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...

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. ...

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...