Vessel size index measurements in a rat model of glioma: comparison of the dynamic (Gd) and steady‐state (iron‐oxide) susceptibility contrast MRI approaches
Bibliographic record
Abstract
Vessel size index (VSI), a parameter related to the distribution of vessel diameters, may be estimated using two MRI approaches: (i) dynamic susceptibility contrast (DSC) MRI following the injection of a bolus of Gd-chelate. This technique is routinely applied in the clinic to assess intracranial tissue perfusion in patients; (ii) steady-state susceptibility contrast with USPIO contrast agents, which is considered here as the standard method. Such agents are not available for human yet and the steady-state approach is currently limited to animal studies. The aim is to compare VSI estimates obtained with these two approaches on rats bearing C6 glioma (n = 7). In a first session, VSI was estimated from two consecutive injections of Gd-Chelate (Gd(1) and Gd(2)). In a second session (4 hours later), VSI was estimated using USPIO. Our findings indicate that both approaches yield comparable VSI estimates both in contralateral (VSI{USPIO} = 7.5 ± 2.0 µm, VSI{Gd(1)} = 6.5 ± 0.7 µm) and in brain tumour tissues (VSI{USPIO} = 19.4 ± 7.1 µm, VSI{Gd(1)} = 16.6 ± 4.5 µm). We also observed that, in the presence of BBB leakage (as it occurs typically in brain tumours), applying a preload of Gd-chelate improves the VSI estimate with the DSC approach both in contralateral (VSI{Gd(2)} = 7.1 ± 0.4 µm) and in brain tumour tissues (VSI{Gd(2)} = 18.5 ± 4.3 µm) but is not mandatory. VSI estimates do not appear to be sensitive to T(1) changes related to Gd extravasation. These results suggest that robust VSI estimates may be obtained in patients at 3 T or higher magnetic fields with the DSC approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".