Robert Jeraj

10.6k total citations
232 papers, 5.4k citations indexed

About

Robert Jeraj is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Radiation. According to data from OpenAlex, Robert Jeraj has authored 232 papers receiving a total of 5.4k indexed citations (citations by other indexed papers that have themselves been cited), including 164 papers in Radiology, Nuclear Medicine and Imaging, 100 papers in Pulmonary and Respiratory Medicine and 84 papers in Radiation. Recurrent topics in Robert Jeraj's work include Medical Imaging Techniques and Applications (124 papers), Advanced Radiotherapy Techniques (77 papers) and Radiomics and Machine Learning in Medical Imaging (71 papers). Robert Jeraj is often cited by papers focused on Medical Imaging Techniques and Applications (124 papers), Advanced Radiotherapy Techniques (77 papers) and Radiomics and Machine Learning in Medical Imaging (71 papers). Robert Jeraj collaborates with scholars based in United States, Slovenia and Belgium. Robert Jeraj's co-authors include Paul Keall, Scott B. Perlman, G Olivera, T Mackie, Bhudatt Paliwal, M Vanderhoek, Chuan Wu, T Mackie, Ngoneh Jallow and Jeffrey V. Siebers and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Robert Jeraj

220 papers receiving 5.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Robert Jeraj United States 43 3.6k 2.6k 2.5k 929 613 232 5.4k
Yusuf E. Erdi United States 43 5.3k 1.5× 2.5k 1.0× 2.3k 1.0× 1.1k 1.2× 684 1.1× 79 6.4k
Shiva K. Das United States 34 2.4k 0.7× 2.3k 0.9× 2.1k 0.9× 1.2k 1.3× 510 0.8× 128 4.9k
Takashi Mizowaki Japan 39 3.1k 0.9× 3.9k 1.5× 4.1k 1.7× 821 0.9× 652 1.1× 423 6.2k
Dirk Verellen Belgium 43 3.4k 1.0× 4.7k 1.8× 3.6k 1.5× 938 1.0× 758 1.2× 237 6.6k
Ursula Nestle Germany 36 3.2k 0.9× 2.0k 0.8× 3.7k 1.5× 330 0.4× 1.2k 2.0× 163 5.7k
Frank Lohr Germany 41 2.6k 0.7× 3.6k 1.4× 3.3k 1.3× 802 0.9× 727 1.2× 233 6.2k
X. Allen Li United States 44 4.1k 1.1× 4.6k 1.8× 3.6k 1.5× 859 0.9× 993 1.6× 179 7.1k
T Mackie United States 33 2.7k 0.8× 3.9k 1.5× 2.8k 1.1× 989 1.1× 233 0.4× 79 5.0k
Moyed Miften United States 36 3.1k 0.9× 3.6k 1.4× 2.6k 1.1× 1.0k 1.1× 418 0.7× 141 5.0k
Ying Xiao United States 37 3.7k 1.0× 4.2k 1.6× 3.4k 1.4× 1.2k 1.3× 435 0.7× 240 6.5k

Countries citing papers authored by Robert Jeraj

Since Specialization
Citations

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

Fields of papers citing papers by Robert Jeraj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Robert Jeraj

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

All Works

20 of 20 papers shown
1.
Sharp, G, et al.. (2025). Probabilistic clinical target definition with nearest neighbor correlation. Physics in Medicine and Biology. 71(1). 15031–15031.
2.
Wagner, Tobias, Lesley Cockmartin, Nicholas Marshall, et al.. (2025). Using Explainable AI to Characterize Features in the Mirai Mammographic Breast Cancer Risk Prediction Model. Radiology Artificial Intelligence. 7(6). e240417–e240417.
3.
4.
Kyriakopoulos, Christos E., Yu‐Hui Chen, Robert Jeraj, et al.. (2024). Cabazitaxel with abiraterone versus abiraterone alone randomized trial for extensive disease following docetaxel: The CHAARTED2 trial of the ECOG-ACRIN Cancer Research Group (EA8153).. Journal of Clinical Oncology. 42(17_suppl). LBA5000–LBA5000. 2 indexed citations
5.
Smolders, Andreas, Stine Korreman, Antony Lomax, et al.. (2024). DiffuseRT: predicting likely anatomical deformations of patients undergoing radiotherapy. Physics in Medicine and Biology. 69(15). 155016–155016. 6 indexed citations
6.
Wagner, Tobias, et al.. (2024). Incorporating longitudinal screening data into image-based breast cancer risk assessment. Lirias (KU Leuven). 66–66. 2 indexed citations
7.
Francis, Roslyn J., et al.. (2023). Prospective inter- and intra-tracer repeatability analysis of radiomics features in [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007 PET scans in metastatic prostate cancer. British Journal of Radiology. 96(1152). 20221178–20221178. 6 indexed citations
8.
Unkelbach, Jan, Thomas Bortfeld, Carlos Cárdenas, et al.. (2020). The role of computational methods for automating and improving clinical target volume definition. Radiotherapy and Oncology. 153. 15–25. 31 indexed citations
9.
Bortfeld, Thomas, et al.. (2020). Optimal treatment plan adaptation using mid-treatment imaging biomarkers. Physics in Medicine and Biology. 65(24). 245011–245011. 6 indexed citations
10.
Weisman, Amy J., et al.. (2020). Retrospective quantitative harmonization in PET using deconvolution and optimal filtering. Bulletin of the American Physical Society. 1 indexed citations
11.
Vrankar, Martina, Mojca Unk, Andrej Studen, et al.. (2020). [ 18 F]FDG PET immunotherapy radiomics signature (iRADIOMICS) predicts response of non-small-cell lung cancer patients treated with pembrolizumab. Radiology and Oncology. 54(3). 285–294. 59 indexed citations
12.
Harmon, Stephanie A., Tyler Bradshaw, Jens C. Eickhoff, et al.. (2018). Response-to-repeatability of quantitative imaging features for longitudinal response assessment. Physics in Medicine and Biology. 64(2). 25019–25019. 6 indexed citations
13.
Simončič, Urban, et al.. (2018). Predicting tumour response to anti-PD-1 immunotherapy with computational modelling. Physics in Medicine and Biology. 64(2). 25017–25017. 20 indexed citations
14.
Grudzinski, Joseph J., John M. Floberg, Sarah R. Mudd, et al.. (2012). Application of a whole-body pharmacokinetic model for targeted radionuclide therapy to NM404 and FLT. Physics in Medicine and Biology. 57(6). 1641–1657. 10 indexed citations
15.
16.
Rodriguez, M & Robert Jeraj. (2008). Design of a radiation facility for very small specimens used in radiobiology studies. Physics in Medicine and Biology. 53(11). 2953–2970. 7 indexed citations
17.
Ballegeer, Elizabeth A., Lisa J. Forrest, Robert Jeraj, T. Rockwell Mackie, & Robert J. Nickles. (2006). PET/CT FOLLOWING INTENSITY‐MODULATED RADIATION THERAPY FOR PRIMARY LUNG TUMOR IN A DOG. Veterinary Radiology & Ultrasound. 47(2). 228–233. 25 indexed citations
18.
Sheng, Ke, et al.. (2005). Imaging dose management using multi-resolution in CT-guided radiation therapy. Physics in Medicine and Biology. 50(6). 1205–1219. 6 indexed citations
19.
Kissick, M, John D. Fenwick, John James, et al.. (2005). The helical tomotherapy thread effect. Medical Physics. 32(5). 1414–1423. 113 indexed citations
20.
Jeraj, Robert, T Mackie, John Balog, et al.. (2004). Radiation characteristics of helical tomotherapy. Medical Physics. 31(2). 396–404. 157 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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