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Neuroblastoma

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A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges.

Pediatric radiology
Neuroblastoma is an extremely heterogeneous tumor that commonly occurs in children. The diagnosis and treatment of this tumor pose considerable challenges due to its varied clinical presentations and intricate genetic aberrations. Presently, various ...

Prediction of MYCN Gene Amplification in Pediatric Neuroblastomas: Development of a Deep Learning-Based Tool for Automatic Tumor Segmentation and Comparative Analysis of Computed Tomography-Based Radiomics Features Harmonization.

Journal of computer assisted tomography
OBJECTIVE: MYCN oncogene amplification is closely linked to high-grade neuroblastoma with poor prognosis. Accurate quantification is essential for risk assessment, which guides clinical decision making and disease management. This study proposes an e...

Successful all robotic-assisted excision of highly malignant mediastinal neuroblastoma in a toddler: A case report.

Asian journal of endoscopic surgery
An otherwise well 28-month-old girl presented with fever/left thigh pain. Computed tomography identified a 7 cm right posterior mediastinal tumor extending to the paravertebral and intercostal spaces with multiple bone and bone marrow metastases on b...

Albumin binding, anticancer and antibacterial properties of synthesized zero valent iron nanoparticles.

International journal of nanomedicine
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...

Machine Learning Approaches for Neuroblastoma Risk Prediction and Stratification.

Critical reviews in oncogenesis
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...

Differentiating a pachychoroid and healthy choroid using an unsupervised machine learning approach.

Scientific reports
The purpose of this study was to introduce a new machine learning approach for differentiation of a pachychoroid from a healthy choroid based on enhanced depth-optical coherence tomography (EDI-OCT) imaging. This study included EDI-OCT images of 103 ...

Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma.

Journal of digital imaging
Neuroblastoma is one of the most common pediatric cancers. This study used machine learning (ML) to predict the mortality and a few other investigated intermediate outcomes of neuroblastoma patients non-invasively from CT images. Performances of mult...