ArticleBMC medical imaging2022
Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma.
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Article
- CT-Based Texture Analysis in Indeterminate Pediatric Renal and Pararenal Masses.The Indian journal of radiology & imaging · 2026Article
- Predicting event-free survival after induction of remission in high-risk pediatric neuroblastoma: combiningPediatric radiology · 2024Article
- CT radiomics to differentiate between Wilms tumor and clear cell sarcoma of the kidney in children.BMC medical imaging · 2024Article
- 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
- Role of MRI radiomics for the prediction of MYCN amplification in neuroblastomas.European radiology · 2023Article
- Clinical value of combined serum CA125, NSE and 24-hour urine VMA for the prediction of recurrence in children with neuroblastoma.Italian journal of pediatrics · 2023Article
- Contrast-enhanced computed tomography radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma.Abdominal radiology (New York) · 2023Article
- Article
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10 authors.
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Abstract
backgroundThis retrospective study aimed to develop and validate a combined model based [
methodsEighty-four high-risk neuroblastoma patients were retrospectively enrolled and divided into training and test sets according to the ratio of 3:2. [
resultsSeven radiomics features were selected for building the R_model. The AUCs of the C_model in training and test sets were 0.744 (95% confidence interval [CI], 0.595-0.874) and 0.750 (95% CI, 0.577-0.904), respectively. The R_model yielded AUCs of 0.813 (95% CI, 0.685-0.916) and 0.869 (95% CI, 0.715-0.985) in the training and test sets, respectively. The RC_model demonstrated the largest AUCs of 0.889 (95% CI, 0.794-0.963) and 0.892 (95% CI, 0.758-0.992) in the training and test sets, respectively. DCA demonstrated that RC_model added more net benefits than either the C_model or the R_model for predicting recurrence in high-risk pediatric neuroblastoma.
conclusionsThe combined model performed well for predicting recurrence in high-risk pediatric neuroblastoma, which can facilitate disease follow-up and management in clinical practice.
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