Abstract

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.

Keywords

RadiomicsFeature (linguistics)Artificial intelligenceComputer sciencePattern recognition (psychology)Medicine

MeSH Terms

AlgorithmsDiagnostic ImagingGenomicsHigh-Throughput Screening AssaysHumansImage ProcessingComputer-AssistedModelsBiologicalPattern RecognitionAutomatedProteomicsRadioactive TracersRadiometry

Affiliated Institutions

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Publication Info

Year
2012
Type
review
Volume
48
Issue
4
Pages
441-446
Citations
5529
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

5529
OpenAlex
86
Influential
4739
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Cite This

Philippe Lambin, Emmanuel Rios-Velazquez, Ralph T. H. Leijenaar et al. (2012). Radiomics: Extracting more information from medical images using advanced feature analysis. European Journal of Cancer , 48 (4) , 441-446. https://doi.org/10.1016/j.ejca.2011.11.036

Identifiers

DOI
10.1016/j.ejca.2011.11.036
PMID
22257792
PMCID
PMC4533986

Data Quality

Data completeness: 86%