Purpose. Due to the size and localization of Wilms’ tumor (WT), nephron-sparing surgery (NSS) is only possible in a limited number of cases. When NSS is considered, the surgeon preoperatively requires a thorough understanding of the patient-specific anatomy to prevent positive surgical margins and other complications. Through a collaboration between the radiology and pediatric surgery departments and 3D imaging specialists, a 3D visualization workflow was developed to improve preoperative planning of NSS for WT patients. Methods. The 3D visualization workflow combines a MRA sequence, a segmentation protocol, and augmented reality (AR) visualization, additional to in-house 3D printing. A noncontrast-enhanced MRA scan was added to the MRI protocol. MRI sequences were segmented with a segmentation protocol in an open-source software package. The resulting 3D models were visualized in AR with a HoloLens and 3D print. Results. In a pilot study, five WT patients eligible for NSS were preoperatively planned through the 3D visualization workflow. AR visualization software was fast and free to use and allowed adequate handling of the 3D holograms. The 3D printed models were considered convenient and practical for intraoperative orientation. The patient-friendly, fast, and low-cost 3D visualization workflow was easily implemented and appeared to be valuable for the preparation of NSS. Conclusion. This pilot study demonstrates how a strong collaboration between the pediatric surgery and radiology departments and 3D imaging specialists will help to shape the future of pediatric oncological surgery. This 3D visualization workflow aims to prepare pediatric oncological surgeons for nephron-sparing surgery in patients with Wilms’ tumors.

Nephron-sparing surgery (NSS) in Wilms tumor (WT) patients is a surgically challenging procedure used in highly selective cases only. Virtual resections can be used for preoperative planning of NSS to estimate the remnant renal volume (RRV) and to virtually mimic radical tumor resection. In this single-center evaluation study, virtual resection for NSS planning and the user experience were evaluated. Virtual resection was performed in nine WT patient cases by two pediatric surgeons and one pediatric urologist. Pre- and postoperative MRI scans were used for 3D visualization. The virtual RRV was acquired after performing virtual resection and a questionnaire was used to assess the ease of use. The actual RRV was derived from the postoperative 3D visualization and compared with the derived virtual RRV. Virtual resection resulted in virtual RRVs that matched nearly perfectly with the actual RRVs. According to the questionnaire, virtual resection appeared to be straightforward and was not considered to be difficult. This study demonstrated the potential of virtual resection as a new planning tool to estimate the RRV after NSS in WT patients. Future research should further evaluate the clinical relevance of virtual resection by relating it to surgical outcome.

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Background Pediatric renal tumors are often heterogeneous lesions with variable regions of distinct histopathology. Direct comparison between in vivo imaging and ex vivo histopathology might be useful for identification of discriminating imaging features. Objective This feasibility study explored the use of a patient-specific three-dimensional (3D)-printed cutting guide to ensure correct alignment (orientation and slice thickness) between magnetic resonance imaging (MRI) and histopathology. Materials and methods Before total nephrectomy, a patient-specific cutting guide based on each patient’s preoperative renal MRI was generated and 3-D printed, to enable consistent transverse orientation of the histological specimen slices with MRI slices. This was expected to result in macroscopic slices of 5 mm each. The feasibility of the technique was determined qualitatively, through questionnaires administered to involved experts, and quantitatively, based on structured measurements including overlap calculation using the dice similarity coefficient. Results The cutting guide was used in eight Wilms tumor patients receiving a total nephrectomy, after preoperative chemotherapy. The median age at diagnosis was 50 months (range: 4–100 months). The positioning and slicing of the specimens were rated overall as easy and the median macroscopic slice thickness of each specimen ranged from 5 to 6 mm. Tumor consistency strongly influenced the practical application of the cutting guide. Digital correlation of a total of 32 slices resulted in a median dice similarity coefficient of 0.912 (range: 0.530–0.960). Conclusion We report the feasibility of a patient-specific 3-D-printed MRI-based cutting guide for pediatric renal tumors, allowing improvement of the correlation of MRI and histopathology in future studies.

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.