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Radiomics Reproducibility Challenge in Computed Tomography Imaging As a Nuisance to Clinical Generalization: A Mini-Review Publisher



Jahanshahi A1 ; Soleymani Y2 ; Fazel Ghaziani M3 ; Khezerloo D3
Authors
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Authors Affiliations
  1. 1. Department of Radiology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
  2. 2. Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
  3. 3. Department of Radiology, Faculty of Allied Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran

Source: Egyptian Journal of Radiology and Nuclear Medicine Published:2023


Abstract

Background: Radiomics has demonstrated striking potential in accurate cancer diagnosis but still needs strengthening of validity and standardization to achieve reproducible and generalizable results. Despite the advantages of radiomics, inter-scanner and intra-scanner variations of computed tomography (CT) scanning parameters can affect the reproducibility of its results. Accordingly, this article aims to review the impact of CT scanning parameters on the reproducibility of radiomics results. Main body of the abstract: In general, radiomics results are sensitive to changes in the noise level; therefore, any parameter that affects image noise, such as kilovoltage (kVp), tube current (mAs), slice thickness, spatial resolution, image reconstruction algorithm, etc., can affect radiomics results. Also, region of interest (ROI) segmentation is another fundamental challenge in reducing radiomics reproducibility. Studies showed that almost all scanning parameters affect the reproducibility of radiomics. However, some robust features are reproducible. Short conclusion: One of the solutions to overcome the radiomics reproducibility challenge is the standardization of imaging protocols according to noise level (not scanning protocols). The second solution is to list reproducible features according to the type of complication and anatomical region. Resampling may also overcome feature instability. © 2023, The Author(s).
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