Repeatability and reproducibility of MRI‐radiomic features: A phantom experiment on a 1.5 T scanner. Issue 2 (9th November 2022)

• Record Type: Journal Article
• Authors: Bologna, Marco; Tenconi, Chiara; Corino, Valentina D. A.; Annunziata, Gaetano; Orlandi, Ester; Calareso, Giuseppina; Pignoli, Emanuele; Valdagni, Riccardo; Mainardi, Luca T.; Rancati, Tiziana
• Is Part Of: Medical physics. Volume 50:Issue 2(2023)
• Page Start: 750
• Page End: 762
• Publication Date: 2022-11-09
• Subjects: magnetic resonance imaging -- physical phantom -- radiomics; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.16054
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: Aim of this study is to assess the repeatability of radiomic features on magnetic resonance images (MRI) and their stability to variations in time of repetition (TR), time of echo (TE), slice thickness (ST), and pixel spacing (PS) using vegetable phantoms. Methods: The organic phantom was realized using two cucumbers placed inside a cylindrical container, and the analysis was performed using T1‐weighted (T1w), T2‐weighted (T2w), and diffusion‐weighted images. One dataset was used to test the repeatability of the radiomic features, whereas other four datasets were used to test the sensitivity of the different MRI sequences to image acquisition parameters (TR, TE, ST, and PS). Four regions of interest (ROIs) were segmented: two for the central part of each cucumber and two for the external parts. Radiomic features were extracted from each ROI using Pyradiomics. To assess the effect of preprocessing on the reduction of variability, features were extracted both before and after the preprocessing. The coefficient of variation (CV) and intra‐class correlation coefficient (ICC) were used to evaluate variability. Results: The use of intensity standardization increased the stability for the first‐order statistics features. Shape and size features were always stable for all the analyses. Textural features were particularly sensitive to changes in ST and PS, although some increase in stability could be obtained by voxel size resampling. When images underwent image preprocessing, the number of stable features (ICC > 0.75 and mean absolute CV < 0.3) was 33 for apparent diffusion coefficient (ADC), 52 for T1w, and 73 for T2w. Conclusions: The most critical source of variability is related to changes in voxel size (either caused by changes in ST or PS). Preprocessing increases features stability to both test–retest and variation of the image acquisition parameters for all the types of analyzed MRI (T1w, T2w, and ADC), except for ST.