ArticleBMC pediatrics2023
Identification and validation of radiomic features from computed tomography for preoperative classification of neuroblastic tumors in children.
Article in BMC pediatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.Current oncology reports · 2026Pooled it
- Differentiation of early relapse and late relapse in intermediate- and high-risk neuroblastoma with anAbdominal radiology (New York) · 2024Trial
- Pediatric Personalized Deep Learning Models for Segmentation of Hepatoblastoma at CT and MRI.Radiology. Imaging cancer · 2026Article
- Advances in multi-omics research on neuroblastoma.Frontiers in pediatrics · 2026Review
- Radiomics-based prediction of pathological subtypes in peripheral neuroblastic tumors usingEuropean journal of pediatrics · 2025Observational
- Radiomics to predict tumor response to combination chemoradiotherapy in squamous cell carcinoma of the anal canal: a preliminary investigation.European radiology experimental · 2025Article
- A head-to-head comparison of computed tomography- and magnetic resonance imaging-based radiomics in assessing pediatric peripheral neuroblastic tumor cell behavior.Abdominal radiology (New York) · 2024Article
- A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges.Pediatric radiology · 2023Review
- Imaging of Ganglioneuroma: A Literature Review and a Rare Case of Cystic Presentation in an Adolescent Girl.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo identify radiomic features that can predict the pathological type of neuroblastic tumor in children.
methodsData on neuroblastic tumors in 104 children were retrospectively analyzed. There were 14 cases of ganglioneuroma, 24 cases of ganglioneuroblastoma, and 65 cases of neuroblastoma. Stratified sampling was used to randomly allocate the cases into the training and validation sets in a ratio of 3:1. The maximum relevance-minimum redundancy algorithm was used to identify the top 10 of two clinical features and 851 radiomic features in portal venous-phase contrast-enhanced computed tomography images. Least absolute shrinkage and selection operator regression was used to classify tumors in two binary steps: first as ganglioneuroma compared to the other two types, then as ganglioneuroblastoma compared to neuroblastoma.
resultsBased on 10 clinical-radiomic features, the classifier identified ganglioneuroma compared to the other two tumor types in the validation dataset with sensitivity of 100.0%, specificity of 81.8%, and an area under the receiver operating characteristic curve (AUC) of 0.875. The classifier identified ganglioneuroblastoma versus neuroblastoma with a sensitivity of 83.3%, a specificity of 87.5%, and an AUC of 0.854. The overall accuracy of the classifier across all three types of tumors was 80.8%.
conclusionRadiomic features can help predict the pathological type of neuroblastic tumors in children.
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