NI-08 * OPTIMIZATION OF MOLECULAR MR IMAGING OF GLIOMA
Bibliographic record
Abstract
BACKGROUND: MRI has been widely recognized as a diagnostic tool for early cancer detection, treatment monitoring and image guided surgery. Of particular interest is imaging of high-grade gliomas due to their rapid growth and very poor prognosis with a median survival rate of only 9 months. Standard contrast enhanced MRI, not provide sufficiently high specificity for tumor diagnosis and thus require targeted contrast agents which can be applied to provide information on tumor status. Therefore we applied targeted contrast agents based on iron oxide, that shorten mostly T2 relaxation time. The aim of this study was to optimize contrast-to-noise ratio (CNR) using spin-echo (SE), gradient echo (GE) and GE with flow compensation (GEFC) pulse sequences in T2 contrast-enhanced molecular MRI of glioma. METHODS: A mouse model of glioma and 9.4T MRI were used. MR imaging was performed. The CNR was measured prior, 20 min, 2 hrs and 24 hrs post intravenous tail administration of the glioma targeted paramagnetic nanoparticles (NPs) using spin-echo (SE), gradeint-ech (GE), gradient-echo flow compensation (GEFC) pulse sequences. Susceptibility weighted images (SWI) based on GEFC were also obtained for comparison. RESULTS: The results showed significant differences in CNR among all pulse sequences prior injection. GEFC provided higher CNR post contrast agent injection when compared to GE and SE. Post injection CNR was the highest with SWI and significantly different from any other pulse sequence. The optimum CNR was found to be TE = 7 ms for GE and GEFC pulse sequences and TE = 60 ms for SE. CONCLUSION: The study showed, that molecular MR imaging using targeted contrast agents can enhance the detection of glioma cells at 9.4T if the optimal pulse sequence is used. Hence, the use of flow compensated pulse sequences, beside SWI, should to be considered in the molecular imaging studies.
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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) | 0.002 | 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".