ArticleBMC pediatrics2024
Automated segmentation and volume prediction in pediatric Wilms' tumor CT using nnu-net.
Article in BMC pediatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Comparison of Radiological and Postoperative Tumor Volume Measurements for SIOP Risk Stratification in Wilms Tumord.Diagnostics (Basel, Switzerland) · 2026Article
- The role of artificial intelligence in paediatric abdominal imaging.Pediatric radiology · 2026Review
- Beyond AUC: a clinician's guide to building and trusting prediction models in oncology-a narrative review.Frontiers in oncology · 2026Review
- MRI Radiomics-Based Machine Learning to Predict Lymphovascular Invasion of HER2-Positive Breast Cancer.Journal of imaging informatics in medicine · 2025Article
- Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study.Tomography (Ann Arbor, Mich.) · 2025Article
- Exploring theFrontiers in physiology · 2025Article
- Artificial Intelligence Models in Diagnosis and Treatment of Kidney Diseases: Current Status and Prospects.Kidney diseases (Basel, Switzerland)Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundRadiologic volumetric evaluation of Wilms' tumor (WT) is an important indicator to guide treatment decisions. However, due to the heterogeneity of the tumors, radiologists have main-guard differences in diagnosis that can lead to misdiagnosis and poor treatment. The aim of this study was to explore whether CT-based outlining of WT foci can be automated using deep learning.
methodsWe included CT intravenous phase images of 105 patients with WT and double-blind outlining of lesions by two radiologists. Then, we trained an automatic segmentation model using nnUnet. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD
resultsThe DSC and HD
conclusionWe introduce a novel method that enhances the accuracy of predicting WT volume by integrating AI automated outlining and 3D tumor diameters. This approach surpasses the accuracy of using AI outcomes alone and has the potential to enhance the clinical evaluation of pediatric patients with WT. By intertwining AI outcomes with clinical data, this method becomes more interpretive and offers promising applications beyond Wilms tumor, extending to other pediatric diseases.
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Registered trials
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