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NCT04215211

MR Based Survival Prediction of Glioma Patients Using Artificial Intelligence

Recruiting now Last updated 8 February 2021
What this trial tests

trial testing Survival prediction for glioma patients in Glioma in 2,500 participants. Currently enrolling.

Timeline
1 January 2017
Primary endpoint
1 January 2027
1 June 2027

Quick facts

Lead sponsorThe First Affiliated Hospital of Zhengzhou University
StatusRecruiting now
Study typeOBSERVATIONAL
Enrollment2,500
Start date1 January 2017
Primary completion1 January 2027
Estimated completion1 June 2027
Sites1 location across China

Drugs / interventions tested

Conditions studied

Sponsor

The First Affiliated Hospital of Zhengzhou University

Who can join

Adults 1 to 90, any sex, with Glioma. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

This registry aims to collect clinical, molecular and radiologic data including detailed survival data, clinical parameters, molecular pathology (1p/19q codeletion, MGMT methylation, IDH and TERTp mutations, etc) and conventional/advanced/new MR sequences (T1, T1c, T2, FLAIR, ADC, DTI, PWI, etc) of patients with primary gliomas. By leveraging artificial intelligence, this registry will seek to construct and refine algorithms that able to predict patients' survivals in the frame of molecular pathology or subgroups of gliomas.

Publications & conference data

5 peer-reviewed publications reference this trial (live from Europe PMC):

  1. Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities.
    Yan J, Zhao Y, Chen Y, Wang W, et al · · 2021 · cited 53× · PMID 34563923 · DOI 10.1016/j.ebiom.2021.103583
  2. Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future.
    Tangsrivimol JA, Schonfeld E, Zhang M, Veeravagu A, et al · · 2023 · cited 43× · PMID 37510174 · DOI 10.3390/diagnostics13142429
  3. Biological underpinnings of radiomic magnetic resonance imaging phenotypes for risk stratification in IDH wild-type glioblastoma.
    Guan F, Wang Z, Qiu Y, Guo Y, et al · · 2023 · cited 10× · PMID 37993907 · DOI 10.1186/s12967-023-04551-3
  4. Pre-operative imaging and post-operative appearance of standard paediatric neurosurgical approaches: a training guide for neuroradiologists.
    Ganau M, Magdum SA, Calisto A. · · 2021 · cited 4× · PMID 34012863 · DOI 10.21037/tp-20-484
  5. Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities.
    Duan W, Wang Z, Ma Z, Zheng H, et al · · 2024 · cited 3× · PMID 38279197 · DOI 10.1111/cas.16089

Verify or expand the search:

Other recruiting trials for Glioma

Currently open trials in the same condition.

Other The First Affiliated Hospital of Zhengzhou University trials

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Data sources for this page

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