Chemical exchange saturation transfer MRI in central nervous system tumours on a 1.5 T MR-Linac
Bibliographic record
Abstract
Purpose To describe the implementation and initial results of using Chemical Exchange Saturation Transfer (CEST) for monitoring patients with central nervous system (CNS) tumours treated using a 1.5 tesla MR-guided radiotherapy system. Methods CNS patients were treated with up to 30 fractions (total dose up to 60 Gy) using a 1.5 T Elekta Unity MR-Linac. CEST scans were obtained in 54 subjects at one or more time points during treatment. CEST metrics, including the amide magnetization transfer ratio (MTR Amide ), nuclear Overhauser effect (NOE) MTR (MTR NOE ) and asymmetry, were quantified in phantoms and CNS patients. The signal was investigated between tumour and white matter, across time, and across disease categories including high- and low-grade tumours. Results The gross tumour volume (GTV) exhibited lower MTR Amide and MTR NOE and higher asymmetry compared to contralateral normal appearing white matter. Signal changes in the GTV during fractionated radiotherapy were observed. There were differences between high- and low-grade tumours, with higher CEST asymmetry associated with higher grade disease. Conclusion CEST MRI using a 1.5 T MR-Linac was demonstrated to be feasible for in vivo imaging of CNS tumours. CEST images showed tumour/white-matter contrast, temporal CEST signal changes, and associations with tumour grade. These results show promise for the eventual goal of using metabolic imaging to inform the design of adaptive radiotherapy protocols.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".