Magnetic resonance imaging-guided linear accelerator arterial spin labelling reveals dynamics of highly perfused non-enhancing glioblastoma during radiotherapy
Bibliographic record
Abstract
Background and Purpose: Targeting tumour vasculature during radiotherapy is a direction of current investigation for radiotherapy strategies in glioblastoma. Arterial spin labelling (ASL), a perfusion imaging technique that uses arterial water as an endogenous tracer, on magnetic resonance imaging (MRI)-guided linear accelerators (MRI-linacs) could guide adaptation to changes in perfusion. However, since ASL is unavailable as a stock sequence on MRI-linac systems, our objective was to implement the first MRI-linac ASL sequence, characterize its performance, and measure tumour perfusion dynamics. Materials and Methods: Forty-seven glioblastoma patients were imaged using 3D pseudo-continuous ASL on a 1.5 T MRI-linac during treatment. ASL labeling efficiency was measured in two healthy volunteers and three patients on the MRI-linac and a 1.5 T MR-simulation scanner. ASL cerebral blood flow (CBF) values and repeatability were characterized. Regions of high tumour CBF were evaluated for overlap with the gross tumour volume (GTV) and temporal dynamics. Results: The labelling efficiency was lower for the MRI-linac compared to the MR-sim and literature consensus values (0.59 vs. 0.88 vs. 0.85). Corrected grey matter CBF was comparable between the MRI-linac and literature (38 ± 13 ml/100 g/min vs. 36.5 ± 8.2 ml/100 g/min, p = 0.41). The within-subject coefficient of variation was similar to literature values (16 % vs. 11 ± 5 %). Across patients, approximately half of the high-CBF region did not overlap the GTV (median 47 %). The high-CBF region tended to decrease in volume during radiotherapy, from - 24 % at week 3 (p = 0.016) to - 45 % (p = 0.044) by week 5 relative to week 1. Conclusion: MRI-linac ASL could allow targeting and adaptation for highly perfused tumour in future trials for glioblastoma.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".