed citations

10.

McDonald, Brigid A., Carlos Cárdenas, Sara Ahmed, et al.. (2023). Investigation of autosegmentation techniques on T2‐weighted MRI for off‐line dose reconstruction in MR‐linac workflow for head and neck cancers. Medical Physics. 51(1). 278–291. 6 indexed citations

11.

Mohamed, Abdallah, Renjie He, Kareem A. Wahid, et al.. (2023). Dynamic Contrast Enhanced MRI as a Biomarker of Tumor Response and Oncologic Outcomes in Head and Neck Cancer: Results of a Single Institution Prospective Imaging Study. International Journal of Radiation Oncology*Biology*Physics. 117(2). e677–e678. 1 indexed citations

12.

Wahid, Kareem A., Jaakko Sahlsten, Joel Jaskari, et al.. (2023). Harnessing uncertainty in radiotherapy auto-segmentation quality assurance. Physics and Imaging in Radiation Oncology. 29. 100526–100526. 4 indexed citations

13.

Almazah, Mohammed M. A., et al.. (2023). A New Bayesian Network-Based Generalized Weighting Scheme for the Amalgamation of Multiple Drought Indices. Complexity. 2023. 1–23. 1 indexed citations

14.

Wahid, Kareem A., Diana Lin, Onur Sahin, et al.. (2023). Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites. Scientific Data. 10(1). 161–161. 5 indexed citations

15.

Wahid, Kareem A., Brigid A. McDonald, Sara Ahmed, et al.. (2022). Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy. Frontiers in Oncology. 12. 1 indexed citations

16.

Naser, Mohamed A., Kareem A. Wahid, Abdallah Mohamed, et al.. (2022). Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data. Lecture notes in computer science. 13209. 287–299. 12 indexed citations

17.

Taku, Nicolette, Kareem A. Wahid, Lisanne V. van Dijk, et al.. (2022). Auto-detection and segmentation of involved lymph nodes in HPV-associated oropharyngeal cancer using a convolutional deep learning neural network. Clinical and Translational Radiation Oncology. 36. 47–55. 12 indexed citations

18.

Wahid, Kareem A., Renjie He, Brigid A. McDonald, et al.. (2021). Intensity standardization methods in magnetic resonance imaging of head and neck cancer. Physics and Imaging in Radiation Oncology. 20. 88–93. 22 indexed citations

19.

Wahid, Kareem A., Sara Ahmed, Renjie He, et al.. (2021). Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry. Clinical and Translational Radiation Oncology. 32. 6–14. 44 indexed citations

20.

Naser, Mohamed A., M. Jamal Deen, & D. A. Thompson. (2006). Spectral function of InAs∕InGaAs quantum dots in a well detector using Green’s function. Journal of Applied Physics. 100(9). 10 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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