Spatial and Temporal Characterization of Gliomas Using Radiomic Analysis (GLIO-RAD)
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ClinicalTrials.gov Identifier: NCT06036381 |
Recruitment Status :
Active, not recruiting
First Posted : September 13, 2023
Last Update Posted : April 2, 2024
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Condition or disease | Intervention/treatment |
---|---|
Glioma | Diagnostic Test: Radiomic analysis of imaging - MRI, PET, CT |
Study Type : | Observational |
Estimated Enrollment : | 1000 participants |
Observational Model: | Case-Only |
Time Perspective: | Retrospective |
Official Title: | Spatial and Temporal Characterization of Gliomas Using Radiomic Analysis |
Actual Study Start Date : | February 15, 2024 |
Estimated Primary Completion Date : | December 2026 |
Estimated Study Completion Date : | December 2026 |
- Diagnostic Test: Radiomic analysis of imaging - MRI, PET, CT
Radiomic analysis of imaging will be undertaken as a standard of care to develop computational algorithms for patients treated in our institution.
- Autosegmentation of tumor [ Time Frame: 3 years ]The correlation of tumor region between manual segmentation and artificial intelligence-based autosegmentation model will be assessed using the Dice coefficient of similarity.
- Prognostication of gliomas [ Time Frame: 3 years ]Radiomic signature in prognostication of gliomas with estimation of progression-free survival and overall survival using Kaplan Meier plots and radiomics score-based nomograms.
- Response assessment in gliomas [ Time Frame: 3 years ]Response assessment of gliomas using artificial intelligence model-based prediction and comparison with actual response (like radionecrosis, progression) using confusion matrices and estimation of parameters like sensitivity, specificity, accuracy, area under curve.
- Differentiation of glioma from non-glioma histology [ Time Frame: 3 years ]Use of radiomics model to differentiate gliomas from other brain tumors, with performance indices calculated using sensitivity, specificity, accuracy, area under curve.
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Ages Eligible for Study: | 1 Year and older (Child, Adult, Older Adult) |
Sexes Eligible for Study: | All |
Accepts Healthy Volunteers: | No |
Sampling Method: | Non-Probability Sample |
Inclusion Criteria:
- Patients with glioma or glioma-mimicking pathology with imaging available in TMC between January 2010 and December 2022.
Exclusion Criteria:
- 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
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT06036381
India | |
Tata Memorial Hospital | |
Mumbai, Maharashtra, India, 400012 |
Principal Investigator: | Dr. ARCHYA DASGUPTA, MD | Tata Memorial Hospital |
Responsible Party: | Dr Archya Dasgupta, Assistant Professor, Radiation Oncology, Tata Memorial Centre |
ClinicalTrials.gov Identifier: | NCT06036381 |
Other Study ID Numbers: |
4146 |
First Posted: | September 13, 2023 Key Record Dates |
Last Update Posted: | April 2, 2024 |
Last Verified: | April 2024 |
Individual Participant Data (IPD) Sharing Statement: | |
Plan to Share IPD: | No |
Studies a U.S. FDA-regulated Drug Product: | No |
Studies a U.S. FDA-regulated Device Product: | No |
Glioma Radiomics Artificial Intelligence Machine learning |
Glioma Neoplasms, Neuroepithelial Neuroectodermal Tumors Neoplasms, Germ Cell and Embryonal |
Neoplasms by Histologic Type Neoplasms Neoplasms, Glandular and Epithelial Neoplasms, Nerve Tissue |