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...
Report on the Study of Fluorescence Molecular Tomography for Morphological Reconstruction of Glioma Based on Group Sparsity Priors 1. Academic Background and Research Motivation Fluorescence Molecular Tomography (FMT) is an important tool in life sciences that allows non-invasive real-time three-dimensional (3D) visualization of fluorescence source...
“IEEE Transactions on Biomedical Engineering” August 2022, Vol. 69, No. 8, First Clinical Study: Application of Near-Infrared Window IIA/IIB Fluorescence Imaging in Precise Glioma Resection Surgery Cao Caiguang, Jin Zeping, Shi Xiaojing, Zhang Zhe, Xiao Anqi, Yang Junying, Ji Nan, Tian Jie (IEEE Member), Hu Zhenhua (IEEE Senior Member) Introduction...
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...
Globally, the most common and deadly malignant brain tumor is glioblastoma (Glioblastoma, GBM). In recent years, research has continuously attempted to predict the overall survival time (OS) of GBM patients using machine learning techniques based on preoperative single-modality or multi-modality imaging phenotypes. Although these machine learning m...
Study of Mathematical Models for Tumor Growth – Exploring Glioma Extension Using Longitudinal Magnetic Resonance Imaging A recent article published in the IEEE Transactions on Biomedical Engineering presents a systematic study on the mathematical modeling and growth patterns of gliomas (glioma). This research was conducted by Birkan Tunç, David 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...
Near-Infrared Window II Fluorescence Imaging-Guided Surgery Prolongs Progression-Free Survival for High-Grade Glioma Patients Research Background High-grade glioma (HGG) is the most common malignant primary tumor in the central nervous system, with glioblastoma (GBM) having the worst prognosis. To improve the treatment outcomes for GBM patients, in...