Wilms tumor is a common pediatric solid tumor. To evaluate tumor response to chemotherapy and decide whether nephron-sparing surgery is possible, tumor volume measurements based on magnetic resonance imaging (MRI) are important. Currently, radiological volume measurements are based on measuring tumor dimensions in three directions. Manual segmentation-based volume measurements might be more accurate, but this process is time-consuming and user-dependent. The aim of this study was to investigate whether manual segmentation-based volume measurements are more accurate and to explore whether these segmentations can be automated using deep learning. We included the MRI images of 45 Wilms tumor patients (age 0–18 years). First, we compared radiological tumor volumes with manual segmentation-based tumor volume measurements. Next, we created an automated segmentation method by training a nnU-Net in a five-fold cross-validation. Segmentation quality was validated by comparing the automated segmentation with the manually created ground truth segmentations, using Dice scores and the 95th percentile of the Hausdorff distances (HD95). On average, manual tumor segmentations result in larger tumor volumes. For automated segmentation, the median dice was 0.90. The median HD95 was 7.2 mm. We showed that radiological volume measurements underestimated tumor volume by about 10% when compared to manual segmentation-based volume measurements. Deep learning can potentially be used to replace manual segmentation to benefit from accurate volume measurements without time and observer constraints.

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 9 for testing. All seven input combinations of postcontrast T 1 -weighted imaging, T 2 -weighted imaging, and diffusion weighted imaging (DWI) were used for nnU-Net training. Dice scores and the 95 th 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 T 1 -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 T 1 -weighted or T 2 -weighted imaging does not significantly improve segmentation results.