ArticleCancers2022
Comparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance Images.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Deep learning applications in osteosarcoma MRI: A systematic review of recent advances in AI-based osteosarcoma diagnosis.PloS one · 2026Pooled it
- Whole-tumor radiomics analysis of T2-weighted imaging in differentiating neuroblastoma from ganglioneuroblastoma/ganglioneuroma in children: an exploratory study.Abdominal radiology (New York) · 2023Trial
- Reasoning in machine vision by learning fast and slow thinking.Nature communications · 2026Article
- A deterministic method for quantifying spindle-shaped cells in noisy bright-field microscopy.Scientific reports · 2026Article
- Performances of experts and automated methods on new multiple sclerosis lesions detection: insights from the MSSeg2 challenge.Scientific reports · 2026Article
- DARE-FUSE: domain aligned evidence guided learning for joint brain tumor MRI segmentation and classification.NPJ digital medicine · 2026Article
- Deep Learning Auto-segmentation of Diffuse Midline Glioma on Multimodal Magnetic Resonance Images.Journal of imaging informatics in medicine · 2026Article
- Automated radiomics pipeline integrating TotalSpineSeg for Pfirrmann grading of lumbar disc degeneration with SHAP-based interpretability.Frontiers in medicine · 2026Article
- A swarm intelligence-driven hybrid framework for brain tumor classification with enhanced deep features.Scientific reports · 2025Article
- A Community Benchmark for the Automated Segmentation of Pediatric Neuroblastoma on Multi-Modal MRI: Design and Results of the SPPIN Challenge at MICCAI 2023.Bioengineering (Basel, Switzerland) · 2025Article
- A Multidimensional Particle Swarm Optimization-Based Algorithm for Brain MRI Tumor Segmentation.Sensors (Basel, Switzerland) · 2025Article
- Deep Learning and Multidisciplinary Imaging in Pediatric Surgical Oncology: A Scoping Review.Cancer medicine · 2025Article
- Impact of signal-to-noise ratio and contrast definition on the sensitivity assessment and benchmarking of fluorescence molecular imaging systems.Journal of biomedical optics · 2025Article
- Comparison of Vendor-Pretrained and Custom-Trained Deep Learning Segmentation Models for Head-and-Neck, Breast, and Prostate Cancers.Diagnostics (Basel, Switzerland) · 2024Article
- Multicellular model of neuroblastoma proposes unconventional therapy based on multiple roles of p53.PLoS computational biology · 2024Article
- Insights into geometric deviations of medical 3d-printing: a phantom study utilizing error propagation analysis.3D printing in medicine · 2024Article
- Article
- Could CT Radiomic Analysis of Benign Adrenal Incidentalomas Suggest the Need for Further Endocrinological Evaluation?Current oncology (Toronto, Ont.) · 2024Article
- Physics-informed deep generative learning for quantitative assessment of the retina.Nature communications · 2024Article
- Reproducibility Analysis of Radiomic Features on T2-weighted MR Images after Processing and Segmentation Alterations in Neuroblastoma Tumors.Radiology. Artificial intelligence · 2024Article
Corrections and comments
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Authors and funding
13 authors at 5 institutions in 3 countries.
Funding
Abstract
Tumor segmentation is one of the key steps in imaging processing. The goals of this study were to assess the inter-observer variability in manual segmentation of neuroblastic tumors and to analyze whether the state-of-the-art deep learning architecture nnU-Net can provide a robust solution to detect and segment tumors on MR images. A retrospective multicenter study of 132 patients with neuroblastic tumors was performed. Dice Similarity Coefficient (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC) were used to compare segmentation sets. Two more metrics were elaborated to understand the direction of the errors: the modified version of False Positive (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented 46 tumors and a comparative study was performed. nnU-Net was trained-tuned with 106 cases divided into five balanced folds to perform cross-validation. The five resulting models were used as an ensemble solution to measure training (n = 106) and validation (n = 26) performance, independently. The time needed by the model to automatically segment 20 cases was compared to the time required for manual segmentation. The median DSC for manual segmentation sets was 0.969 (±0.032 IQR). The median DSC for the automatic tool was 0.965 (±0.018 IQR). The automatic segmentation model achieved a better performance regarding the FPRm. MR images segmentation variability is similar between radiologists and nnU-Net. Time leverage when using the automatic model with posterior visual validation and manual adjustment corresponds to 92.8%.
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