Nuclear Overhauser Enhancement-Mediated Magnetization Transfer Imaging in Glioma with Different Progression at 7 T
Bibliographic record
Abstract
Glioma is a malignant neoplasm affecting the central nervous system. The conventional approaches to diagnosis, such as T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI, give an oversimplified representation of anatomic structures. Nuclear Overhauser enhancement (NOE) imaging is a special form of magnetization transfer (MT) that provides a new way to detect small solute pools through indirect measurement of attenuated water signals, and makes it possible to probe semisolid macromolecular protons. In this study, we investigated the correlation between the effect of NOE-mediated imaging and progression of glioma in a rat tumor model. We found that the NOE signal decreased in tumor region, and signal of tumor center and peritumoral normal tissue markedly decreased with growth of the glioma. At the same time, NOE signal in contralateral normal tissue dropped relatively late (at about day 16-20 after implanting the glioma cells). NOE imaging is a new contrast method that may provide helpful insights into the pathophysiology of glioma with regard to mobile proteins, lipids, and other metabolites. Further, NOE images differentiate normal brain tissue from glioma tissue at a molecular level. Our study indicates that NOE-mediated imaging is a new and promising approach for estimation of tumor progression.
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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 | 0.000 | 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 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".