The impact of the variation of imaging factors on the robustness of Computed Tomography Radiomic Features: A review

Abstract

The field of radiomics is at the forefront of personalized medicine. However, there are concerns regarding the robustness of its features against multiple medical imaging parameters and the performance of the predictive models built upon them. Therefore, our review aims to identify image perturbation factors (IPF) that most influence the robustness of radiomic features in biomedical research. We also provide insights into the validity and discrepancy of different methodologies applied to investigate the robustness of radiomic features. We selected 527 papers based on the primary criterion that the papers had imaging parameters that affected the reproducibility of radiomic features extracted from computed tomography (CT) images. We compared the reported performance of these parameters along with IPF in the eligible studies. We then proceeded to divide our studies into three groups based on the type of their IPF. The three groups were (i) scanner parameters, (ii) acquisition parameters and (iii) reconstruction parameters. Our review highlighted that the reconstruction algorithm was the most reproducible factor and shape along with Intensity histogram (IH) were the most robust radiomic features against variation in imaging parameters. This review identified substantial inconsistencies related to the methodology and the reporting style of the reviewed studies such as type of study performed, the metrics used for robustness, the feature extraction techniques, the imaging factors, the reporting style and their outcome inclusion. Finally, we hope the IPFs and the methodology inconsistencies identified will aid the scientific community in devising its research in a way that is more reproducible and avoids the pitfalls of previous analyses.

The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset

“…A major area of research in the field of radiomics is the selection of robust and informative image features to be used as input for machine learning models [ 5 ]. Evidence suggests that radiomic features (RFs) are sensitive to differences in several factors, including make and type of imaging scanner, reconstruction settings, and protocols used to acquire the images [ 6 , 7 ].…”

Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds

“…Regarding density analysis of lung disease in CT, histograms are a robust method for the visualization and quantification of Hounsfield unit (HU) differences [ 14 ]. Shifts in density histogram curves have been used to differentiate between normal lungs and those affected by structural lung pathologies, such as fibrosis and emphysema [ 15 , 16 , 17 , 18 , 19 ].…”

PixelPrint: Three-dimensional printing of realistic patient-specific lung phantoms for validation of computed tomography post-processing and inference algorithms

“…However, several major challenges hamper the widespread clinical translation of these promising new capabilities. The problem of data variability , which stems from differences in image acquisition and reconstruction settings among medical institutions, and scanner models, is recognized by many as a critical hurdle that requires dedicated solutions to enable the scalability of developed algorithms [6][7][8] . While recent studies made significant progress with solutions to account for some of the data variability, i.e., normalizations of image quality or imaging features, there is a critical need for lifelike phantoms that will enable the affirmations of these solutions without introducing additional risk to patients or logistical restrictions.…”

“…While these differences typically have little clinical impacts for routine radiological interpretation, they introduce biases when analyzed numerically to extract meaningful data 6 . This hampers advancement of reproducible feature extraction pipelines, a critical pre-requisite for clinical translation 7 .…”