ArticleInsights into imaging2022
Article in Insights into 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.
What it found
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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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 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
- CT-Based Texture Analysis in Indeterminate Pediatric Renal and Pararenal Masses.The Indian journal of radiology & imaging · 2026Article
- Scaling of Haralick features with image bit depth and gray level co-occurrence matrix displacement vector for linear gradients.Computers in biology and medicine · 2025Article
- An interpretable machine learning model for predicting bone marrow invasion in patients with lymphoma viaBMC medical informatics and decision making · 2025Article
- Development and validation of a nomogram for predicting bone marrow involvement in lymphoma patients based onAnnals of nuclear medicine · 2025Article
- Radiomics-based prediction of pathological subtypes in peripheral neuroblastic tumors usingEuropean journal of pediatrics · 2025Observational
- Risk stratification in neuroblastoma patients through machine learning in the multicenter PRIMAGE cohort.Frontiers in oncology · 2025Article
- Predicting event-free survival after induction of remission in high-risk pediatric neuroblastoma: combiningPediatric radiology · 2024Article
- A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges.Pediatric radiology · 2023Review
- Radiomics analysis of contrast-enhanced computed tomography in predicting the International Neuroblastoma Pathology Classification in neuroblastoma.Insights into imaging · 2023Article
- Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveTo develop and validate an
methodsA total of 133 patients with neuroblastoma were retrospectively included and randomized into the training set (n = 93) and test set (n = 40). Radiomics features were extracted from both CT and PET images. The radiomics signature was developed. Independent clinical risk factors were identified using the univariate and multivariate logistic regression analyses to construct the clinical model. The clinical-radiomics model, which integrated the radiomics signature and the independent clinical risk factors, was constructed using multivariate logistic regression analysis and finally presented as a radiomics nomogram. The predictive performance of the clinical-radiomics model was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis (DCA).
resultsTwenty-five radiomics features were selected to construct the radiomics signature. Age at diagnosis, neuron-specific enolase and vanillylmandelic acid were identified as independent predictors to establish the clinical model. In the training set, the clinical-radiomics model outperformed the radiomics model or clinical model (AUC: 0.924 vs. 0.900, 0.875) in predicting the BMI, which was then confirmed in the test set (AUC: 0.925 vs. 0.893, 0.910). The calibration curve and DCA demonstrated that the radiomics nomogram had a good consistency and clinical utility.
conclusionThe
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.