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...

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Detalles Bibliográficos
Publicado en:	European Journal of Nuclear Medicine & Molecular Imaging Vol. 46; no. 13; pp. 2673 - 2700
Autores principales:	Castiglioni, Isabella, Gallivanone, Francesca, Soda, Paolo, Avanzo, Michele, Stancanello, Joseph, Aiello, Marco, Interlenghi, Matteo, Salvatore, Marco
Formato:	Journal Article
Publicado:	Springer Nature Dec2019
Acceso en línea:	Ver este registro en EBSCOhost

Descripción
Sumario:	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.