ArticleCancer innovation2023
Machine-learning radiomics to predict bone marrow metastasis of neuroblastoma using magnetic resonance imaging.
Article in Cancer innovation, 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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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
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.Current oncology reports · 2026Pooled it
- Artificial intelligence in paediatric chest imaging: applications, challenges, and future directions.Pediatric radiology · 2026Review
- Prediction of lymphovascular invasion in invasive breast cancer via intratumoral and peritumoral multiparametric magnetic resonance imaging machine learning-based radiomics with Shapley additive explanations interpretability analysis.Quantitative imaging in medicine and surgery · 2025Article
- Pan-cancer secreted proteome and skeletal muscle regulation: insight from a proteogenomic data-driven knowledge base.Functional & integrative genomics · 2025Review
- Risk stratification in neuroblastoma patients through machine learning in the multicenter PRIMAGE cohort.Frontiers in oncology · 2025Article
- Radiomics models to predict bone marrow metastasis of neuroblastoma using CT.Cancer innovation · 2024Article
- Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning.Frontiers in immunology · 2024Article
- Machine-learning radiomics to predict bone marrow metastasis of neuroblastoma using magnetic resonance imaging.Cancer innovation · 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 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Neuroblastoma is one common pediatric malignancy notorious for high temporal and spatial heterogeneities. More than half of its patients develop distant metastases involving vascularized organs, especially the bone marrow. It is thus necessary to have an economical, noninvasive method without much radiation for follow-ups. Radiomics has been used in many cancers to assist accurate diagnosis but not yet in bone marrow metastasis in neuroblastoma. Methods: A total of 182 patients with neuroblastoma were retrospectively collected and randomly divided into the training and validation sets. Five-hundred and seventy-two radiomics features were extracted from magnetic resonance imaging, among which 41 significant ones were selected via Results: Extreme gradient boosting, random forest (RF), and adaptive boosting were the top three to predict bone marrow metastases in neuroblastoma while RF was the most accurate one. Its AUC was 0.90 (0.86-0.93), F1 score was 0.82, sensitivity was 0.76, and negative predictive value was 0.79 in the training set. The values were 0.82 (0.71-0.93), 0.80, 0.75, and 0.92 in the validation set, respectively. Conclusions: Radiomics models are likely to contribute more to metastatic diagnoses and the formulation of personalized healthcare strategies in clinics. It has great potential of being a revolutionary method to replace traditional interventions in the future.
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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.