Vicky Goh

21.0k total citations · 4 hit papers
244 papers, 10.7k citations indexed

About

Vicky Goh is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Surgery. According to data from OpenAlex, Vicky Goh has authored 244 papers receiving a total of 10.7k indexed citations (citations by other indexed papers that have themselves been cited), including 168 papers in Radiology, Nuclear Medicine and Imaging, 65 papers in Pulmonary and Respiratory Medicine and 59 papers in Surgery. Recurrent topics in Vicky Goh's work include Radiomics and Machine Learning in Medical Imaging (104 papers), Medical Imaging Techniques and Applications (71 papers) and MRI in cancer diagnosis (68 papers). Vicky Goh is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (104 papers), Medical Imaging Techniques and Applications (71 papers) and MRI in cancer diagnosis (68 papers). Vicky Goh collaborates with scholars based in United Kingdom, Singapore and United States. Vicky Goh's co-authors include Gary Cook, Balaji Ganeshan, Kenneth A. Miles, Steve Halligan, Connie Yip, Clive I. Bartram, Robert Kozarski, Musib Siddique, Anwar R. Padhani and Rob Glynne‐Jones and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Vicky Goh

237 papers receiving 10.6k citations

Hit Papers

Assessment of tumor heter... 2012 2026 2016 2021 2012 2014 2012 2012 200 400 600

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Vicky Goh 7.0k 3.5k 2.5k 2.3k 1.7k 244 10.7k
Regina G. H. Beets‐Tan 7.3k 1.0× 3.1k 0.9× 4.5k 1.8× 3.2k 1.4× 1.7k 1.0× 246 11.8k
Frank Hoebers 4.8k 0.7× 2.7k 0.8× 1.5k 0.6× 1.2k 0.5× 1.2k 0.7× 147 7.5k
Masoom A. Haider 7.4k 1.1× 9.9k 2.9× 1.1k 0.4× 1.9k 0.8× 1.8k 1.1× 258 14.9k
Dow‐Mu Koh 9.4k 1.4× 2.4k 0.7× 1.9k 0.8× 2.0k 0.9× 616 0.4× 268 13.0k
Jeffrey D. Bradley 6.0k 0.9× 8.3k 2.4× 2.1k 0.8× 1.2k 0.5× 997 0.6× 338 12.1k
Anwar R. Padhani 13.5k 1.9× 8.3k 2.4× 2.7k 1.1× 1.8k 0.8× 1.6k 0.9× 287 21.1k
Mark Rosen 3.9k 0.6× 1.9k 0.6× 1.3k 0.5× 846 0.4× 1.2k 0.7× 178 7.8k
Massimo Bellomi 3.3k 0.5× 3.2k 0.9× 1.3k 0.5× 1.0k 0.4× 1.2k 0.7× 229 7.5k
Wouter van Elmpt 8.4k 1.2× 4.2k 1.2× 1.3k 0.5× 801 0.3× 2.5k 1.5× 193 10.2k
Dirk De Ruysscher 4.6k 0.7× 6.2k 1.8× 2.7k 1.1× 663 0.3× 981 0.6× 227 10.0k

Countries citing papers authored by Vicky Goh

Since Specialization
Citations

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

Fields of papers citing papers by Vicky Goh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vicky Goh

This figure shows the co-authorship network connecting the top 25 collaborators of Vicky Goh. A scholar is included among the top collaborators of Vicky Goh 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 Vicky Goh. Vicky Goh 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.
Goh, Vicky, Susan Mallett, Manuel Rodriguez‐Justo, et al.. (2025). Evaluation of prognostic models to improve prediction of metastasis in patients following potentially curative treatment for primary colorectal cancer: the PROSPECT trial. Health Technology Assessment. 29(8). 1–91.
2.
Nougaret, Stéphanie, Doenja M. J. Lambregts, Caroline Reinhold, et al.. (2025). MRI of the Rectum: A Decade into DISTANCE, Moving to DISTANCED. Radiology. 314(1). e232838–e232838. 4 indexed citations
5.
Cleary, Jon O., et al.. (2024). 3D distortion‐free, reduced FOV diffusion‐prepared gradient echo at 3 T. Magnetic Resonance in Medicine. 93(4). 1471–1483. 1 indexed citations
6.
Touska, Philip, Irumee Pai, Francesco Padormo, et al.. (2024). Magnetic resonance imaging evaluation of cochlear and vestibular nerve calibre: a case-control study in Ménière’s disease and endolymphatic hydrops. European Archives of Oto-Rhino-Laryngology. 282(1). 91–101.
7.
Cid, Yashin Dicente, Ian Selby, Keerthini Muthuswamy, et al.. (2023). Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study. The Lancet Digital Health. 6(1). e44–e57. 11 indexed citations
8.
Withey, Samuel J., Kasia Owczarczyk, Mariusz Grzeda, et al.. (2023). Association of dynamic contrast-enhanced MRI and 18F-Fluorodeoxyglucose PET/CT parameters with neoadjuvant therapy response and survival in esophagogastric cancer. European Journal of Surgical Oncology. 49(10). 106934–106934. 1 indexed citations
9.
Jenkins, Paul, et al.. (2023). Research should remain a priority in 21st century radiology recruitment to training. British Journal of Radiology. 96(1145). 20221083–20221083. 1 indexed citations
10.
Patel, Ashay, Petru-Daniel Tudosiu, Walter Hugo Lopez Pinaya, et al.. (2023). Geometry-Invariant Abnormality Detection. Lecture notes in computer science. 300–309. 1 indexed citations
11.
Kläser, Kerstin, Marc Modat, David Atkinson, et al.. (2021). A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis. Applied Sciences. 11(4). 1667–1667. 10 indexed citations
12.
Withey, Samuel J., et al.. (2019). Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks. Radiology. 291(1). 196–202. 194 indexed citations
13.
Owczarczyk, Kasia, Davide Prezzi, Matthew D. Cascino, et al.. (2019). MRI heterogeneity analysis for prediction of recurrence and disease free survival in anal cancer. Radiotherapy and Oncology. 134. 119–126. 10 indexed citations
14.
Pesce, Emanuele, et al.. (2019). Learning to detect chest radiographs containing pulmonary lesions using visual attention networks. Medical Image Analysis. 53. 26–38. 86 indexed citations
16.
Withey, Samuel J., Joanna Gariani, Davide Prezzi, et al.. (2018). Is there a role for perfusion imaging in assessing treatment response following ablative therapy of small renal masses—A systematic review. European Journal of Radiology Open. 5. 102–107.
17.
Cook, Gary, Mary O’Brien, Sugama Chicklore, et al.. (2015). Heterogeneity of 18F-FDG PET uptake: Association with treatment response and prognosis in non-small cell lung cancer treated with erlotinib.. Radiology. 276(3). 5 indexed citations
18.
Dart, Robin, Nyree Griffin, Vicky Goh, et al.. (2013). PTH-092 Interval Scanning with Magnetic Resonance Enterography Demonstrates Response to Anti-Tnf Therapy and has Utility in Reassessment of Crohn’S Disease. Gut. 62(Suppl 1). A248.2–A249. 1 indexed citations
19.
Sinha, Ashish, Anika Hansmann, Sunil Bhandari, et al.. (2012). Imaging assessment of desmoid tumours in familial adenomatous polyposis: is state-of-the-art 1.5 T MRI better than 64-MDCT?. British Journal of Radiology. 85(1015). e254–e261. 20 indexed citations
20.
Sinha, Ashish, Emily Tam, James Stirling, et al.. (2011). Diffusion tensor imaging (DTI) of desmoid tumours in familial adenomatous polyposis: Initial experience. European Journal of Radiology. 81(11). 3646–3651. 3 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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