MRI-BASED OXYGENATION IMAGING IN DIFFERENTIATING HIGH VS LOW- GRADE GLIOMA
Bibliographic record
Abstract
Abstract Background: Glioblastoma is the most common primary malignant neoplasm in adults, with a median survival of 15-18 months after diagnosis. Our group has previously demonstrated that quantitative blood oxygen level dependent (qBOLD) magnetic resonance imaging (MRI)-derived levels of oxygen saturation (SO2) can be used as a surrogate to map oxygen tension in patients with glioblastoma. We investigated whether qBOLD MRI was also able to differentiate different grades of gliomas. Patients and Methods: 10 patients were enrolled into this prospective study. All patients underwent a preoperative MRI with Ferumoxytol as a contrast agent. Two volumes of interest from the tumor were chosen for biopsy, from sites with different levels of hypoxia. These samples were stained with histological markers and graded by neuropathologists through consensus on a 0-3 scale. Patients with glioblastoma were compared with lower- grade gliomas. Scores were assessed for significant differences using Wilcoxon Two-Sample Test. Results: In total, 6 patients had pathological GBM; 1 patient had diffuse astrocytoma; and 3 patients had anaplastic astrocytoma. 1 patient a low-grade glioma had an inconclusive biopsy and was therefore excluded from the study. Although VOIs with different levels of SO2 were chosen, SO2 of VOIs did not differ, and histological markers were not significantly different within high-SO2 VOIs. However, within low-SO2 areas GBM showed significantly higher levels of CAIX (p=0.02), and nearly for VEGF (P=0.08). HIF1a staining did not differ (P=0.13). Conclusions: Advanced qBOLD MRI can potentially differentiate high-grade from low-grade glioma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".