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...
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...
St. Jude Survivorship Portal: Analysis and Sharing of Large-Scale Clinical and Genomic Data of Pediatric Cancer Survivors Research Background In the United States, the five-year survival rate for childhood cancer has increased from about 60% in the 1970s to over 85% today. Despite the significant improvement in survival rates, these childhood cance...
Introduction In the process of healthy hematopoiesis, a group of hematopoietic stem cells (HSC) contribute to all blood-related lineages. However, as we age, this process often leads to clonal hematopoiesis (CH), meaning the expansion of clones originating from a particular HSC, which then occupies a significant portion of blood cells and platelets...
Research Background: Suicide is a global public health challenge, but there is still significant uncertainty regarding the relationship between behavioral and physiological factors and suicide attempts (SA). Previous studies often focus on limited hypothesized factors such as mental illnesses (e.g., depression), personality and psychological traits...
Single-Sample Tumor Subclonal Reconstruction Algorithm Based on Crowd-Sourced Resources Background The evolution of cancer and the genetic heterogeneity of tumors are critical fields in modern oncology research. Tumors evolve from normal cells through progressive acquisition of somatic mutations. These mutations occur probabilistically, influenced ...