Deep learning-based Wilms tumor segmentation to create 3D models for surgical planning; implementation in the clinical workflow

Background and aim: Creating 3D models based on pre-operative MRI of patients with a Wilms tumor (WT) can aid surgical planning. However, creating these models requires manual delineation (segmentation) of the MRI imaging. Deep learning can automate this, but most validations of these segmentation methods are retrospective. This article prospective evaluation of a WT segmentation method in a clinical workflow aimed at creating 3D models for surgical planning. Methods: A deep learning-based segmentation method was developed and retrospectively validated on a dataset of 56 patients. This method, based on nnU-Net, segmented both kidney and tumor and was implemented within the current clinical workflow. It was tested prospectively on 10 consecutive patients with WT. The performance of this method was quantified using the Dice score between the automated and corrected segmentations, the time required for the various steps of the clinical workflow and an analysis of segmentation errors. Results: In 2/10 patients the automated segmentation was sufficient to be used directly. In 8/10 patients the automated segmentation needed corrections (with a median correction time of 11 min). The median Dice score for kidney was 1.00 (range: 0.58–1.00) and for tumor 0.98 (range: 0.00–1.00). The segmentation errors identified most often were an under-segmentation of tumor borders (n = 3) and the incorrect identification of the tumor/kidney border (n = 3). Conclusion: The implementation of an automated segmentation method for creating 3D models of patients with a WT is feasible in current clinical workflows. In 20% of the patients, no corrections were needed and for most other patients, corrections could be applied in less than 15 min. Clinical Trial Registration: None.