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REVIEW  RADIOMICS IN MULTIMODALITY IMAGING Freefree

The Quarterly Journal of Nuclear Medicine and Molecular Imaging 2019 December;63(4):323-38

DOI: 10.23736/S1824-4785.19.03213-8

Copyright © 2019 EDIZIONI MINERVA MEDICA

language: English

Machine learning for radiomics-based multimodality and multiparametric modeling

Lise WEI 1, Sarah OSMAN 2, Mathieu HATT 3, Issam EL NAQA 1

1 Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA; 2 Centre for Cancer Research and Cell Biology, Queens’ University, Belfast, UK; 3 LaTIM, INSERM, UMR 1101, University of Brest, Brest, France



Due to the recent developments of both hardware and software technologies, multimodality medical imaging techniques have been increasingly applied in clinical practice and research studies. Previously, the application of multimodality imaging in oncology has been mainly related to combining anatomical and functional imaging to improve diagnostic specificity and/or target definition, such as positron emission tomography/computed tomography (PET/CT) and single-photon emission CT (SPECT)/CT. More recently, the fusion of various images, such as multiparametric magnetic resonance imaging (MRI) sequences, different PET tracer images, PET/MRI, has become more prevalent, which has enabled more comprehensive characterization of the tumor phenotype. In order to take advantage of these valuable multimodal data for clinical decision making using radiomics, we present two ways to implement the multimodal image analysis, namely radiomic (handcrafted feature) based and deep learning (machine learned feature) based methods. Applying advanced machine (deep) learning algorithms across multimodality images have shown better results compared with single modality modeling for prognostic and/or prediction of clinical outcomes. This holds great potentials for providing more personalized treatment for patients and achieve better outcomes.


KEY WORDS: Multimodal imaging; Deep learning; Oncology

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