Radiomics: extracting more information from medical images using advanced feature analysis
Open accessAbstract
Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.
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Radiomics can be used to identify lesions, allowing for early detection and diagnosis of disease; radiological features can predict prognoses such as survival, tumor metastasis, and treatment response, and correlate with genomic, transcriptomic, or proteomic features.
“Radiomics can be used to identify lesions, allowing for early detection and diagnosis of disease. Also, radiological features can predict prognoses, such as survival, tumor metastasis, and treatment response, and correlate with genomic, transcriptomic, or proteomic features 88-91.”
From Artificial intelligence in pancreatic cancer (2022) · cites this paper as [88]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredRadiomics converts medical images into quantitative features that can be utilized for clinical decision support.
“Radiomics converts medical images into quantitative features that can be utilized for clinical decision support [72,73].”
From Transparency of deep neural networks for medical image analysis: A review of interpretability methods (2022) · cites this paper as [73]
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- Authors
- Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., & Aerts, H. J. W. L.
- Affiliations
- Maastricht University Medical Centre (NL)
- Moffitt Cancer Center (US)
- Harvard University (US)
- Corresponding author
- Lambin, Philippe
- ORCID
- Venue
- European Journal of Cancer
- Publisher
- Elsevier BV
- Published
- 2012-03
- Volume
- 48
- Issue
- 4
- Pages
- 441-446
- ISSN
- 0959-8049 (ISSN-L)
- PMC
- PMC4533986
- OpenAlex
- W2128739912
- Cited by
- 6,084
- References
- 21
- Citations by year
- 2026: 643 · 2025: 765 · 2024: 805 · 2023: 764 · 2022: 750 · 2021: 667 · 2020: 542 · 2019: 373 · 2018: 317 · 2017: 183 · 2016: 132 · 2015: 67 · 2014: 34 · 2013: 23 · 2012: 9
- FWCI
- 32.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- European Journal of Cancer (publishedVersion)
- PubMed (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Amgen
- Eli Lilly and Company
- Pfizer
- AstraZeneca
- GlaxoSmithKline
- Sanofi
- European Federation of Pharmaceutical Industries and Associations
- Cancer Research UK (115151)
- Imperial College London
- King's College London
- Radboud Universiteit
- KWF Kankerbestrijding (UM 2009-4454, 2011-5020, KWF UM 2009-4454, KWF UM 2011-5020, 2009-4454)
- European Organisation for Research and Treatment of Cancer
- Center for Translational Molecular Medicine
- Radboud Universitair Medisch Centrum
- Universitair Ziekenhuis Antwerpen
- Pfizer UK
- Erasmus Universitair Medisch Centrum Rotterdam
- National Cancer Institute (U01CA143062)
- Interreg
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- radiomics, image feature extraction, intra-tumour heterogeneity, solid tumors, quantitative imaging, automated image analysis, multicenter validation
- MeSH terms
- Algorithms, Diagnostic Imaging, Humans, Image Processing, Computer-Assisted, Models, Biological, Pattern Recognition, Automated, Radioactive Tracers, Radiometry, Genomics, Proteomics, High-Throughput Screening Assays
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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