Research Report on Comprehensive Genomic and Transcriptomic Characteristics of High-Grade Gastro-Entero-Pancreatic Neuroendocrine Tumors Research Background High-grade gastro-entero-pancreatic neuroendocrine neoplasms (HG GEP-NENs) are a heterogeneous group of malignant tumors characterized by neuroendocrine differentiation. According to WHO 2019 [...
A Large Prospective Cohort Study in the United States Reveals Potential Links Between Outdoor Air Pollution and Subtypes of Adult Hematologic Cancers Background and Purpose In recent years, the impact of outdoor air pollution on human health has garnered extensive attention. Since 2013, the International Agency for Research on Cancer (IARC) has cla...
Useful Features for Overall Survival Prediction in Low-Grade Glioma Patients Academic Background Glioma is a type of neoplastic growth in the brain that usually poses a serious threat to the patients’ lives. In most cases, glioma eventually leads to the death of the patient. The analysis of glioma typically involves examining pathological slices of...
Improved Segmentation of Pediatric Low-Grade Gliomas Through Multitask Learning Background Introduction The segmentation of pediatric brain tumors is a critical task in tumor volume analysis and artificial intelligence algorithms. However, this process is time-consuming and requires the expertise of neuroradiologists. Although significant research ...
“Prediction of Glioma Grades Based on Radiomic Features Inside and Outside Tumors Using Multiparametric MRI Images” Research Background Glioma is the most common primary brain tumor in the central nervous system, accounting for 80% of adult malignant brain tumors. In clinical practice, treatment decisions often require individualized adjustments ba...
Graph Convolutional Network Based on Self-Attention Similarity for Multi-type Low-Grade Glioma Classification 1. Research Background Low-grade glioma is a common malignant brain tumor caused by the cancerous transformation of glial cells in the brain and spinal cord. Gliomas are characterized by high incidence, high recurrence rate, high mortality ...
AI-Assisted Radiomics Algorithm for Glioma Grading Based on Slice Pooling Background Introduction Glioma is the most common and threatening tumor in the central nervous system, characterized by high incidence, high recurrence rates, high mortality, and low cure rates. The World Health Organization (WHO) classifies gliomas into four grades (I, II, I...
Glioma Disease Prediction Based on Optimized Integrated Machine Learning Background and Research Objectives In medical research, gliomas are the most common type of primary brain tumors, encompassing various cancer types with different clinical behaviors and treatment outcomes. Accurate prognosis prediction for glioma patients is crucial for optimi...
Review of Non-Invasive Glioma Grading Research: Lightweight Convolutional Neural Networks Based on Knowledge Distillation Background Gliomas are the main tumors of the central nervous system, and early detection is crucial. The World Health Organization (WHO) classifies gliomas from grade I to IV, with grades I and II being low-grade gliomas (LGG) ...
Application of Multi-modal Disentangled Variational Autoencoder and Game Theory Interpretability in Glioma Grading Background Gliomas are the most common primary brain tumors in the central nervous system. According to cellular activity and invasiveness, the World Health Organization (WHO) classifies them into grades I to IV, with grades I and II r...