ArticleCancer research2004
Prediction of clinical outcome using gene expression profiling and artificial neural networks for patients with neuroblastoma.
Article in Cancer research, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 77 papers, 3 of them syntheses 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
77 citing papers in PubMed, 3 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
- Patient-derived human tumour tissue xenografts in immunodeficient mice: a systematic review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2010Pooled it
- Cross-study analysis of gene expression data for intermediate neuroblastoma identifies two biological subtypes.BMC cancer · 2007Pooled it
- Emerging Trends in Neuroblastoma Diagnosis, Therapeutics, and Research.Molecular neurobiology · 2025Review
- Artificial intelligence methods available for cancer research.Frontiers of medicine · 2024Review
- Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review.Medical sciences (Basel, Switzerland) · 2024Review
- Development and validation of a 21-gene prognostic signature in neuroblastoma.Scientific reports · 2023Article
- Trends and patterns in cancer nanotechnology research: A survey of NCI's caNanoLab and nanotechnology characterization laboratory.Advanced drug delivery reviews · 2022Review
- EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer.Scientific reports · 2022Article
- Risk Prediction in Patients With Heart Failure With Preserved Ejection Fraction Using Gene Expression Data and Machine Learning.Frontiers in genetics · 2021Article
- Maximizing the reusability of gene expression data by predicting missing metadata.PLoS computational biology · 2020Article
- Targeting the Tumor Microenvironment in Neuroblastoma: Recent Advances and Future Directions.Cancers · 2020Review
- The Potential Functional Roles of NME1 Histidine Kinase Activity in Neuroblastoma Pathogenesis.International journal of molecular sciences · 2020Article
- Identification and characterization of the lncRNA signature associated with overall survival in patients with neuroblastoma.Scientific reports · 2019Article
- Predicting neuroblastoma using developmental signals and a logic-based model.Biophysical chemistry · 2018Article
- Epigenetic silencing of PRSS3 provides growth and metastasis advantage for human hepatocellular carcinoma.Journal of molecular medicine (Berlin, Germany) · 2017Article
- Silencing Intersectin 1 Slows Orthotopic Neuroblastoma Growth in Mice.Journal of pediatric hematology/oncology · 2017Article
- Targeting P-selectin blocks neuroblastoma growth.Oncotarget · 2017Article
- Prediction of NSCLC recurrence from microarray data with GEP.IET systems biology · 2017Article
- Heparin-binding epidermal growth factor-like growth factor promotes neuroblastoma differentiation.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2017Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
14 authors.
Funding
Abstract
Currently, patients with neuroblastoma are classified into risk groups (e.g., according to the Children's Oncology Group risk-stratification) to guide physicians in the choice of the most appropriate therapy. Despite this careful stratification, the survival rate for patients with high-risk neuroblastoma remains <30%, and it is not possible to predict which of these high-risk patients will survive or succumb to the disease. Therefore, we have performed gene expression profiling using cDNA microarrays containing 42,578 clones and used artificial neural networks to develop an accurate predictor of survival for each individual patient with neuroblastoma. Using principal component analysis we found that neuroblastoma tumors exhibited inherent prognostic specific gene expression profiles. Subsequent artificial neural network-based prognosis prediction using expression levels of all 37,920 good-quality clones achieved 88% accuracy. Moreover, using an artificial neural network-based gene minimization strategy in a separate analysis we identified 19 genes, including 2 prognostic markers reported previously, MYCN and CD44, which correctly predicted outcome for 98% of these patients. In addition, these 19 predictor genes were able to additionally partition Children's Oncology Group-stratified high-risk patients into two subgroups according to their survival status (P = 0.0005). Our findings provide evidence of a gene expression signature that can predict prognosis independent of currently known risk factors and could assist physicians in the individual management of patients with high-risk neuroblastoma.
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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.