How to develop a meaningful radiomic signature for clinical use in oncologic patients.
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Embargo End Date
ICR Authors
Authors
Papanikolaou, N
Matos, C
Koh, DM
Matos, C
Koh, DM
Document Type
Journal Article
Date
2020-05-01
Date Accepted
2020-04-15
Abstract
During the last decade, there is an increasing usage of quantitative methods in Radiology in an effort to reduce the diagnostic variability associated with a subjective manner of radiological interpretation. Combined approaches where visual assessment made by the radiologist is augmented by quantitative imaging biomarkers are gaining attention. Advances in machine learning resulted in the rise of radiomics that is a new methodology referring to the extraction of quantitative information from medical images. Radiomics are based on the development of computational models, referred to as "Radiomic Signatures", trying to address either unmet clinical needs, mostly in the field of oncologic imaging, or to compare radiomics performance with that of radiologists. However, to explore this new technology, initial publications did not consider best practices in the field of machine learning resulting in publications with questionable clinical value. In this paper, our effort was concentrated on how to avoid methodological mistakes and consider critical issues in the workflow of the development of clinically meaningful radiomic signatures.
Citation
Cancer imaging : the official publication of the International Cancer Imaging Society, 2020, 20 (1), pp. 33 - ?
Source Title
Publisher
BMC
ISSN
1740-5025
eISSN
1470-7330