CLINICAL APPLICATION OF SATURATION TRANSFER MRI FOR DIFFERENTIATING TUMOUR PROGRESSION FROM RADIATION NECROSIS IN BRAIN METASTASES
Bibliographic record
Abstract
Abstract Stereotactic radiosurgery for the treatment of brain metastases delivers a high dose of radiation with excellent local control, but increases the likelihood of radiation necrosis. As shown in our previous work, saturation transfer MRI, consisting of quantitative magnetization transfer (qMT) and chemical exchange saturation transfer (CEST), is a promising technique for distinguishing radiation necrosis (RN) from tumour progression (TP) in brain metastases. A 3D qMT/CEST acquisition was recently implemented and over 100 patients have been scanned to date. The purpose of this work is to assess the ability of advanced MRI parameters, including qMT and CEST metrics, which are sensitive to macromolecules and metabolism. The specific metrics that were explored included the amide and NOE contributions of the magnetization transfer ratio (MTR), the MTR asymmetry, the apparent exchange-dependent relaxation (AREX), the qMT semi-solid pool fraction and the T1 and T2 relaxation times. For a subset of the patients, dynamic susceptibility contrast (DSC) perfusion images were acquired. Examples of confirmed tumour progression and radiation necrosis cases will be presented, comparing the structural images (pre- and post-contrast T1-weighted and FLAIR images) with parameter maps from qMT and CEST and also the relative cerebral blood flow (rCBF) from DSC perfusion imaging. Interim cohort results will be presented. Approaches for standardizing the parameters across multiple MRI vendors are also explored.
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 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) | 0.000 | 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".