← back to search

Ralph T. H. Leijenaar

Maastricht University · NL
Area of research
Radiology, Nuclear Medicine and Imaging · Biomedical Engineering
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, Advanced X-ray and CT Imaging, Medical Imaging Techniques and Applications, and Lung Cancer Diagnosis and Treatment.
h-index
47
citations
28,127
works
141
NIH funding
primary concept
email

Recent publications

Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression
Journal of Neuro-Oncology 2022cited by 19position: middledoi
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
Radiology 2020cited by 3,780position: middledoi
Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures
Radiotherapy and Oncology 2020cited by 32position: middledoi
Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET/CT radiomics
PLoS ONE 2019cited by 50position: middledoi
Applicability of a prognostic CT-based radiomic signature model trained on stage I-III non-small cell lung cancer in stage IV non-small cell lung cancer
Lung Cancer 2018cited by 53position: middledoi
PO-0981: Results from the Image Biomarker Standardisation Initiative
Radiotherapy and Oncology 2018cited by 40position: middledoi
Defining the biological basis of radiomic phenotypes in lung cancer
eLife 2017cited by 359position: middledoi
PO-0922: Are planning CT radiomics and cone-beam CT radiomics interchangeable?
Radiotherapy and Oncology 2016cited by 4position: middledoi
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
Radiotherapy and Oncology 2015cited by 704position: middledoi
The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis
Scientific Reports 2015cited by 416position: firstdoi
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Nature Communications 2014cited by 5,102position: middledoi
Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation
PLoS ONE 2014cited by 599position: middledoi
Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability
Acta Oncologica 2013cited by 423position: firstdoi
Radiomics: Extracting more information from medical images using advanced feature analysis
European Journal of Cancer 2012cited by 5,864position: middledoi

Grants

No grants ingested yet.

Frequent collaborators

Philippe Lambin · Maastricht University10 papers (2012–2019)Hugo J.W.L. Aerts · Harvard University7 papers (2012–2017)Sara Carvalho · Cancer Research UK Cambridge Center6 papers (2012–2019)Robert J. Gillies · University of Calgary5 papers (2012–2017) · 5 papers (2013–2017)Esther G.C. Troost · Heidelberg University4 papers (2015–2019)Benjamin Haibe‐Kains · Structural Genomics Consortium4 papers (2014–2017)Chintan Parmar · AstraZeneca (United States)4 papers (2013–2017)Wouter van Elmpt · University of Wollongong3 papers (2016–2019)Patrick Großmann · ETH Zurich3 papers (2014–2017)André Dekker · Maastricht University Medical Centre3 papers (2012–2014) · 2 papers (2013–2015)Raymond H. Mak · Brigham and Women's Hospital2 papers (2014–2015) · 2 papers (2016–2019)Ronald Boellaard · Netherlands Institute for Neuroscience2 papers (2013–2015) · 2 papers (2018–2019)Saeed Ashrafinia · Medical Solutions1 papers (2018–2018)Steffen Löck · German Cancer Research Center1 papers (2018–2018)Bart Reymen · Maastro Clinic1 papers (2018–2018)Karl‐Josef Langen · Forschungszentrum Jülich1 papers (2022–2022)