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Radiomics: the process and the challenges
Open accessAbstract
"Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging. Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomics data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures. The core hypothesis of radiomics is that these models, which can include biological or medical data, can provide valuable diagnostic, prognostic or predictive information. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (a) image acquisition and reconstruction, (b) image segmentation and rendering, (c) feature extraction and feature qualification and (d) databases and data sharing for eventual (e) ad hoc informatics analyses. Each of these individual processes poses unique challenges. For example, optimum protocols for image acquisition and reconstruction have to be identified and harmonized. Also, segmentations have to be robust and involve minimal operator input. Features have to be generated that robustly reflect the complexity of the individual volumes, but cannot be overly complex or redundant. Furthermore, informatics databases that allow incorporation of image features and image annotations, along with medical and genetic data, have to be generated. Finally, the statistical approaches to analyze these data have to be optimized, as radiomics is not a mature field of study. Each of these processes will be discussed in turn, as well as some of their unique challenges and proposed approaches to solve them. The focus of this article will be on images of non-small-cell lung cancer.
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“The first such study used 182 texture features in combination with 22 semantic features to describe CT images of lung cancer (24).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [24]
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Research works printed but not linked: 1
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Radiomics: Images Are More than Pictures, They Are Data
Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016) · Radiology · doi:10.1148/radiol.2015151169 · Cited by 8,421
Self-citationRadiomics: the bridge between medical imaging and personalized medicine
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“5 iv Chapter 1 On this document While analysis of medical images has practically taken place since the first image was recorded, high throughput analysis of medical images is a more recent phenomenon (Kumar et al., 2012; Lambin et al., 2012; Gillies et al., 2015). The aim of such a radiomics process is to provide decis…”
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Metadata
- Authors
- Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J. W. L., Dekker, A., Fenstermacher, D., Goldgof, D. B., Hall, L. O., Lambin, P., Balagurunathan, Y., Gatenby, R. A., & Gillies, R. J.
- Affiliations
- Moffitt Cancer Center (US)
- University of South Florida (US)
- Harvard University (US)
- Maastricht University Medical Centre (NL)
- Corresponding author
- Gillies, Robert J.
- ORCID
- Venue
- Magnetic Resonance Imaging
- Publisher
- Elsevier BV
- Published
- 2012-11
- Volume
- 30
- Issue
- 9
- Pages
- 1234-1248
- ISSN
- 0730-725X (ISSN-L)
- PMC
- PMC3563280
- OpenAlex
- W2097475056
- Cited by
- 2,258
- References
- 104
- Citations by year
- 2026: 106 · 2025: 189 · 2024: 224 · 2023: 236 · 2022: 277 · 2021: 290 · 2020: 253 · 2019: 209 · 2018: 190 · 2017: 127 · 2016: 86 · 2015: 43 · 2014: 16 · 2013: 6 · 2012: 3
- FWCI
- 16.42
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Radiological Society of North America
- National Cancer Institute (U01CA143062, P30CA076292)
- 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, quantitative imaging features, image segmentation, feature extraction, data sharing, non-small cell lung cancer, computed tomography, positron emission tomography, prediction model
- MeSH terms
- Algorithms, Carcinoma, Non-Small-Cell Lung, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Magnetic Resonance Imaging, Medical Informatics, Pattern Recognition, Automated, Risk Factors, Software, Tomography, X-Ray Computed, Reproducibility of Results, Multivariate Analysis, Radiation Oncology, Phantoms, Imaging, Positron-Emission Tomography
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