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Establishment and Evaluation of Multimodal Image Recognition System of Glioma Based on Deep Learning

NCT04407039 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 350

Last updated 2021-09-09

No results posted yet for this study

Summary

Research purposes:

1. To obtain the metabolic characteristics of glioma molecular imaging through a multimodal image recognition system.
2. To determine whether molecular imaging metabolic parameters can characterize the molecular typing of glioma by analyzing the relationship between metabolic parameters and tumor subtypes
3. 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.

Conditions

Interventions

DEVICE

PET/CT, H-MRS and MRI

Use imaging methods to get metabolic parameters.

Sponsors & Collaborators

  • Tao Xin

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
70 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-12-30
Primary Completion
2022-08-30
Completion
2022-12-30
FDA Device
Yes

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Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04407039 on ClinicalTrials.gov