Multi-Year, Multi-Disciplinary Efforts in Scientific Research Data Management Education Research Background With the development of modern neuroscience, Research Data Management (RDM) has become an indispensable skill for scientists. However, despite the importance of research data management for scientific research, such technical skills are often...

In contemporary clinical oncology practice and drug development, the methods for evaluating tumor response are on the cusp of a revolution. Since the World Health Organization (WHO) proposed tumor response classification criteria for assessing the effectiveness of anti-cancer drugs in 1981, this field has undergone several improvements. Notably, th...

Study on the Role of Machine Learning-Assisted Quantitative Hyperspectral Imaging in Brain Tumor Resection Background Introduction Complete resection of malignant gliomas has always been challenged by the difficulty of distinguishing tumor cells in invasive regions. The background of this study is: In neurosurgery, the application of 5-aminolevulin...

Breakthrough in Automated Echocardiogram Diagnosis via Deep Learning: A Comparative Study of Self-Supervised Learning Methods Research Background With the rapid development of artificial intelligence and machine learning technologies, their role in medical imaging diagnosis is becoming increasingly significant. In particular, Self-Supervised Learni...

In the global context, the prevalence of obesity is on the rise, bringing significant impacts to public health. Obesity is independently associated with the incidence and mortality of cardiovascular diseases, with an estimated economic burden exceeding $200 billion annually for healthcare systems. In recent years, glucagon-like peptide-1 (GLP-1) re...

Characterization of Heterogeneity in Muscle Fiber Types and Selective Susceptibility in Inclusion Body Myositis With advancing age, the incidence of inflammatory myopathies gradually increases, among which inclusion body myositis (IBM) is the most common type, currently lacking effective treatment methods. Unlike other inflammatory myopathies, IBM ...

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...

“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 ...

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...