Evaluation of multiparametric MRI for deep learning-based segmentation of Wilms tumor

Deep learning techniques to segment Wilms tumor typically use a single MRI sequence as input. The aim of this study was to assess whether multiparametric MRI input improves Wilms tumor segmentation. 45 patients were consecutively included, of which 36 were used for training and nine for testing. All seven input combinations of postcontrast T1-weighted imaging, T2-weighted imaging, and diffusion weighted imaging (DWI) were used for nnU-Net training. Dice scores and the 95th percentile of the Haussdorf distance (HD95) were used to evaluate the input combinations. The median Dice score was highest when combining all MRI sequences (Dice = 0.93), the median HD95 was lowest when combining postcontrast T1-weighted imaging and DWI (HD95 = 5.4 mm). Single-parametric DWI input performed significantly worse than other input combinations (median Dice = 0.64, median HD95 = 29.5 mm, p = 0.004). All other combinations, including standalone sequences, showed similar performance to each other. Our results suggest that adding sequences to standalone T1-weighted or T2-weighted imaging does not significantly improve segmentation results.