Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers.
Abstract
Existing quantitative imaging biomarkers (QIBs) are associated with known biological
tissue characteristics and follow a well-understood path of technical, biological
and clinical validation before incorporation into clinical trials. In radiomics, novel
data-driven processes extract numerous visually imperceptible statistical features
from the imaging data with no a priori assumptions on their correlation with biological
processes. The selection of relevant features (radiomic signature) and incorporation
into clinical trials therefore requires additional considerations to ensure meaningful
imaging endpoints. Also, the number of radiomic features tested means that power calculations
would result in sample sizes impossible to achieve within clinical trials. This article
examines how the process of standardising and validating data-driven imaging biomarkers
differs from those based on biological associations. Radiomic signatures are best
developed initially on datasets that represent diversity of acquisition protocols
as well as diversity of disease and of normal findings, rather than within clinical
trials with standardised and optimised protocols as this would risk the selection
of radiomic features being linked to the imaging process rather than the pathology.
Normalisation through discretisation and feature harmonisation are essential pre-processing
steps. Biological correlation may be performed after the technical and clinical validity
of a radiomic signature is established, but is not mandatory. Feature selection may
be part of discovery within a radiomics-specific trial or represent exploratory endpoints
within an established trial; a previously validated radiomic signature may even be
used as a primary/secondary endpoint, particularly if associations are demonstrated
with specific biological processes and pathways being targeted within clinical trials.
KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality
of the dataset compared to sample size, making adequate diversity of the data, cross-validation
and external validation essential to mitigate the risks of spurious associations and
overfitting. • Use of radiomic signatures within clinical trials requires multistep
standardisation of image acquisition, image analysis and data mining processes. •
Biological correlation may be established after clinical validation but is not mandatory.
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https://hdl.handle.net/10161/22315Published Version (Please cite this version)
10.1007/s00330-020-07598-8Publication Info
Fournier, Laure; Costaridou, Lena; Bidaut, Luc; Michoux, Nicolas; Lecouvet, Frederic
E; de Geus-Oei, Lioe-Fee; ... European Society Of Radiology (2021). Incorporating radiomics into clinical trials: expert consensus on considerations for
data-driven compared to biologically driven quantitative biomarkers. European radiology. 10.1007/s00330-020-07598-8. Retrieved from https://hdl.handle.net/10161/22315.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Daniel Carl Sullivan
Professor Emeritus of Radiology
Research interests are in oncologic imaging, especially the clinical evaluation and
validation of imaging biomarkers for therapeutic response assessment.

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