Stability of Liver Radiomics across Different 3D ROI Sizes-An MRI In Vivo Study.

Laura J. Jensen,Sebastian N. Nagel,Thomas Elgeti,Ingo G. Steffen,Bernd Hamm,Damon Kim

doi:10.3390/tomography7040073

Laura J. Jensen, Sebastian N. Nagel + Show 4 more

Open Access

https://doi.org/10.3390/tomography7040073

Copy DOI

Abstract

We aimed to evaluate the stability of radiomic features in the liver of healthy individuals across different three-dimensional regions of interest (3D ROI) sizes in T1-weighted (T1w) and T2-weighted (T2w) images from different MR scanners. We retrospectively included 66 examinations of patients without known diseases or pathological imaging findings acquired on three MRI scanners (3 Tesla I: 25 patients, 3 Tesla II: 19 patients, 1.5 Tesla: 22 patients). 3D ROIs of different diameters (10, 20, 30 mm) were drawn on T1w GRE and T2w TSE images into the liver parenchyma (segment V–VIII). We extracted 93 radiomic features from the different ROIs and tested features for significant differences with the Mann–Whitney-U (MWU)-test. The MWU-test revealed significant differences for most second- and higher-order features, indicating a systematic difference dependent on the ROI size. The features mean, median, root mean squared (RMS), 10th percentile, and 90th percentile were not significantly different. We also assessed feature robustness to ROI size variation with overall concordance correlation coefficients (OCCCs). OCCCs across the different ROI-sizes for mean, median, and RMS were excellent (>0.90) in both sequences on all three scanners. These features, therefore, seem robust to ROI-size variation and suitable for radiomic studies of liver MRI.

Highlights

Radiomics analysis translates a medical image into quantitative features that are otherwise not perceptible to the human eye [1,2]
We aimed to evaluate the stability of radiomic features in the liver of healthy individuals across different three-dimensional regions of interest (3D ROI) sizes in T1-weighted (T1w) and T2weighted (T2w) images from different MR scanners
We retrospectively included 66 examinations of patients without known diseases or pathological imaging findings acquired on three MRI scanners (3 Tesla I: 25 patients, 3 Tesla II: 19 patients, 1.5 Tesla: 22 patients). 3D ROIs of different diameters (10, 20, 30 mm) were drawn on T1w Gradient Echo (GRE) and T2w Turbo Spin Echo (TSE) images into the liver parenchyma

Summary

Introduction

Radiomics analysis translates a medical image into quantitative features that are otherwise not perceptible to the human eye [1,2]. Since these features have quantitative values, they can be statistically linked to various biological and diagnostic endpoints [2,3]. Jajodia et al built prediction models to evaluate the outcome in cervical cancer based on radiomic features derived from ADC maps [4]. There are plenty of radiomic studies attempting to characterize liver abnormalities with the aim to predict their outcome [6]. Contrary to the growing body of published data, radiomics analysis is still not applicable in clinical routine, possibly because of the lack of reproducibility [2,7]

Objectives

Methods

Discussion

Conclusion