Glioblastom WHO Grade 4, Glioma, Glioma (Diagnosis), IDH Wildtype Glioblastoma
Conditions
Keywords
Glioma, Brain Tumour, Glioma AI, MRI, Histological Glioblastoma, IDH Wildtype Glioblastoma
Brief summary
The goal of this observational study is to learn whether an artificial intelligence system called GliomaAI-GBM can help detect a specific molecular type of brain tumour called IDH wildtype glioblastoma using routine MRI scans. The study uses previously collected and fully anonymised MRI data from 1,372 patients from 13 institutions in the Cancer Imaging Archive (TCIA). The main questions it aims to answer are: * How accurately can GliomaAI-GBM identify IDH wildtype glioblastoma from MRI scans? * How well does the system perform across data from different hospitals and patient groups? Researchers will use existing MRI scans and clinical information to train and test the AI system. No new scans, treatments, or hospital visits are required for participants, and all data used is fully anonymised and obtained from an existing research database. Participants will not be asked to do anything, as this study only uses previously collected imaging data.
Interventions
DEVICEGliomaAI-GBM
Software as Medical Device (SaMD) for predicting histological glioblastoma
Sponsors
Deep Learning Institute of Radiological Sciences
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Adult (\>=18 years of age) * Having pre op MRI scan * Having biopsy / surgery * Having post biopsy/ surgery histology diagnosis and genetic analysis.
Exclusion criteria
* MRI scan significantly degraded by motion or other artefact * Incomplete genetic analysis * Prior treatment (e.g., radiotherapy or chemotherapy) before baseline MRI
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic performance of GliomaAI-GBM for identification of IDH wildtype glioblastoma from MRI, measured by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC. | Perioperative | The diagnostic performance of the GliomaAI-GBM artificial intelligence model will be assessed by comparing pre-operative MRI-based predictions of IDH wildtype glioblastoma status against post-operative (biopsy or surgery) molecular/genetic profiling results as the reference standard. Performance metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC will be calculated. |
Countries
India
Outcome results
None listed