“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 ...
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...
Research Report on Fully Automated Multimodal MRI Multi-task Learning for Glioma Segmentation and IDH Gene Typing Background of the Study Glioma is the most common primary brain tumor in the central nervous system. According to the World Health Organization (WHO) 2016 classification, gliomas are divided into low-grade gliomas (LGG, grades II and II...
Context-Aware Network Study Report for Glioma Segmentation Glioma is a common type of adult brain tumor that severely harms health and has a high mortality rate. To provide sufficient evidence for early diagnosis, surgical planning, and postoperative observation, multimodal Magnetic Resonance Imaging (MRI) has been widely applied in this field. The...
Enabling Explainable Artificial Intelligence for Glioma Prognosis: Translational Insights from Transparent Machine Learning Academic Background This study is dedicated to developing a reliable technique to detect whether patients have a specific type of brain tumor—glioma—using various machine learning methods and deep learning methods, combined wi...
Survival Analysis of Glioblastoma Patients: An Overview Empowered by Data Engineering Introduction Glioblastoma is a type of tumor that occurs in glial cells and accounts for 26.7% of all primary brain and central nervous system tumors. Survival analysis of glioblastoma patients is a key task in clinical management due to the heterogeneity of the t...
Personalized Prediction of Glioma Growth Using Bayesian Inference Introduction Glioblastoma is the most aggressive type of primary brain tumor, characterized by highly invasive tumor cells that spread to surrounding tissues. Conventional medical imaging techniques cannot precisely identify these diffuse tumor boundaries, leading to suboptimal clini...
Numerical Analysis of Rectangular Open-Channel PCF Biosensor Embedded with TiO2-Au-MXene for Brain Tumor Diagnosis Academic Background and Problem Statement In recent years, the development of cost-effective and highly reliable biosensors has become a research hotspot. These sensors aim to detect minute concentrations of analytes and cover a wide a...
Comparison of Acoustic Patterns and Microbubble Delivery Strategies in Large-Volume Drug Delivery Mediated by Focused Ultrasound for Blood-Brain Barrier Opening Background Diffuse Intrinsic Pontine Glioma (DIPG) is the most common and lethal brainstem tumor in children. Due to the usually intact blood-brain barrier (BBB) in DIPG, drug penetration i...