AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics.
Introduction: The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 46; no. 13; pp. 2673 - 2700 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2019
|
| 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=139867261&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139867261 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Dec2019 vid: 46 iid: 13 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139867261 144001728 10.1007/s00259-019-04414-4 139867261 ppf: 2673 ppct: 27 formats: fmt: @attributes: type: P tig: atl: AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics. aug: au: Castiglioni, Isabella Gallivanone, Francesca Soda, Paolo Avanzo, Michele Stancanello, Joseph Aiello, Marco Interlenghi, Matteo Salvatore, Marco affil: Institute of Molecular Imaging and Physiology, National Research Council (IBFM-CNR), 20090, Segrate, MI, Italy sug: ab: Introduction: The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes. Objective: The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|