Saturation transfer (CEST and MT) MRI for characterization of U‐87 MG glioma in the rat
Bibliographic record
Abstract
Abstract The focus of this work was to identify the optimal magnetic resonance imaging (MRI) contrast between orthotopic U‐87 MG tumours and normal appearing brain with the eventual goal of treatment response monitoring. U‐87 MG human glioblastoma cells were injected into the brain of RNU nude rats (n = 9). The rats were imaged at 7 T at three timepoints for all animals: 3–5, 7–9, and 11–13 days after implantation. Whole‐brain T1‐weighted (before and after gadolinium contrast agent injection), diffusion, and fluid‐attenuated inversion recovery scans were performed. In addition, single‐slice saturation‐transfer‐weighted chemical exchange saturation transfer (CEST), magnetization transfer (MT), and water saturation shift referencing (WASSR) contrast Z‐spectra and T1 and T2 maps were also acquired. The MT and WASSR 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 (RMT). The T1‐corrected apparent exchange‐dependent relaxation (AREX) metric to isolate the CEST contributions was also calculated. The lesion on M0, MT and AREX maps with a B1 of 2 μT best matched the hyperintensity on the post‐contrast T1‐weighted image. There was also good separation in Z‐spectra between the lesion and contralateral cortex in the 2‐μT CEST and 3‐ and 5‐μT MT Z‐spectra at all time points. A pairwise Wilcoxon signed‐rank tests with Holm–Bonferroni adjustment on MRI parameters was performed and the differences between enhancing lesion and contralateral cortex for the MT ratio with 2 μT saturation at 3.6 ppm frequency offset (corresponding to the amide chemical group) and M0, MT were both strongly significant (p < 0.001) at all time points. This work has identified that differences between enhancing lesion and contralateral cortex are strongest in MTR with B1 = 2 μT at 3.6 ppm and relative macromolecular pool size (M0, MT) images over entire period of 3–13 days after cancer cell implantation.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".