ArticleFrontiers in medicine2022
Prediction of MYCN Amplification, 1p and 11q Aberrations in Pediatric Neuroblastoma
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.Current oncology reports · 2026Pooled it
- Radiomics-Based Machine Learning for DeterminingTechnology in cancer research & treatmentPooled it
- Prognostic value of baseline [68Ga]Ga‑DOTA‑NOC PET/CT in paediatric neuroblastoma.European journal of nuclear medicine and molecular imaging · 2026Article
- Advances in multi-omics research on neuroblastoma.Frontiers in pediatrics · 2026Review
- Article
- A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges.Pediatric radiology · 2023Review
- Predicting MYCN amplification in paediatric neuroblastoma: development and validation of a 18F-FDG PET/CT-based radiomics signature.Insights into imaging · 2023Article
- Improved risk stratification by PET-based intratumor heterogeneity in children with high-risk neuroblastoma.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study aimed to assess the predictive ability of 18F-FDG PET/CT radiomic features for MYCN, 1p and 11q abnormalities in NB. Method: One hundred and twenty-two pediatric patients (median age 3. 2 years, range, 0.2-9.8 years) with NB were retrospectively enrolled. Significant features by multivariable logistic regression were retained to establish a clinical model (C_model), which included clinical characteristics. 18F-FDG PET/CT radiomic features were extracted by Computational Environment for Radiological Research. The least absolute shrinkage and selection operator (LASSO) regression was used to select radiomic features and build models (R-model). The predictive performance of models constructed by clinical characteristic (C_model), radiomic signature (R_model), and their combinations (CR_model) were compared using receiver operating curves (ROCs). Nomograms based on the radiomic score (rad-score) and clinical parameters were developed. Results: The patients were classified into a training set ( Conclusions: The pre-therapy 18F-FDG PET/CT radiomics is able to predict MYCN amplification and 1p and 11 aberrations in pediatric NB, thus aiding tumor stage, risk stratification and disease management in the clinical practice.
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Registered trials
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