Spatial and Temporal Characterization of Gliomas Using Radiomic Analysis
GLIO-RAD
1 other identifier
observational
1,000
1 country
1
Brief Summary
Glioma are type of primary brain tumors arising within the substance of brain. Different type of gliomas are seen which are classified depending upon pathological examination and advanced molecular techniques, which help to determine the aggressiveness of the tumor and outcomes. Artificial intelligence uses advanced analytical process aided by computer which can be undertaken on the medical images. We plan to use artificial intelligence techniques to identify the abnormal areas within the brain representing tumor from the radiological images. Also, similar approach will be undertaken to classify gliomas with good or bad prognosis, to differentiate glioma from other type of brain tumors, and to detect response after treatment.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Feb 2024
Typical duration for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
August 28, 2023
CompletedFirst Posted
Study publicly available on registry
September 13, 2023
CompletedStudy Start
First participant enrolled
February 15, 2024
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 1, 2026
ExpectedStudy Completion
Last participant's last visit for all outcomes
December 1, 2026
April 9, 2025
April 1, 2025
2.8 years
August 28, 2023
April 8, 2025
Conditions
Keywords
Outcome Measures
Primary Outcomes (2)
Autosegmentation of tumor
The correlation of tumor region between manual segmentation and artificial intelligence-based autosegmentation model will be assessed using the Dice coefficient of similarity.
3 years
Prognostication of gliomas
Radiomic signature in prognostication of gliomas with estimation of progression-free survival and overall survival using Kaplan Meier plots and radiomics score-based nomograms.
3 years
Secondary Outcomes (2)
Response assessment in gliomas
3 years
Differentiation of glioma from non-glioma histology
3 years
Interventions
Radiomic analysis of imaging will be undertaken as a standard of care to develop computational algorithms for patients treated in our institution.
Eligibility Criteria
Study population will be according to the Inclusion and Exclusion Criteria . Study Includes vulnerable participants also. Minors (up to 18 years),Elderly
You may qualify if:
- Patients with glioma or glioma-mimicking pathology with imaging available in TMC between January 2010 and December 2022.
You may not qualify if:
- Imaging done outside TMC.
- Motion artifacts or other artifacts causing image degradation.
- Size of tumor or region of interest \< 1 cm in the largest dimension
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Tata Memorial Centrelead
- Indian Statistical Institute, Kolkatacollaborator
Study Sites (1)
Tata Memorial Hospital
Mumbai, Maharashtra, 400012, India
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Dr. ARCHYA DASGUPTA, MD
Tata Memorial Hospital
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- CASE ONLY
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Assistant Professor, Radiation Oncology
Study Record Dates
First Submitted
August 28, 2023
First Posted
September 13, 2023
Study Start
February 15, 2024
Primary Completion (Estimated)
December 1, 2026
Study Completion (Estimated)
December 1, 2026
Last Updated
April 9, 2025
Record last verified: 2025-04
Data Sharing
- IPD Sharing
- Will not share