Pre-trial quality assurance of diffusion-weighted MRI for radiomic analysis and the role of harmonisation. (November 2022) - Paquier, Zelda; Chao, Shih-Li; Bregni, Giacomo; Sanchez, Ana Veron; Guiot, Thomas; Dhont, Jennifer; Gulyban, Akos; Levillain, Hugo; Sclafani, Francesco; Reynaert, Nick; Bali, Maria Antonietta
Highlights: 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. Abstract: Purpose: 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. Materials and methods: 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. Results: 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 mm 2 /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. Conclusion: 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.