Maarten de Rooij

4.8k total citations · 4 hit papers
41 papers, 3.0k citations indexed

About

Maarten de Rooij is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Surgery. According to data from OpenAlex, Maarten de Rooij has authored 41 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Radiology, Nuclear Medicine and Imaging, 27 papers in Pulmonary and Respiratory Medicine and 6 papers in Surgery. Recurrent topics in Maarten de Rooij's work include Prostate Cancer Diagnosis and Treatment (25 papers), Radiomics and Machine Learning in Medical Imaging (18 papers) and Prostate Cancer Treatment and Research (15 papers). Maarten de Rooij is often cited by papers focused on Prostate Cancer Diagnosis and Treatment (25 papers), Radiomics and Machine Learning in Medical Imaging (18 papers) and Prostate Cancer Treatment and Research (15 papers). Maarten de Rooij collaborates with scholars based in Netherlands, United Kingdom and Italy. Maarten de Rooij's co-authors include Jelle O. Barentsz, Maroeska M. Rovers, E.H.J. Hamoen, J. Alfred Witjes, Jurgen J. Fütterer, Kicky G. van Leeuwen, Steven Schalekamp, Bram van Ginneken, Matthieu Rutten and Morgan Pokorny and has published in prestigious journals such as Radiology, International Journal of Molecular Sciences and Hypertension.

In The Last Decade

Maarten de Rooij

35 papers receiving 2.9k 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.

Accuracy of Magnetic Resonance Imaging for Local Staging of Prostate Cancer: A Diagnostic Meta-analysis

2015 448 citations Maarten de Rooij, E.H.J. Hamoen et al. European Urology profile →
Prospective Study of Diagnostic Accuracy Comparing Prostate Cancer Detection by Transrectal Ultrasound–Guided Biopsy Versus Magnetic Resonance (MR) Imaging with Subsequent MR-guided Biopsy in Men Without Previous Prostate Biopsies

2014 395 citations Morgan Pokorny, Maarten de Rooij et al. European Urology profile →
Accuracy of Multiparametric MRI for Prostate Cancer Detection: A Meta-Analysis

2014 368 citations Maarten de Rooij, E.H.J. Hamoen et al. profile →
Artificial intelligence in radiology: 100 commercially available products and their scientific evidence

2021 260 citations Kicky G. van Leeuwen, Steven Schalekamp et al. European Radiology profile →

Peers

Maarten de Rooij

PRM

RNMI

RHEUM

Arnaldo Stanzione Italy

Andrei S. Purysko United States

Vibeke Løgager Denmark

Matteo Manfredi Italy

Leonardo Kayat Bittencourt United States

Veeru Kasivisvanathan United Kingdom

Urs J. Muehlematter Switzerland

Grace Hyun J. Kim United States

Alex Kirkham United Kingdom

Faina Shtern United States

Arnaldo Stanzione Italy

Maarten de Rooij

914 ×0.4

PRM

1.7k ×1.2

RNMI

172 ×0.2

RHEUM

285 ×0.8

454 ×1.6

Citations per year, relative to Maarten de Rooij Maarten de Rooij (= 1×) peers Arnaldo Stanzione

Countries citing papers authored by Maarten de Rooij

Since Specialization

Citations

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

Fields of papers citing papers by Maarten de Rooij

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maarten de Rooij. 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 Maarten de Rooij. The network helps show where Maarten de Rooij may publish in the future.

Co-authorship network of co-authors of Maarten de Rooij

This figure shows the co-authorship network connecting the top 25 collaborators of Maarten de Rooij. A scholar is included among the top collaborators of Maarten de Rooij 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 Maarten de Rooij. Maarten de Rooij 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

Rooij, Maarten de, et al.. (2025). External validation of automated prostate MR T2-weighted image quality assessment on multi-centre multi-vendor data. Radboud Repository (Radboud University). 1. 100002–100002. 2 indexed citations

Panebianco, Valeria, Alberto Briganti, Jason A. Efstathiou, et al.. (2025). Multiparametric Magnetic Resonance Imaging and Vesical Imaging-Reporting and Data System (VI-RADS) for Bladder Cancer Diagnosis and Staging: A Guide for Clinicians from the American College of Radiology VI-RADS Steering Committee. European Urology.

Twilt, Jasper J., Tristan Barrett, Francesco Giganti, et al.. (2025). Quality of prostate MRI in early diagnosis—a national survey and reading evaluation. Insights into Imaging. 16(1). 82–82.

Jacobs, Colin, et al.. (2025). Artificial intelligence in radiology: 173 commercially available products and their scientific evidence. European Radiology. 36(1). 526–536. 7 indexed citations

Privé, Bastiaan M., Tim M. Govers, Bas Israël, et al.. (2025). A cost-effectiveness study of PSMA-PET/CT for the detection of clinically significant prostate cancer. European Journal of Nuclear Medicine and Molecular Imaging. 52(9). 3159–3169. 1 indexed citations

Oort, Inge M. van, et al.. (2024). Prostate MRI and artificial intelligence during active surveillance: should we jump on the bandwagon?. European Radiology. 34(12). 7698–7704. 5 indexed citations

Barrett, Tristan, et al.. (2023). Update on Optimization of Prostate MR Imaging Technique and Image Quality. Radiologic Clinics of North America. 62(1). 1–15. 9 indexed citations

Leeuwen, Kicky G. van, D. Grob, Frank de Lange, et al.. (2023). AI-support for the detection of intracranial large vessel occlusions: One-year prospective evaluation. Heliyon. 9(8). e19065–e19065. 3 indexed citations

Saha, Anindo, et al.. (2023). Semisupervised Learning with Report-guided Pseudo Labels for Deep Learning–based Prostate Cancer Detection Using Biparametric MRI. Radiology Artificial Intelligence. 5(5). e230031–e230031. 31 indexed citations

10.

Jacobs, Colin, et al.. (2023). Prediction Variability to Identify Reduced AI Performance in Cancer Diagnosis at MRI and CT. Radiology. 308(3). e230275–e230275. 8 indexed citations

11.

Leeuwen, Kicky G. van, Maarten de Rooij, Steven Schalekamp, Bram van Ginneken, & Matthieu Rutten. (2023). Clinical use of artificial intelligence products for radiology in the Netherlands between 2020 and 2022. European Radiology. 34(1). 348–354. 24 indexed citations

12.

Rooij, Maarten de, et al.. (2023). Radiomics based automated quality assessment for T2W prostate MR images. European Journal of Radiology. 165. 110928–110928. 7 indexed citations

13.

Barrett, Tristan, Maarten de Rooij, Francesco Giganti, et al.. (2022). Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway. Nature Reviews Urology. 20(1). 9–22. 43 indexed citations

14.

Leeuwen, Kicky G. van, Steven Schalekamp, Matthieu Rutten, Bram van Ginneken, & Maarten de Rooij. (2021). Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. European Radiology. 31(6). 3797–3804. 260 indexed citations breakdown →

15.

Rooij, Maarten de, Hendrik Van Poppel, & Jelle O. Barentsz. (2021). Risk Stratification and Artificial Intelligence in Early Magnetic Resonance Imaging–based Detection of Prostate Cancer. European Urology Focus. 8(5). 1187–1191. 10 indexed citations

16.

Hosseinzadeh, Matin, et al.. (2021). Deep learning–assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge. European Radiology. 32(4). 2224–2234. 84 indexed citations

17.

Scholten, Ernst T., Stefan Bruijnen, Mathijn de Jong, et al.. (2021). Development and Validation of a Convolutional Neural Network for Automated Detection of Scaphoid Fractures on Conventional Radiographs. Radiology Artificial Intelligence. 3(4). e200260–e200260. 32 indexed citations

18.

Pokorny, Morgan, Maarten de Rooij, Earl Duncan, et al.. (2014). Prospective Study of Diagnostic Accuracy Comparing Prostate Cancer Detection by Transrectal Ultrasound–Guided Biopsy Versus Magnetic Resonance (MR) Imaging with Subsequent MR-guided Biopsy in Men Without Previous Prostate Biopsies. European Urology. 66(1). 22–29. 395 indexed citations breakdown →

19.

Hamoen, E.H.J., Maarten de Rooij, J. Alfred Witjes, Jelle O. Barentsz, & Maroeska M. Rovers. (2014). Measuring health-related quality of life in men with prostate cancer: A systematic review of the most used questionnaires and their validity. Urologic Oncology Seminars and Original Investigations. 33(2). 69.e19–69.e28. 63 indexed citations

20.

Jansen, Martin, et al.. (2011). De herziene KNGF-richtlijn artrose heup-knie.. Data Archiving and Networked Services (DANS). 133(2). 275–80.

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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