Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT05656053 |
| Other study ID # |
MINGGE-SW-00002-V1-01 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
November 15, 2021 |
| Est. completion date |
September 2026 |
Study information
| Verified date |
March 2023 |
| Source |
Mingge LLC |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational [Patient Registry]
|
Clinical Trial Summary
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.
Description:
BACKGROUND:
The extent of glioma resection is directly related to patient survival, and a combination of
multiple imaging and molecular pathology imaging methods has been developed to achieve
maximum safe resection. In this study, three types of data, preoperative magnetic resonance
imaging, intraoperative ultrasound and molecular genotype, will be collected and combined to
build an artificial intelligence imaging model to achieve maximum safe resection and prolong
patient's life.
PLAN:
In order to achieve the goal of maximum safe resection, we plan to sequentially implement
imaging-based molecular visualization techniques, and integrated guidance techniques through
a combination of intraoperative ultrasound and preoperative magnetic resonance imaging, in
order to address the two critical scientific issues of glioma molecular boundary
visualization and intraoperative real-time molecular diagnosis. It can also help
neurosurgeons to achieve complete glioma resection at the molecular level, maximizing patient
survival time and providing another effective approach to improving glioma treatment.
PROCESS:
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 and intraoperative ultrasound to obtain magnetic resonance images,
ultrasound images, and ultrasound radio-frequency signals. After surgery, the patient's tumor
tissue samples will undergo specialist genetic testing to obtain multiple molecular
diagnostic results, such as isocitrate dehydrogenase (IDH), telomerase reverse transcriptase
promoter (TERTp), the short arm chromosome 1 and the long arm of chromosome 19 (1p/19q), et
al. Also, their imaging data, genotype data, clinical history data, and pathology data will
be used for the experimental study.
The data collected from each patient will be performed in three steps as follows.
1. Image translation and alignment of intraoperative ultrasound and preoperative MRI
navigation across modalities for glioma.
2. Multimodality imaging of IDH1/2 gene mutations from structural to molecular boundaries.
3. Applied study of molecular boundary visualization. All the above information will be
summarized and handed over to Fudan University to build an artificial intelligent model.
Compared with the previous gold standard glioma resection, this study adds intraoperative
ultrasound, intraoperative multi-point tumor specimen sampling for IDH genotype
identification during the surgery, and will collect relevant molecular imaging data, MRI
data, intraoperative ultrasound data, clinical case data and pathology data from patients
after the surgery. Intraoperative ultrasound is non-invasive, real-time and rapid, without
adding additional operative time or risk of infection.
