Active clinical trials for "Glioma"

The purpose of this study is to assess the safety, biodistribution, dosimetric properties of the positron emission tomography (PET) radiopharmaceutical agent 68Ga-NOTA-NFB. To preliminarily evaluate its application in glioma diagnosis. To assess the application of 68Ga-NOTA-NFB PET/CT in the evaluation of the neoadjuvant chemotherapy in patients with breast cancer before and after the therapy.

Malignant gliomas are aggressive tumours with poor prognosis despite the current multimodal treatment. Hence, there is a clear need for new, effective therapies, among which immune therapy has emerged as a promising treatment option. When interpreting follow-up magnetic resonance (MR) examinations, the radiologist is often confronted with images that are difficult to interpret with the conventional anatomical imaging techniques. The difference between tumour relapse and therapy-mediated changes is not always distinctive. In this project, the investigators attempt to characterize the inflammatory response with parameters from advanced MRI techniques like MR spectroscopy, MR perfusion imaging and MR-diffusion imaging. These techniques allow characterization of cellular properties like metabolism and tissue structure respectively. Doing so, the investigators will monitor disease evolution in order to timely detect treatment failure, thereby allowing appropriate switch in patient management.

The aim of the study is to compare the two imaging modalities perfusion weighted MR-imaging and FET-PET in their ability to provide an accurate histological evaluation of low grade glioma and to reveal focal abnormalities within a homogeneously appearing tumor. Additionally, therapeutic effects should be assessed during a time period of two years.

The purpose of this study is to see if a specialized imaging technique using MRI called multi-exponential T2 component analysis can reliably differentiate between normal brain and brain tumour.

The purpose of this study is to determine whether Q cells separated from the glioma sample are determinants in treatment response and prognosis of glioma patients

To evaluate the efficacy of multi-modality magnetic resonance quantitative parameters in evaluating the treatment effects of high-grade gliomas, and to provide new biomarkers for the establishment of new diagnostic criteria for the identification of true and pseudoprogression of high-grade gliomas.

This project intends to use multiple types of biological samples from glioma patients and mouse intracranial tumor models as research objects, and comprehensively apply a series of omics sequencing technologies and molecular biology technologies to jointly define the following research objectives :

Research purposes: To obtain the metabolic characteristics of glioma molecular imaging through a multimodal image recognition system. To determine whether molecular imaging metabolic parameters can characterize the molecular typing of glioma by analyzing the relationship between metabolic parameters and tumor subtypes To get metabolic classification based on metabolic parameters of glioma molecular imaging, and to identify the relationship between metabolic subtypes and surgical resection, radiotherapy and chemotherapy, and prognosis and further refine the molecular classification of glioma. Research Background: Glioma is the most common primary intracranial malignant tumor, accounting for 80% of central nervous system malignant tumors. It is highly invasive, with a surgical recurrence rate of up to 90%. The prognosis is extremely poor, which has caused a great burden. There are different molecular subtypes of glioma with distinct molecular biological characteristics, resulting in various prognosis of patients. With the continuous development of basic and clinical research of glioma and the advent of various new drugs and treatment technologies, molecular pathological diagnosis based on the individual level of glioma patients is particularly important. Clarifying the molecular pathology type before surgery will help the clinical diagnosis and prognostic judgment of glioma, and is of great significance for the optimization of treatment options. Based on the establishment of glioma molecular typing system, the project team use noninvasive molecular imaging technology to clarify the characteristics of molecular subsets of glioma based on the tumor metabolic parameters. Through combining deep learning-based target detection and image recognition with big data analysis, it has great potential in the clinical research of glioma diagnosis, prognosis and treatment options, which could provide a scientific basis for the establishment and promotion of glioma molecular analysis and recognition system.

This study aimed to analyze the connectivity alterations in brain networks of LGG patients with epilepsy who take levetiracetam at short-term preoperatively.

The key molecular changes in the progression of glioma are closely related to tumor heterogeneity, pathological grade, precision treatment and prognosis of glioma. At present, a visually quantitative assessment criteria about the key molecular typing of glioma is still absent. Based on the previous research, this project intends to establish a multi-dimensional database of glioma from clinical, radiomics and microomics levels. The investigators aim to filter out the specific molecular markers in the progression of glioma and explore the intrinsic connection of radiomics features and microomics molecular markers by using bioinformatics integration analysis and artificial intelligence multiple kernel learning. Thus, the investigators could determine the specific molecular mechanism in the progression of glioma, and establish a visually quantitative assessment system of pre-operative precisive grading, molecular typing discrimination and prognosis prediction. The completion of this project is of great significance for improving molecular diagnostic level of glioma, guiding individualized diagnosis and treatment decisions, and improving the survival rate of patients.