Isabella Castiglioni1, Francesca Gallivanone1, Paolo Soda2, Michele Avanzo3, Joseph Stancanello3,4, Marco Aiello5, Matteo Interlenghi1, Marco Salvatore5. 1. Institute of Molecular Imaging and Physiology, National Research Council (IBFM-CNR), 20090, Segrate, MI, Italy. 2. Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128, Rome, Italy. p.soda@unicampus.it. 3. Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081, Aviano, Italy. 4. Radiologia e Diagnostica per Immagini, Centro Diagnostico Italiano, 20147, Milan, Italy. 5. IRCCS SDN, Istituto di Ricerca Diagnostica e Nucleare, Naples, Italy.
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.
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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