Clinical trial · Observational
Spatial and Temporal Characterization of Gliomas Using Radiomic Analysis
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Glioma | Glioma | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Radiomic analysis of imaging - MRI, PET, CT | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (2)
- measure
- Autosegmentation of tumor
- timeFrame
- 3 years
- description
- The correlation of tumor region between manual segmentation and artificial intelligence-based autosegmentation model will be assessed using the Dice coefficient of similarity.
- measure
- Prognostication of gliomas
- timeFrame
- 3 years
- description
- Radiomic signature in prognostication of gliomas with estimation of progression-free survival and overall survival using Kaplan Meier plots and radiomics score-based nomograms.
Secondary outcomes (2)
- measure
- Response assessment in gliomas
- timeFrame
- 3 years
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 1 Year
Show eligibility criteria text
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
References
Publications (0)
Data not yet available