Quantitative analysis of magnetic resonance images for characterization of blood-brain barrier dysfunction in dogs with brain tumors
Bibliographic record
Abstract
BACKGROUND: Blood-brain barrier (BBB) permeability can be assessed quantitatively using advanced imaging analysis. HYPOTHESIS/OBJECTIVES: Quantification and characterization of blood-brain barrier dysfunction (BBBD) patterns in dogs with brain tumors can provide useful information about tumor biology and assist in distinguishing between gliomas and meningiomas. ANIMALS: Seventy-eight hospitalized dogs with brain tumors and 12 control dogs without brain tumors. METHODS: In a 2-arm study, images from a prospective dynamic contrast-enhanced (DCE; n = 15) and a retrospective archived magnetic resonance imaging study (n = 63) were analyzed by DCE and subtraction enhancement analysis (SEA) to quantify BBB permeability in affected dogs relative to control dogs (n = 6 in each arm). For the SEA method, 2 ranges of postcontrast intensity differences, that is, high (HR) and low (LR), were evaluated as possible representations of 2 classes of BBB leakage. BBB score was calculated for each dog and was associated with clinical characteristics and tumor location and class. Permeability maps were generated, using the slope values (DCE) or intensity difference (SEA) of each voxel, and analyzed. RESULTS: Distinctive patterns and distributions of BBBD were identified for intra- and extra-axial tumors. At a cutoff of 0.1, LR/HR BBB score ratio yielded a sensitivity of 80% and specificity of 100% in differentiating gliomas from meningiomas. CONCLUSIONS AND CLINICAL IMPORTANCE: Blood-brain barrier dysfunction quantification using advanced imaging analyses has the potential to be used for assessment of brain tumor characteristics and behavior and, particularly, to help differentiating gliomas from meningiomas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".