Health TrialsbyHealthPanel

Spatial and Temporal Characterization of Gliomas Using Radiomic Analysis

ConditionGlioma
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age1+
SponsorTata Memorial Centre

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Patients with glioma or glioma-mimicking pathology with imaging available in TMC between January 2010 and December 2022.

Disqualifiers

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

Trial population

Study population will be according to the Inclusion and Exclusion Criteria . Study Includes vulnerable participants also. Minors (up to 18 years),Elderly

Trial design

Design model

Case-only

Time perspective

Retrospective

Treatments tested in this trial

  • Radiomic analysis of imaging - MRI, PET, CT

    Diagnostic test

    Radiomic analysis of imaging will be undertaken as a standard of care to develop computational algorithms for patients treated in our institution.

Treatment groups

No treatment groups listed

Trial outcomes

Primary outcomes

1

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.

Time frame
3 years
2

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.

Time frame
3 years

Secondary outcomes

1

Response assessment in gliomas

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.

Time frame
3 years
2

Differentiation of glioma from non-glioma histology

Use of radiomics model to differentiate gliomas from other brain tumors, with performance indices calculated using sensitivity, specificity, accuracy, area under curve.

Time frame
3 years

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Tata Memorial Centre

Lead sponsor

Indian Statistical Institute, Kolkata

Collaborator

Source ClinicalTrials.gov
Apply now