AIMC Topic: Neuroblastoma

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HetEnc: a deep learning predictive model for multi-type biological dataset.

BMC genomics Aug 8, 2019
BACKGROUND: Researchers today are generating unprecedented amounts of biological data. One trend in current biological research is integrated analysis with multi-platform data. Effective integration of multi-platform data into the solution of a singl...
Humans Databases, Factual Unsupervised Machine Learning Neuroblastoma Transcriptome Deep Learning Computational Biology Models, Statistical
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Albumin binding, anticancer and antibacterial properties of synthesized zero valent iron nanoparticles.

International journal of nanomedicine Dec 28, 2018
BACKGROUND: Nanoparticles (NPs) have been emerging as potential players in modern medicine with clinical applications ranging from therapeutic purposes to antimicrobial agents. However, before applications in medical agents, some in vitro studies sho...
Tumor Cells, Cultured Staphylococcus aureus Metal Nanoparticles Pseudomonas aeruginosa Humans Iron Protein Conformation Antineoplastic Agents Anti-Bacterial Agents Bacteria Molecular Docking Simulation Neuroblastoma Cell Proliferation Serum Albumin, Human Escherichia coli
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Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma.

PloS one Dec 7, 2018
We introduce the CDRP (Concatenated Diagnostic-Relapse Prognostic) architecture for multi-task deep learning that incorporates a clinical algorithm, e.g., a risk stratification schema to improve prognostic profiling. We present the first application ...
Progression-Free Survival Neuroblastoma Child Gene Expression Regulation, Neoplastic Neoplasm Recurrence, Local Prognosis Female Male Risk Assessment Infant Gene Expression Profiling Deep Learning Humans Algorithms
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Change in liver, spleen and bone marrow magnetic resonance imaging signal intensity over time in children with solid abdominal tumors.

Pediatric radiology Dec 19, 2017
BACKGROUND: Reticuloendothelial system MRI signal hypointensity is common in pediatric oncology patients with solid abdominal tumors.
Hepatoblastoma Female Wilms Tumor Male Neuroblastoma Humans Magnetic Resonance Imaging Child, Preschool Abdominal Neoplasms Retrospective Studies Bone Marrow Liver Neoplasms Blood Component Transfusion Kidney Neoplasms Ganglioneuroblastoma Spleen
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Artificial neural network classifier predicts neuroblastoma patients' outcome.

BMC bioinformatics Nov 8, 2016
BACKGROUND: More than fifty percent of neuroblastoma (NB) patients with adverse prognosis do not benefit from treatment making the identification of new potential targets mandatory. Hypoxia is a condition of low oxygen tension, occurring in poorly va...
Kaplan-Meier Estimate Neuroblastoma Gene Expression Profiling Humans Oxygen Hypoxia Neural Networks, Computer Female Male Infant
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Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm.

BMC medical genomics Sep 10, 2015
BACKGROUND: Metastatic neuroblastoma (NB) occurs in pediatric patients as stage 4S or stage 4 and it is characterized by heterogeneous clinical behavior associated with diverse genotypes. Tumors of stage 4 contain several structural copy number aberr...
Neoplasm Metastasis Machine Learning Neuroblastoma Humans Genomic Instability Algorithms Models, Genetic Cell Transformation, Neoplastic
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Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Jul 1, 2025
PURPOSES: This study aimed to develop a computed tomography (CT)-based multi-organ segmentation model for delineating organs-at-risk (OARs) in pediatric upper abdominal tumors and evaluate its robustness across multiple datasets.
Deep Learning Adolescent Child, Preschool Child Organs at Risk Radiotherapy Planning, Computer-Assisted Kidney Neoplasms Tomography, X-Ray Computed Humans Female Neuroblastoma Male Abdominal Neoplasms Infant
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Scmaskgan: masked multi-scale CNN and attention-enhanced GAN for scRNA-seq dropout imputation.

BMC bioinformatics May 20, 2025
Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but dropout events, where gene expression is undetected in individual cells, present a significant challenge. We propose scMASKGAN, which transforms ma...
Humans RNA-Seq Single-Cell Gene Expression Analysis Sequence Analysis, RNA Neuroblastoma Neural Networks, Computer Software Algorithms Single-Cell Analysis
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Machine Learning Approaches for Neuroblastoma Risk Prediction and Stratification.

Critical reviews in oncogenesis Jan 1, 2025
Machine learning (ML) holds great promise in advancing risk prediction and stratification for neuroblastoma, a highly heterogeneous pediatric cancer. By utilizing large-scale biological and clinical data, ML models can detect complex patterns that tr...
Prognosis Risk Assessment Neuroblastoma Humans Machine Learning
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Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features.

Technology in cancer research & treatment Jan 1, 2024
PURPOSE: To predict bone marrow metastasis in neuroblastoma using contrast-enhanced computed tomography (CECT) radiomics features and explainable machine learning.
Humans Machine Learning Neuroblastoma Adolescent Child, Preschool Female Child Algorithms Radiomics ROC Curve Retrospective Studies Male Support Vector Machine Tomography, X-Ray Computed Infant Contrast Media Bone Marrow Bone Marrow Neoplasms
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