Hugo J.W.L. Aerts

60.2k total citations · 24 hit papers
268 papers, 33.1k citations indexed

About

Hugo J.W.L. Aerts is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Hugo J.W.L. Aerts has authored 268 papers receiving a total of 33.1k indexed citations (citations by other indexed papers that have themselves been cited), including 208 papers in Radiology, Nuclear Medicine and Imaging, 115 papers in Pulmonary and Respiratory Medicine and 47 papers in Biomedical Engineering. Recurrent topics in Hugo J.W.L. Aerts's work include Radiomics and Machine Learning in Medical Imaging (166 papers), Lung Cancer Diagnosis and Treatment (78 papers) and Medical Imaging Techniques and Applications (50 papers). Hugo J.W.L. Aerts is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (166 papers), Lung Cancer Diagnosis and Treatment (78 papers) and Medical Imaging Techniques and Applications (50 papers). Hugo J.W.L. Aerts collaborates with scholars based in United States, Netherlands and Canada. Hugo J.W.L. Aerts's co-authors include Chintan Parmar, Philippe Lambin, Ahmed Hosny, Robert J. Gillies, André Dekker, Ralph T. H. Leijenaar, John Quackenbush, Sara Carvalho, Patrick Großmann and Raymond H. Mak and has published in prestigious journals such as Nature, Science and Nature Communications.

In The Last Decade

Hugo J.W.L. Aerts

240 papers receiving 32.7k citations

Hit Papers

Computational Radiomics System to Decode the Radiographi... 2012 2026 2016 2021 2017 2012 2014 2018 2012 1000 2.0k 3.0k 4.0k
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.

  1. Computational Radiomics System to Decode the Radiographic Phenotype
    2017 4.2k citations Andriy Fedorov, Chintan Parmar et al. Cancer Research profile →
  2. Radiomics: Extracting more information from medical images using advanced feature analysis
    2012 4.1k citations Philippe Lambin, Robert J. Gillies et al. European Journal of Cancer profile →
  3. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
    2014 3.6k citations Hugo J.W.L. Aerts, Emmanuel Rios Velazquez et al. profile →
  4. Artificial intelligence in radiology
    2018 2.3k citations Ahmed Hosny, Chintan Parmar et al. Nature reviews. Cancer profile →
  5. Radiomics: the process and the challenges
    2012 1.7k citations Steven A. Eschrich, Matthew B. Schabath et al. profile →
  6. Artificial intelligence in cancer imaging: Clinical challenges and applications
    2019 1.2k citations Ahmed Hosny, Matthew B. Schabath et al. profile →
  7. Applications and limitations of radiomics
    2016 856 citations Hugo J.W.L. Aerts et al. profile →
  8. Machine Learning methods for Quantitative Radiomic Biomarkers
    2015 746 citations Chintan Parmar, Philippe Lambin et al. profile →
  9. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
    2015 547 citations Thibaud Coroller, Emmanuel Rios Velazquez et al. Radiotherapy and Oncology profile →
  10. The Potential of Radiomic-Based Phenotyping in Precision Medicine
    2016 482 citations Hugo J.W.L. Aerts profile →
  11. Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation
    2014 468 citations Chintan Parmar, Emmanuel Rios Velazquez et al. profile →
  12. Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging
    2019 402 citations Yiwen Xu, Ahmed Hosny et al. Clinical Cancer Research profile →
  13. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
    2018 398 citations Ahmed Hosny, Chintan Parmar et al. profile →
  14. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers
    2019 372 citations Nicolai J. Birkbak, Chintan Parmar et al. profile →
  15. Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer
    2015 358 citations Chintan Parmar, Emmanuel Rios Velazquez et al. profile →
  16. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
    2017 355 citations Hugo J.W.L. Aerts et al. profile →
  17. The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis
    2015 327 citations Hugo J.W.L. Aerts, Robert J. Gillies et al. profile →
  18. Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer
    2017 304 citations Emmanuel Rios Velazquez, Chintan Parmar et al. Cancer Research profile →
  19. Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology
    2016 299 citations Chintan Parmar, John Quackenbush et al. profile →
  20. Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
    2015 297 citations Chintan Parmar, Philippe Lambin et al. profile →
  21. Deep learning classification of lung cancer histology using CT images
    2021 163 citations Tafadzwa L. Chaunzwa, Ahmed Hosny et al. profile →
  22. Large language models to identify social determinants of health in electronic health records
    2024 119 citations Tafadzwa L. Chaunzwa, Idalid Franco et al. profile →
  23. Foundation model for cancer imaging biomarkers
    2024 63 citations Suraj Pai, Dennis Bontempi et al. Nature Machine Intelligence profile →
  24. Skeletal muscle adiposity, coronary microvascular dysfunction, and adverse cardiovascular outcomes
    2025 14 citations Borek Foldyna, Hugo J.W.L. Aerts et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hugo J.W.L. Aerts United States 68 27.2k 11.4k 7.6k 5.8k 5.3k 268 33.1k
André Dekker Netherlands 54 18.8k 0.7× 8.6k 0.8× 5.5k 0.7× 3.4k 0.6× 3.8k 0.7× 329 23.4k
Philippe Lambin Netherlands 96 29.7k 1.1× 17.4k 1.5× 8.4k 1.1× 4.6k 0.8× 9.0k 1.7× 720 46.6k
Chintan Parmar United States 26 13.5k 0.5× 5.4k 0.5× 3.9k 0.5× 3.0k 0.5× 2.4k 0.5× 36 15.8k
Ralph T. H. Leijenaar Netherlands 38 15.2k 0.6× 6.1k 0.5× 4.3k 0.6× 2.6k 0.5× 3.0k 0.6× 84 16.8k
Anant Madabhushi United States 75 12.0k 0.4× 4.1k 0.4× 2.7k 0.4× 11.2k 1.9× 3.0k 0.6× 544 23.3k
Lawrence H. Schwartz United States 80 16.5k 0.6× 20.6k 1.8× 3.0k 0.4× 2.1k 0.4× 21.2k 4.0× 395 52.0k
Hedvig Hricak United States 105 20.2k 0.7× 17.4k 1.5× 3.5k 0.5× 1.3k 0.2× 2.8k 0.5× 629 39.8k
Issam El Naqa United States 59 8.3k 0.3× 6.0k 0.5× 2.4k 0.3× 1.8k 0.3× 1.5k 0.3× 349 14.9k
Lei Xing United States 76 12.9k 0.5× 6.8k 0.6× 7.0k 0.9× 1.6k 0.3× 1.1k 0.2× 881 23.4k
Maryellen L. Giger United States 73 12.5k 0.5× 5.2k 0.5× 2.3k 0.3× 9.3k 1.6× 1.8k 0.3× 477 18.6k

Countries citing papers authored by Hugo J.W.L. Aerts

Since Specialization
Citations

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

Fields of papers citing papers by Hugo J.W.L. Aerts

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hugo J.W.L. Aerts. 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 Hugo J.W.L. Aerts. The network helps show where Hugo J.W.L. Aerts may publish in the future.

Co-authorship network of co-authors of Hugo J.W.L. Aerts

This figure shows the co-authorship network connecting the top 25 collaborators of Hugo J.W.L. Aerts. A scholar is included among the top collaborators of Hugo J.W.L. Aerts 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 Hugo J.W.L. Aerts. Hugo J.W.L. Aerts 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.
Zeleznik, Roman, David Maintz, Thomas Mayrhofer, et al.. (2025). Association of Epicardial Adipose Tissue Changes on Serial Chest CT Scans with Mortality: Insights from the National Lung Screening Trial. Radiology. 314(2). e240473–e240473. 3 indexed citations
2.
Ye, Zezhong, Sanjay P. Prabhu, Michael C. Tjong, et al.. (2024). Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario. Radiology Artificial Intelligence. 6(4). e230254–e230254. 7 indexed citations
3.
Ye, Zezhong, Sridhar Vajapeyam, Ariana Familiar, et al.. (2024). LGG-15. DEEP LEARNING ENABLES LONGITUDINAL RISK PREDICTION FOR PEDIATRIC LOW-GRADE GLIOMAS AFTER SURGERY. Neuro-Oncology. 26(Supplement_4). 0–0. 1 indexed citations
4.
Pai, Suraj, Dennis Bontempi, Mateo Sokač, et al.. (2024). Foundation model for cancer imaging biomarkers. Nature Machine Intelligence. 6(3). 354–367. 63 indexed citations breakdown →
5.
Zhang, Zhen, Anne‐Marie C. Dingemans, Joachim G.J.V. Aerts, et al.. (2023). Computed tomography-based radiomics for the differential diagnosis of pneumonitis in stage IV non-small cell lung cancer patients treated with immune checkpoint inhibitors. European Journal of Cancer. 183. 142–151. 16 indexed citations
6.
Busch, Felix, Lina Xu, Daniel Truhn, et al.. (2023). Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs. Computer Methods and Programs in Biomedicine. 234. 107505–107505. 2 indexed citations
7.
Ye, Zezhong, et al.. (2023). SegmentationReview: A Slicer3D extension for fast review of AI-generated segmentations. Software Impacts. 17. 100536–100536. 1 indexed citations
8.
Chen, Shiping, Marco Guevara, Nicolás David Ramírez, et al.. (2023). Deep Learning-Based Natural Language Processing to Automate Esophagitis Severity Grading from the Electronic Health Records. International Journal of Radiation Oncology*Biology*Physics. 117(2). S18–S18. 1 indexed citations
9.
Welch, Mattea, Chris McIntosh, Shao Hui Huang, et al.. (2023). Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics. Cancer Research Communications. 3(6). 1140–1151. 18 indexed citations
10.
Simon, George R., Chiharu Sako, Ryan Beasley, et al.. (2023). AI-based radiomic biomarkers to predict PD-(L)1 immune checkpoint inhibitor response within PD-L1 high/low/negative expression categories in stage IV NSCLC.. Journal of Clinical Oncology. 41(16_suppl). 1517–1517. 1 indexed citations
11.
Brekel, Michiel W. M. van den, Abrahim Al‐Mamgani, Hugo J.W.L. Aerts, et al.. (2021). Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models. European Journal of Radiology. 139. 109701–109701. 23 indexed citations
12.
Tunali, Ilke, Yan Tan, Jhanelle E. Gray, et al.. (2021). Hypoxia-Related Radiomics and Immunotherapy Response: A Multicohort Study of Non-Small Cell Lung Cancer. JNCI Cancer Spectrum. 5(4). 31 indexed citations
13.
Kann, Benjamin H., Ahmed Hosny, & Hugo J.W.L. Aerts. (2021). Artificial intelligence for clinical oncology. Cancer Cell. 39(7). 916–927. 204 indexed citations
14.
Fedorov, Andriy, David Clunie, Mathias Brochhausen, et al.. (2020). DICOM re‐encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules. Medical Physics. 47(11). 5953–5965. 11 indexed citations
15.
Xu, Yiwen, Ahmed Hosny, Roman Zeleznik, et al.. (2019). Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging. Clinical Cancer Research. 25(11). 3266–3275. 402 indexed citations breakdown →
16.
Hosny, Ahmed, Chintan Parmar, John Quackenbush, Lawrence H. Schwartz, & Hugo J.W.L. Aerts. (2018). Artificial intelligence in radiology. Nature reviews. Cancer. 18(8). 500–510. 2301 indexed citations breakdown →
17.
Velazquez, Emmanuel Rios, Chintan Parmar, Ying Liu, et al.. (2017). Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer. Cancer Research. 77(14). 3922–3930. 304 indexed citations breakdown →
18.
Hatzis, Christos, Philippe L. Bédard, Nicolai J. Birkbak, et al.. (2014). Enhancing Reproducibility in Cancer Drug Screening: How Do We Move Forward?. Cancer Research. 74(15). 4016–4023. 67 indexed citations
19.
Aerts, Hugo J.W.L., et al.. (2007). Towards a better prediction of radiation-induced lung damage (RILD). Radiotherapy and Oncology. 84.
20.
Aerts, Hugo J.W.L., Guy Bosmans, Angela van Baardwijk, et al.. (2006). Time trends in the heterogeneity of FDG uptake, based on SUV contours, during a course of radical radiotherapy in NSCLC. Radiotherapy and Oncology. 81.

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