Radiomics: a primer on high-throughput image phenotyping.
Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may...
| Publicado en: | Abdominal Radiology Vol. 47; no. 9; pp. 2986 - 3003 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158610915&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158610915 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Sep2022 vid: 47 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 158610915 152074032 10.1007/s00261-021-03254-x 158610915 ppf: 2986 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics: a primer on high-throughput image phenotyping. aug: au: Lafata, Kyle J. Wang, Yuqi Konkel, Brandon Yin, Fang-Fang Bashir, Mustafa R. affil: Department of Radiology, Duke University School of Medicine, Durham, NC, USA sug: ab: Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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