1. Pre-trial quality assurance of diffusion-weighted MRI for radiomic analysis and the role of harmonisation.
- Author
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Paquier, Zelda, Chao, Shih-Li, Bregni, Giacomo, Sanchez, Ana Veron, Guiot, Thomas, Dhont, Jennifer, Gulyban, Akos, Levillain, Hugo, Sclafani, Francesco, Reynaert, Nick, and Bali, Maria Antonietta
- Abstract
• Mean ADC values are robust across different scanners with imaging protocol standardisation. • Radiomic features of ADC map in a homogeneous phantom show substantial variation between MRI systems, even after ComBat harmonisation. • Resampling to a smaller voxel size provides more repeatable and reproducible radiomic features than resampling to a larger voxel size. The aim of this study was to perform a quantitative quality assurance of diffusion-weighted MRI to assess the variability of the mean apparent diffusion coefficient (ADC) and other radiomic features across the scanners involved in the REGINA trial. The NIST/QIBA diffusion phantom was acquired on six 3 T scanners from five centres with a rectum-specific diffusion protocol. All sequences were repeated in each scan session without moving the phantom from the table. Linear interpolation to two isotropic voxel spacing (0.9 and 4 mm) was performed as well as the ComBat feature harmonisation method between scanners. The absolute accuracy error was evaluated for the mean ADC. Repeatability and reproducibility within-subject coefficients of variation (wCV) were computed for 142 radiomic features. For the mean ADC, accuracy error ranged between 0.1 % and 8.5 %, repeatability was <1 % and reproducibility was <3 % for diffusivity range between 0.4 and 1.1x10
-3 mm2 /s. For the other radiomic features, wCV was below 10 % for 24 % and 15 % features for repeatability with resampling 0.9 mm and 4 mm, respectively, and 13 % and 11 % feature for reproducibility. ComBat method could improve significantly the wCV compared to reproducibility without ComBat (p-value < 0.001) but variation was still high for most of the features. Our study provided the first investigation of feature selection for development of robust predictive models in the REGINA trial, demonstrating the added value of such a quality assurance process to select conventional and radiomic features in prospective multicentre trials. [ABSTRACT FROM AUTHOR]- Published
- 2022
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