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

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

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)2673-2699
Number of pages27
JournalEuropean Journal of Pediatrics
Volume46
Issue number13
DOIs
Publication statusPublished - Dec 1 2019

Keywords

  • Artificial intelligence
  • Decision models
  • Hybrid imaging
  • PET/CT
  • PET/MRI
  • Radiomics

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