REVIEW  RADIOMICS IN MULTIMODALITY IMAGING 

The Quarterly Journal of Nuclear Medicine and Molecular Imaging 2019 December;63(4):339-46

DOI: 10.23736/S1824-4785.19.03217-5

Copyright © 2019 EDIZIONI MINERVA MEDICA

language: English

Integrating radiomics into clinical trial design

Jessica J. WANINGER ^{1, 2}, Michael D. GREEN ^{3, 4}, Catherine CHEZE LE REST ⁵, Benjamin ROSEN ³, Issam EL NAQA ³ ✉

¹ Department of Medical Education, University of Michigan School of Medicine, Ann Arbor, MI, USA; ² Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA; ³ Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, MI, USA; ⁴ University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA; ⁵ Department of Nuclear Medicine, Poitiers University Hospital, Poitiers, France

In radiomics, quantitative features that describe phenotypic tumor characteristics are derived from radiographic images. Because radiomics generates information from routine medical images, it is a powerful way to non-invasively examine the spatial and temporal heterogeneity of disease, and thus has potential to significantly impact clinical trial design, execution, and ultimately patient care. The aim of this review article is to discuss how radiomics may address some of the current challenges in clinical randomized control trials, and the difficulties of integrating robust and repeatable radiomics analysis into trial design. Each step of the radiomics process, including image acquisition and reconstruction, image segmentation, feature extraction, and computational analysis, requires extensive standardization in order to be successfully incorporated into clinical trials and inform clinical decision making. By addressing these challenges, the potential of radiomics may be realized.

KEY WORDS: Tomography, X-ray computed; Humans; Magnetic resonance imaging; Diagnostic imaging; Positron-emission tomography