Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype.
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Embargo End Date
ICR Authors
Authors
Fornacon-Wood, I
Faivre-Finn, C
O'Connor, JPB
Price, GJ
Faivre-Finn, C
O'Connor, JPB
Price, GJ
Document Type
Journal Article
Date
2020-08-01
Date Accepted
2020-05-23
Abstract
Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.
Citation
Lung cancer (Amsterdam, Netherlands), 2020, 146 pp. 197 - 208
Source Title
Publisher
ELSEVIER IRELAND LTD
ISSN
0169-5002
eISSN
1872-8332
Collections
Research Team
Quantitative Biomedical Imaging