arXiv subjects

A. F. W. van der Steeg

Publications and source records attributed to A. F. W. van der Steeg.

2 recordsLinked to original sources

arXiv2026

Direct correlation of intravoxel incoherent motion imaging based on implicit neural representations and histopathology in pediatric Wilms tumors: can it differentiate between subtypes?

Wilms tumor (WT) is a heterogeneous pediatric renal malignancy. Invasive procedures to establish histology prior to pre operative treatment are not recommended by the International Society of Pediatric (SIOP) Oncology Renal Tumor Study Group, yet different tissue components are associated with different responses to therapy (SIOP Umbrella protocol). Intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) may improve subtype differentiation before surgery as a noninvasive radiographic biomarker by capturing both diffusion and perfusion characteristics, as opposed to the conventionally used apparent diffusion coefficient (ADC). The objective of this study is to evaluate the discriminative performance of IVIM MRI in differentiating WT subtypes. IVIM provides quantitative parameters that disentangle the diffusion coefficient (D), incoherent blood flow (pseudodiffusion Dstar) and perfusion fraction (f). This retrospective study included thirthy children with WT. Standardized 1.5T MRI with 10 b value diffusion weighted imaging (DWI) was performed. IVIM parameters were estimated using a selfsupervised implicit neural representations network and were correlated with histopathology via a patient specific 3D printed cutting guide. Statistical analyses included mixed models, receiver operating curve analysis, and logistic regression. Thirty lesions were MRI histopathology correlated on slice level. Significant subtype differences were observed for Dstar stromal versus epithelial (p = 0.024), regressive tumors (p = 0.031) and mixed WT tumors (p = 0.043). ADC showed no significant subtype differences. Direct MRI histopathology correlation enabled differentiation between stromal subtype and epithelial, mixed and regressive subtype on macroscopic slice level using Dstar.

physics.med-ph↗

arXiv2025

Automated segmentation of pediatric neuroblastoma on multi-modal MRI: Results of the SPPIN challenge at MICCAI 2023

Surgery plays an important role within the treatment for neuroblastoma, a common pediatric cancer. This requires careful planning, often via magnetic resonance imaging (MRI)-based anatomical 3D models. However, creating these models is often time-consuming and user dependent. We organized the Surgical Planning in Pediatric Neuroblastoma (SPPIN) challenge, to stimulate developments on this topic, and set a benchmark for fully automatic segmentation of neuroblastoma on multi-model MRI. The challenge started with a training phase, where teams received 78 sets of MRI scans from 34 patients, consisting of both diagnostic and post-chemotherapy MRI scans. The final test phase, consisting of 18 MRI sets from 9 patients, determined the ranking of the teams. Ranking was based on the Dice similarity coefficient (Dice score), the 95th percentile of the Hausdorff distance (HD95) and the volumetric similarity (VS). The SPPIN challenge was hosted at MICCAI 2023. The final leaderboard consisted of 9 teams. The highest-ranking team achieved a median Dice score 0.82, a median HD95 of 7.69 mm and a VS of 0.91, utilizing a large, pretrained network called STU-Net. A significant difference for the segmentation results between diagnostic and post-chemotherapy MRI scans was observed (Dice = 0.89 vs Dice = 0.59, P = 0.01) for the highest-ranking team. SPPIN is the first medical segmentation challenge in extracranial pediatric oncology. The highest-ranking team used a large pre-trained network, suggesting that pretraining can be of use in small, heterogenous datasets. Although the results of the highest-ranking team were high for most patients, segmentation especially in small, pre-treated tumors were insufficient. Therefore, more reliable segmentation methods are needed to create clinically applicable models to aid surgical planning in pediatric neuroblastoma.

cs.CV↗