Can Artificial Intelligence Fix the Reproducibility Problem of Radiomics?
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.
For CT radiomics, image reconstruction algorithms (ie, reconstruction kernels) and section thickness have been major sources of radiomic feature variability (4,5). Indeed, variability stemming from the use of different reconstruction kernels impedes the comparability of radiomic features between studies performed by different groups as well as in longitudinal studies that include a variety of imaging parameters. Theoretically, we may solve this issue by obtaining raw CT data (sinograms of CT acquisition) or by reconstructing and storing many combinations of CT reconstruction parameters. However, such solutions are not feasible in practice. Thus, another method to resolve this variability is warranted.
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