Sharif Elguindi

550 total citations
17 papers, 355 citations indexed

About

Sharif Elguindi is a scholar working on Radiation, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Sharif Elguindi has authored 17 papers receiving a total of 355 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Radiation, 12 papers in Radiology, Nuclear Medicine and Imaging and 6 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Sharif Elguindi's work include Advanced Radiotherapy Techniques (12 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and Radiation Dose and Imaging (5 papers). Sharif Elguindi is often cited by papers focused on Advanced Radiotherapy Techniques (12 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and Radiation Dose and Imaging (5 papers). Sharif Elguindi collaborates with scholars based in United States, Netherlands and Canada. Sharif Elguindi's co-authors include Erin F. Gillespie, Joseph O. Deasy, Michael J. Zeléfsky, Simon Duke, Max Dahele, Li Tee Tan, Jue Jiang, Michael V. Sherer, Jon Cacicedo and Harini Veeraraghavan and has published in prestigious journals such as International Journal of Radiation Oncology*Biology*Physics, Physics in Medicine and Biology and Medical Physics.

In The Last Decade

Sharif Elguindi

16 papers receiving 352 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sharif Elguindi United States 8 240 210 99 90 59 17 355
Joshua Giambattista Canada 6 217 0.9× 184 0.9× 87 0.9× 81 0.9× 51 0.9× 16 308
Carter Kolbeck Canada 7 208 0.9× 182 0.9× 93 0.9× 80 0.9× 64 1.1× 15 351
Dao Lam United States 7 247 1.0× 213 1.0× 114 1.2× 122 1.4× 40 0.7× 9 339
Anna M. Dinkla Netherlands 7 390 1.6× 363 1.7× 144 1.5× 129 1.4× 57 1.0× 13 526
Anjali Balagopal United States 8 221 0.9× 180 0.9× 113 1.1× 105 1.2× 47 0.8× 11 346
Rachel McCarroll United States 10 339 1.4× 323 1.5× 138 1.4× 101 1.1× 62 1.1× 19 475
Nuo Tong China 7 206 0.9× 105 0.5× 35 0.4× 127 1.4× 60 1.0× 16 309
Haonan Xiao Hong Kong 11 227 0.9× 93 0.4× 61 0.6× 70 0.8× 41 0.7× 23 302
Jiawei Fan China 11 402 1.7× 439 2.1× 199 2.0× 133 1.5× 58 1.0× 29 553
Colien Hazelaar Netherlands 10 268 1.1× 272 1.3× 124 1.3× 164 1.8× 25 0.4× 17 406

Countries citing papers authored by Sharif Elguindi

Since Specialization
Citations

This map shows the geographic impact of Sharif Elguindi'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 Sharif Elguindi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sharif Elguindi more than expected).

Fields of papers citing papers by Sharif Elguindi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sharif Elguindi. 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 Sharif Elguindi. The network helps show where Sharif Elguindi may publish in the future.

Co-authorship network of co-authors of Sharif Elguindi

This figure shows the co-authorship network connecting the top 25 collaborators of Sharif Elguindi. A scholar is included among the top collaborators of Sharif Elguindi 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 Sharif Elguindi. Sharif Elguindi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Thor, Maria, Vonetta M. Williams, Laura Cerviño, et al.. (2025). Under-representation for Female Pelvis Cancers in Commercial Auto-segmentation Solutions and Open-source Imaging Datasets. Clinical Oncology. 38. 103651–103651.
2.
Hope, Andrew, Jan‐Jakob Sonke, John Kang, et al.. (2025). Three discipline collaborative radiation therapy (3DCRT) special debate: AI structure segmentation is better than clinician contouring for both OARs and targets. Journal of Applied Clinical Medical Physics. 26(7). e70183–e70183. 1 indexed citations
3.
Liu, Yan, et al.. (2024). Retreatment Review for Abdominal Cancer: Assessing the Impact of Deformable Image Registration and AI Auto-Segmentation on Dose Accumulation. International Journal of Radiation Oncology*Biology*Physics. 120(2). e160–e161. 1 indexed citations
4.
Jiang, Jingting, et al.. (2023). Clinical Feasibility of Deep Learning-Based CT during Treatment CBCT Tumor Registration-Segmentation in Thoracic Radiotherapy (RT). International Journal of Radiation Oncology*Biology*Physics. 117(2). e656–e656. 1 indexed citations
5.
Jiang, Jue, Sharif Elguindi, Sean L. Berry, et al.. (2022). Nested block self‐attention multiple resolution residual network for multiorgan segmentation from CT. Medical Physics. 49(8). 5244–5257. 15 indexed citations
6.
Sherer, Michael V., Diana Lin, Sharif Elguindi, et al.. (2021). Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review. Radiotherapy and Oncology. 160. 185–191. 129 indexed citations
7.
Elguindi, Sharif, et al.. (2021). Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy. Radiotherapy and Oncology. 159. 1–7. 70 indexed citations
8.
Thor, Maria, Aditi Iyer, Jue Jiang, et al.. (2021). Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy. Physics and Imaging in Radiation Oncology. 19. 96–101. 13 indexed citations
9.
Iyer, Aditi, Maria Thor, Kaveh Zakeri, et al.. (2021). Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT. Physics in Medicine and Biology. 67(2). 24001–24001. 13 indexed citations
10.
Gorovets, Daniel, Laura Happersett, Sharif Elguindi, et al.. (2021). Minimum Bladder Reference Contour to Guide Bladder Filling for Prostate Radiotherapy. International Journal of Radiation Oncology*Biology*Physics. 111(3). e274–e275. 1 indexed citations
11.
Apte, Aditya, Aditi Iyer, Maria Thor, et al.. (2020). Library of deep-learning image segmentation and outcomes model-implementations. Physica Medica. 73. 190–196. 17 indexed citations
12.
Berry, Sean L., Ying Zhou, Hai Pham, et al.. (2020). Efficiency and safety increases after the implementation of a multi‐institutional automated plan check tool at our institution. Journal of Applied Clinical Medical Physics. 21(4). 51–58. 9 indexed citations
13.
Happersett, Laura, Antonio L. Damato, Sharif Elguindi, & Daniel Gorovets. (2020). Minimum Bladder Reference Contour To Guide Patient Setup For Prostate Radiotherapy. International Journal of Radiation Oncology*Biology*Physics. 108(3). e899–e899. 1 indexed citations
14.
Elguindi, Sharif, Michael J. Zeléfsky, Jue Jiang, et al.. (2019). Deep learning-based auto-segmentation of targets and organs-at-risk for magnetic resonance imaging only planning of prostate radiotherapy. Physics and Imaging in Radiation Oncology. 12. 80–86. 75 indexed citations
15.
Berry, Sean L., et al.. (2017). Five years’ experience with a customized electronic checklist for radiation therapy planning quality assurance in a multicampus institution. Practical Radiation Oncology. 8(4). 279–286. 6 indexed citations
16.
Anand, Aman, et al.. (2014). Spot Scanned Proton Treatment Could Potentially Reduce Hemotoxicity in Patients Being Treated With Radiation for Endometrial and Cervix Cancer. International Journal of Radiation Oncology*Biology*Physics. 90(1). S927–S927. 1 indexed citations
17.
Elguindi, Sharif, et al.. (1975). Endocrine changes in hepatosplenic schistosomiasis (H.S.S.). An experimental study.. 26(1). 81–90. 2 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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