ClinicalTrials.gov
public full text
Public Domain
Intraoperative Rapid Diagnosis of Glioma Based on Fusion of Magnetic Resonance and Ultrasound Imaging
The aim of this observational study is to enable rapid diagnosis of molecular biomarkers in patients during surgery by medical imaging and artificial intelligence models, to help clinicians with strategies to maximize safe resection of gliomas. The main questions it aims to answer are: 1. To solve the current clinical shortcomings of intraoperative molecular diagnosis, which is time-consuming and complex, and enables rapid and automated molecular diagnosis of glioma, thus providing the possibility of personalized tumor resection plans. 2. To implement a neuro-navigation platform that combines preoperative magnetic resonance images, intraoperative ultrasound signals and intraoperative ultrasound images to address real-time molecular boundary visualisation and molecular diagnosis for glioma, providing an approach to improve glioma treatment. Participants will read an informed consent agreement before surgery and voluntarily decide whether or not to join the experimental group. they will undergo preoperative magnetic resonance imaging, intraoperative ultrasound, and postoperative genotype identification. Their imaging data, genotype data, clinical history data, and pathology data will be used for the experimental study. The data collection process will not interrupt the normal surgical process.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A deep learning based NeuroFusionNet approach for automated brain tumor diagnosis from MRI.#150302
- Integration of Artificial Intelligence, Machine Learning, and Deep Learning into Clinically Routine Molecular Imaging#10763
- Digitised histopathology slides now ready for artificial intelligence: predicting the molecular signatures of gliomas.#174809
- Hierarchical Diagnosis for Adult Diffuse Glioma Based on Deep Learning#1125468
- Molecular alterations prediction in gliomas via an interpretable deep learning model: a multicentre and retrospective study.#174810
Record · ID 900510
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive