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

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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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Author notes

  1. Isabella Castiglioni and Paolo Soda contributed equally to this work.

Authors and Affiliations

  1. Institute of Molecular Imaging and Physiology, National Research Council (IBFM-CNR), 20090, Segrate, MI, Italy

    Isabella Castiglioni, Francesca Gallivanone & Matteo Interlenghi

  2. Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128, Rome, Italy

    Paolo Soda

  3. Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081, Aviano, Italy

    Michele Avanzo & Joseph Stancanello

  4. Radiologia e Diagnostica per Immagini, Centro Diagnostico Italiano, 20147, Milan, Italy

    Joseph Stancanello

  5. IRCCS SDN, Istituto di Ricerca Diagnostica e Nucleare, Naples, Italy

    Marco Aiello & Marco Salvatore

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  1. Isabella Castiglioni
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Correspondence to Paolo Soda.

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Isabella Castiglioni declares that she has no conflict of interest. Francesca Gallivanone declares that she has no conflict of interest. Paolo Soda declares that he has no conflict of interest. Michele Avanzo declares that he has no conflict of interest. Joseph Stancanello discloses an interest in Oncoradiomics SA. Marco Aiello declares that he has no conflict of interest. Matteo Interlenghi declares that he has no conflict of interest. Marco Salvatore declares that he has no conflict of interest. This article does not contain any studies with human participants or animals performed by any of the authors.

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Castiglioni, I., Gallivanone, F., Soda, P. et al. AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics. Eur J Nucl Med Mol Imaging 46, 2673–2699 (2019). https://doi.org/10.1007/s00259-019-04414-4

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