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

Castiglioni, Isabella; Gallivanone, Francesca; Soda, Paolo ✉; Avanzo, Michele; Stancanello, Joseph; Aiello, Marco; Interlenghi, Matteo; Salvatore, Marco

English Scientific Survey paper (Journal Article)
  • SJR Scopus - Medicine (miscellaneous): D1
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.
Citation styles: IEEEACMAPAChicagoHarvardCSLCopyPrint
2022-01-22 19:02