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Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging 13/2019

11.07.2019 | Review Article

AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics

verfasst von: Isabella Castiglioni, Francesca Gallivanone, Paolo Soda, Michele Avanzo, Joseph Stancanello, Marco Aiello, Matteo Interlenghi, Marco Salvatore

Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging | Ausgabe 13/2019

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Abstract

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.
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Metadaten
Titel
AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics
verfasst von
Isabella Castiglioni
Francesca Gallivanone
Paolo Soda
Michele Avanzo
Joseph Stancanello
Marco Aiello
Matteo Interlenghi
Marco Salvatore
Publikationsdatum
11.07.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
European Journal of Nuclear Medicine and Molecular Imaging / Ausgabe 13/2019
Print ISSN: 1619-7070
Elektronische ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-019-04414-4

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