Combing a Deep Learning-Based Radiomics With Liquid Biopsy for Preoperative and Non-invasive Diagnosis of Glioma
NCT05536024 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 500
Last updated 2022-09-10
Summary
This registry has the following objectives. First, according to the guidance of 2021 WHO of CNS classification, we constructed and externally tested a multi-task DL model for simultaneous diagnosis of tumor segmentation, glioma classification and more extensive molecular subtype, including IDH mutation, ATRX deletion status, 1p19q co-deletion, TERT gene mutation status, etc. Second, based on the same ultimate purpose of liquid biopsy and radiomics, we innovatively put forward the concept and idea of combining radiomics and liquid biopsy technology to improve the diagnosis of glioma. And through our study, it will provide some clinical validation for this concept, hoping to supply some new ideas for subsequent research and supporting clinical decision-making.
Conditions
- Glioma (Diagnosis)
- Liquid Biopsy
- Deep Learning
Interventions
- DIAGNOSTIC_TEST
-
Prediction of glioma grading and molecular subtype
Prediction of WHO grading(II/III/IV), IDH gene mutation status, ATRX deletion status, 1p/19q deletion status, CDKN2A/B homozygous deletion status, TERT gene mutation status, epidermal growth factor receptor (EGFR) mutation status, chromosome 7gain and chromosome 10 less status, H3F3A G34 (H3.3 G34) mutation status, H3 K27M mutation status
Sponsors & Collaborators
-
Renmin Hospital of Wuhan University
collaborator OTHER -
Wuhan University
collaborator OTHER -
Second Affiliated Hospital of Nanchang University
lead OTHER
Principal Investigators
-
Xingen Zhu, Prof · Second Affiliated Hospital of Nanchang University
-
Qianxue Chen · Renmin Hospital of Wuhan University
Eligibility
- Min Age
- 18 Years
- Sex
- ALL
- Healthy Volunteers
- Yes
Timeline & Regulatory
- Start
- 2022-05-01
- Primary Completion
- 2023-05-01
- Completion
- 2023-08-30
Countries
- China
Study Locations
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