Can Artificial Intelligence Fix the Reproducibility Problem of Radiomics?
Radiology. 2019 Jun 18:191154. doi: 10.1148/radiol.2019191154. [Epub ahead of print]
Radiomics is at the forefront of oncologic radiology research today (1). Radiomics converts medical images such as CT scans into high-throughput quantitative data that can be used to improve diagnostic, prognostic, and predictive accuracy. Indeed, radiomics can provide objective and comprehensive information regarding whole or subregions of cancers in a noninvasive and repeatable way (1). Radiomic signatures have been demonstrated to reflect intratumoral heterogeneity and to be associated with gene-expression profiles, both of which can serve as important prognostic factors (2). A myriad of research articles have showcased the potential of radiomics in providing useful imaging biomarkers in patients with cancer (1–3). However, radiomics has not yet been used in clinical practice due to several major obstacles, including: (a) lack of an easy-to-handle, high-performing set of analytic tools, and (b) variability of radiomic features that are prone to substantial influence from image acquisition parameters, as well as target selection, target segmentation, feature extraction/selection, and radiomics modeling.
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