Thomas C. Booth

2.7k total citations
70 papers, 996 citations indexed

About

Thomas C. Booth is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Epidemiology. According to data from OpenAlex, Thomas C. Booth has authored 70 papers receiving a total of 996 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Radiology, Nuclear Medicine and Imaging, 17 papers in Genetics and 17 papers in Epidemiology. Recurrent topics in Thomas C. Booth's work include Radiomics and Machine Learning in Medical Imaging (22 papers), Glioma Diagnosis and Treatment (17 papers) and Acute Ischemic Stroke Management (10 papers). Thomas C. Booth is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (22 papers), Glioma Diagnosis and Treatment (17 papers) and Acute Ischemic Stroke Management (10 papers). Thomas C. Booth collaborates with scholars based in United Kingdom, United States and Germany. Thomas C. Booth's co-authors include Adam Waldman, Alan Jackson, Joanna M. Wardlaw, David Wood, Stuart A. Taylor, Jeremy Lynch, Haris Shuaib, Sébastien Ourselin, M. Jorge Cardoso and James H. Cole and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and NeuroImage.

In The Last Decade

Thomas C. Booth

62 papers receiving 980 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas C. Booth United Kingdom 20 442 201 185 142 109 70 996
Florian Putz Germany 16 300 0.7× 214 1.1× 220 1.2× 64 0.5× 129 1.2× 91 945
Houman Sotoudeh United States 17 331 0.7× 130 0.6× 129 0.7× 200 1.4× 189 1.7× 74 814
Hiroyuki Uetani Japan 18 666 1.5× 217 1.1× 147 0.8× 245 1.7× 89 0.8× 89 1.1k
Inpyeong Hwang South Korea 17 511 1.2× 114 0.6× 204 1.1× 114 0.8× 142 1.3× 64 938
Michael Perkuhn Germany 16 630 1.4× 101 0.5× 165 0.9× 123 0.9× 132 1.2× 18 958
Luke Macyszyn United States 16 410 0.9× 274 1.4× 83 0.4× 125 0.9× 51 0.5× 44 975
Bihong T. Chen United States 18 475 1.1× 198 1.0× 360 1.9× 102 0.7× 45 0.4× 91 1.2k
Ryo Kurokawa Japan 15 227 0.5× 137 0.7× 100 0.5× 207 1.5× 70 0.6× 124 829
Ramona Woitek Austria 26 1.1k 2.6× 129 0.6× 248 1.3× 116 0.8× 123 1.1× 88 2.0k
David Zopfs Germany 18 669 1.5× 79 0.4× 151 0.8× 127 0.9× 148 1.4× 77 1.1k

Countries citing papers authored by Thomas C. Booth

Since Specialization
Citations

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

Fields of papers citing papers by Thomas C. Booth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas C. Booth. 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 Thomas C. Booth. The network helps show where Thomas C. Booth may publish in the future.

Co-authorship network of co-authors of Thomas C. Booth

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas C. Booth. A scholar is included among the top collaborators of Thomas C. Booth 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 Thomas C. Booth. Thomas C. Booth 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.
Maršala, Martin, et al.. (2026). Aquaporin-4: A Predictor and Therapeutic Target for Permanent Paraplegia after Endovascular Thoraco-abdominal Aortic Aneurysm Repair. European Journal of Vascular and Endovascular Surgery.
2.
Robertshaw, Harry, Benjamin M. Jackson, Katharina Breininger, et al.. (2025). Learning-based autonomous navigation, benchmark environments and simulation framework for endovascular interventions. Computers in Biology and Medicine. 196(Pt C). 110844–110844.
3.
Treasure, Peter, Thomas C. Booth, Stephen J. Price, et al.. (2025). Adult glioblastoma in England: Incidence, treatment, and outcomes with novel population-based strata. Cancer Epidemiology. 97. 102811–102811.
4.
Sadati, S. M. Hadi, Panagiotis Vartholomeos, Mohamed E. M. K. Abdelaziz, et al.. (2025). Vine4Spine: A Steerable Tip-Growing Robot with Contact Force Estimation for Navigation in the Spinal Subarachnoid Space. 17279–17286.
5.
Öztürk-Işık, Esin, et al.. (2025). Post-hoc eXplainable AI methods for analyzing medical images of gliomas (— A review for clinical applications). Computers in Biology and Medicine. 196(Pt A). 110649–110649. 1 indexed citations
6.
Wood, David, Matthew Townend, Asif Mazumder, et al.. (2024). Optimising brain age estimation through transfer learning: A suite of pre‐trained foundation models for improved performance and generalisability in a clinical setting. Human Brain Mapping. 45(4). e26625–e26625. 11 indexed citations
7.
Kinnersley, Ben, et al.. (2024). Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies. Neuro-Oncology Advances. 6(1). vdae055–vdae055. 3 indexed citations
8.
Gould, Juli R., et al.. (2024). Managing emerald ash borer in urban forests: Integrating biocontrol and insecticide treatments. Biological Control. 199. 105658–105658.
9.
Tormey, David, et al.. (2024). An eXplainable deep learning model for multi-modal MRI grading of IDH-mutant astrocytomas. Results in Engineering. 24. 103353–103353. 9 indexed citations
10.
Papa, João Paulo, et al.. (2024). SurvNet: A low-complexity convolutional neural network for survival time classification of patients with glioblastoma. Heliyon. 10(12). e32870–e32870. 3 indexed citations
11.
Benger, Matthew, David Wood, Jeremy Lynch, et al.. (2023). Factors affecting the labelling accuracy of brain MRI studies relevant for deep learning abnormality detection. SHILAP Revista de lepidopterología. 3. 1251825–1251825. 3 indexed citations
12.
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14.
Clement, Patricia, Thomas C. Booth, Fran Borovečki, et al.. (2020). GliMR: Cross-Border Collaborations to Promote Advanced MRI Biomarkers for Glioma. Journal of Medical and Biological Engineering. 41(2). 115–125. 11 indexed citations
15.
Kandasamy, Naga, Jonathan Hart, Jeremy Lynch, et al.. (2019). Outcome Study of the Pipeline Embolization Device with Shield Technology in Unruptured Aneurysms (PEDSU). American Journal of Neuroradiology. 40(12). 2094–2101. 27 indexed citations
16.
Gallagher, Ferdia A., Mikko I. Kettunen, Eva Serrão, et al.. (2015). Carbonic Anhydrase Activity Monitored In Vivo by Hyperpolarized 13C-Magnetic Resonance Spectroscopy Demonstrates Its Importance for pH Regulation in Tumors. Cancer Research. 75(19). 4109–4118. 36 indexed citations
17.
Booth, Thomas C., et al.. (2015). The Current Impact of Incidental Findings Found during Neuroimaging on Neurologists’ Workloads. PLoS ONE. 10(2). e0118155–e0118155. 11 indexed citations
18.
Wardlaw, Joanna M., H. Dele Davies, Thomas C. Booth, et al.. (2015). Acting on incidental findings in research imaging. BMJ. 351(nov10 1). h5190–h5190. 25 indexed citations
19.
Booth, Thomas C., Adam Waldman, Sarah Jefferies, & Hans Rolf Jäger. (2014). Comment on “The role of imaging in the management of progressive glioblastoma. A systematic review and evidence-based clinical practice guideline” [J Neurooncol 2014; 118:435–460]. Journal of Neuro-Oncology. 121(2). 423–424. 4 indexed citations
20.
World, Michael & Thomas C. Booth. (2008). Iraq: the environmental challenge to HM Land Forces. Clinical Medicine. 8(4). 399–403. 8 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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