Geoffrey A. Sonn

6.0k total citations · 2 hit papers
108 papers, 3.7k citations indexed

About

Geoffrey A. Sonn is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. According to data from OpenAlex, Geoffrey A. Sonn has authored 108 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 83 papers in Pulmonary and Respiratory Medicine, 45 papers in Radiology, Nuclear Medicine and Imaging and 16 papers in Biomedical Engineering. Recurrent topics in Geoffrey A. Sonn's work include Prostate Cancer Diagnosis and Treatment (67 papers), Prostate Cancer Treatment and Research (34 papers) and Radiomics and Machine Learning in Medical Imaging (32 papers). Geoffrey A. Sonn is often cited by papers focused on Prostate Cancer Diagnosis and Treatment (67 papers), Prostate Cancer Treatment and Research (34 papers) and Radiomics and Machine Learning in Medical Imaging (32 papers). Geoffrey A. Sonn collaborates with scholars based in United States, United Kingdom and Denmark. Geoffrey A. Sonn's co-authors include Daniel Margolis, Leonard S. Marks, Richard E. Fan, Mark S. Litwin, Shyam Natarajan, Jiaoti Huang, Patricia Lieu, Frederick J. Dorey, James D. Brooks and Malu Macairan and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Oncology and Cancer.

In The Last Decade

Geoffrey A. Sonn

100 papers receiving 3.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Value of Targeted Prostate Biopsy Using Magnetic Resonance–Ultrasound Fusion in Men with Prior Negative Biopsy and Elevated Prostate-specific Antigen

2013 297 citations Geoffrey A. Sonn, Shyam Natarajan et al. profile →
Prostate Magnetic Resonance Imaging and Magnetic Resonance Imaging Targeted Biopsy in Patients with a Prior Negative Biopsy: A Consensus Statement by AUA and SAR

2016 273 citations Daniel Margolis, Leonard S. Marks et al. The Journal of Urology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Geoffrey A. Sonn United States	33	2.3k	1.0k	709	551	515	108	3.7k
J.P. Michiel Sedelaar Netherlands	32	2.6k 1.2×	992 1.0×	774 1.1×	306 0.6×	291 0.6×	110	3.3k
Georg Salomon Germany	37	4.0k 1.7×	1.6k 1.5×	1.1k 1.6×	1.1k 2.1×	470 0.9×	181	5.5k
Vincent J. Gnanapragasam United Kingdom	37	2.4k 1.0×	843 0.8×	531 0.7×	429 0.8×	956 1.9×	143	3.6k
Lee Ponsky United States	34	2.3k 1.0×	1.1k 1.0×	305 0.4×	1.4k 2.5×	565 1.1×	194	3.9k
Maurice Stephan Michel Germany	42	3.6k 1.6×	661 0.6×	547 0.8×	1.2k 2.2×	972 1.9×	285	6.2k
James Thompson Australia	29	1.8k 0.8×	644 0.6×	552 0.8×	523 0.9×	393 0.8×	77	2.9k
Ottavio De Cobelli Italy	39	2.8k 1.2×	623 0.6×	735 1.0×	1.5k 2.8×	737 1.4×	328	4.8k
Chris H. Bangma Netherlands	34	2.7k 1.2×	584 0.6×	1.0k 1.4×	811 1.5×	1.3k 2.5×	99	4.4k
Amit R. Patel United States	46	2.7k 1.2×	2.1k 2.0×	354 0.5×	1.6k 2.9×	752 1.5×	308	7.3k
Scott Williams Australia	41	4.7k 2.1×	2.3k 2.2×	522 0.7×	718 1.3×	575 1.1×	164	6.5k

Countries citing papers authored by Geoffrey A. Sonn

Since Specialization

Citations

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

Fields of papers citing papers by Geoffrey A. Sonn

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Geoffrey A. Sonn. 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 Geoffrey A. Sonn. The network helps show where Geoffrey A. Sonn may publish in the future.

Co-authorship network of co-authors of Geoffrey A. Sonn

This figure shows the co-authorship network connecting the top 25 collaborators of Geoffrey A. Sonn. A scholar is included among the top collaborators of Geoffrey A. Sonn 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 Geoffrey A. Sonn. Geoffrey A. Sonn 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

Zhou, Steve, Li‐Chun Zhang, Moon Hyung Choi, et al.. (2025). ProMUS ‐ NET : Artificial intelligence detects more prostate cancer than urologists on micro‐ultrasonography. British Journal of Urology. 136(6). 1071–1079.

Li, Cynthia, Indrani Bhattacharya, Sulaiman Vesal, et al.. (2025). ProstAtlasDiff: Prostate cancer detection on MRI using Diffusion Probabilistic Models guided by population spatial cancer atlases. Medical Image Analysis. 101. 103486–103486. 1 indexed citations

Rusu, Mirabela, Sulaiman Vesal, Cynthia Li, et al.. (2025). ProCUSNet: Prostate Cancer Detection on B-mode Transrectal Ultrasound Using Artificial Intelligence for Targeting During Prostate Biopsies. European Urology Oncology. 8(2). 477–485.

Zhang, Lichun, Steve Zhou, Moon Hyung Choi, et al.. (2024). Deep learning for prostate and central gland segmentation on micro-ultrasound images. 30. 5–5. 2 indexed citations

Soerensen, Simon John Christoph, Hriday P. Bhambhvani, Richard E. Fan, et al.. (2024). External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens. British Journal of Urology. 135(1). 133–139. 5 indexed citations

Vesal, Sulaiman, Indrani Bhattacharya, Xinran Li, et al.. (2024). A deep learning framework to assess the feasibility of localizing prostate cancer on b-mode transrectal ultrasound images. 26–26. 1 indexed citations

Shao, Wei, Sulaiman Vesal, Simon John Christoph Soerensen, et al.. (2024). RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate. Computers in Biology and Medicine. 173. 108318–108318. 8 indexed citations

Lim, D. Scott, Christian A. Kunder, Wei Shao, et al.. (2023). MP55-12 IMPROVING AUTOMATIC DETECTION OF PROSTATE CANCER ON MRI WITH CLINICAL HISTORY. The Journal of Urology. 209(Supplement 4). 1 indexed citations

Priester, Alan, Richard E. Fan, Mirabela Rusu, et al.. (2023). Prediction and Mapping of Intraprostatic Tumor Extent with Artificial Intelligence. European Urology Open Science. 54. 20–27. 8 indexed citations

10.

Fu, Yunguan, Vasilis Stavrinides, Zachary M. C. Baum, et al.. (2022). Image quality assessment for machine learning tasks using meta-reinforcement learning. Medical Image Analysis. 78. 102427–102427. 28 indexed citations

11.

Fang, Andrew M., Kimberly D. Martin, Richard E. Fan, et al.. (2022). Multi‐institutional analysis of clinical and imaging risk factors for detecting clinically significant prostate cancer in men with PI‐RADS 3 lesions. Cancer. 128(18). 3287–3296. 21 indexed citations

12.

Vesal, Sulaiman, Indrani Bhattacharya, Shyam Natarajan, et al.. (2022). Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study. Medical Image Analysis. 82. 102620–102620. 22 indexed citations

13.

Steinberg, Idan, Jeesu Kim, Dongwoon Hyun, et al.. (2021). Superiorized Photo-Acoustic Non-NEgative Reconstruction (SPANNER) for Clinical Photoacoustic Imaging. IEEE Transactions on Medical Imaging. 40(7). 1888–1897. 29 indexed citations

14.

Bhattacharya, Indrani, Leo C. Chen, Christian A. Kunder, et al.. (2021). Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging. Medical Physics. 48(6). 2960–2972. 41 indexed citations

15.

Awamlh, Bashir Al Hussein Al, Leonard S. Marks, Geoffrey A. Sonn, et al.. (2020). Multicenter analysis of clinical and MRI characteristics associated with detecting clinically significant prostate cancer in PI-RADS (v2.0) category 3 lesions. Urologic Oncology Seminars and Original Investigations. 38(7). 637.e9–637.e15. 18 indexed citations

16.

Shao, Wei, Christian A. Kunder, Richard E. Fan, et al.. (2020). ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate. Medical Image Analysis. 68. 101919–101919. 61 indexed citations

17.

Onofrey, John A., Dana I. Casetti‐Dinescu, Andreas D. Lauritzen, et al.. (2019). Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image Normalization. PubMed. 2019. 348–351. 38 indexed citations

18.

Thomas, I‐Chun, Harsha R. Mittakanti, Jeremy B. Shelton, et al.. (2018). The Research Implications of Prostate Specific Antigen Registry Errors: Data from the Veterans Health Administration. The Journal of Urology. 200(3). 541–548. 8 indexed citations

19.

Sonn, Geoffrey A., Ziyue Karen Jiang, Kirstin A. Zettlitz, et al.. (2015). Fluorescent Image–Guided Surgery with an Anti-Prostate Stem Cell Antigen (PSCA) Diabody Enables Targeted Resection of Mouse Prostate Cancer Xenografts in Real Time. Clinical Cancer Research. 22(6). 1403–1412. 41 indexed citations

20.

Krupski, Tracey L., Geoffrey A. Sonn, Lorna Kwan, et al.. (2005). Ethnic variation in health-related quality of life among low-income men with prostate cancer.. PubMed. 15(3). 461–8. 48 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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