P09.15.A SATURATION TRANSFER MRI ENABLES EARLY DETECTION OF CANCER LESIONS IN THE CNS
Bibliographic record
Abstract
Abstract BACKGROUND Chemical exchange saturation transfer (CEST) MRI provides chemical-group-specificity (e.g., amide, guanidinium, and aliphatic) to exchangeable protons in dissolved proteins. Magnetization transfer (MT) MRI is sensitive to water bound to macromolecules, e.g., myelin and collagen. The major objective of the study was to identify the chemical exchange saturation transfer- and magnetization transfer-derived magnetic resonance images that have high contrast between orthotopic U87 tumours and normal-appearing brain. MATERIAL AND METHODS U-87 MG glioblastoma cell line-derived xenografts were implanted in RNU nude rats (n=9). Animals were divided into two groups based on the scanning schedule. The rats were imaged using 7 tesla scanner (PharmaScan; Bruker BioSpin) at three time points for all animals: 3-5, 7-9, and 11-13 days after implantation. Single-slice saturation-transfer-weighted CEST (saturation amplitude: 0.5 and 2 µT) and MT (3 and 5 µT) contrast Z-spectra and T1 and T2 maps were acquired. The MT Z-spectra and T1 map were fitted to a two-pool quantitative MT model to estimate the T2 of the free and macromolecular-bound water molecules, the relative macromolecular pool size (M0, MT), and the magnetization exchange rate from the macromolecular pool to the free pool. RESULTS Statistically significant differences, by pairwise Wilcoxon signed-rank tests with Holm-Bonferroni adjustment, were found. The differences between enhancing lesion and contralateral cortex for the magnetization transfer ratio with 2 µT saturation at 3.6 ppm frequency offset (corresponding to the amide chemical group) and M0, MT are both strongly significant (p < 0.001) at all time points. CONCLUSION MT and CEST are a promising method of identifying the earliest time points of glioblastoma development. Such an early image biomarker of neoplasia may allow for improved treatment outcomes leading to reduced recurrence events.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".