Components Determination in Hypoxic Glioblastoma Measured with 18F-FMISO PET imaging
Bibliographic record
Abstract
Tumor hypoxia is defined as the reduction of oxygen supply to tumor cells. Glioblastoma (GBM) tumors are characterized by high levels of hypoxia, which results in poor prognosis even after treatment by radiotherapy and chemotherapy. The most current technique used for imaging hypoxia in GBM is 18F-Fluoromisonidazole (18F-FMISO) with PET imaging. The aim of this study is to decompose the dynamic 18F-FMISO images in tissue components and to study the spatial distribution of GBM in the tumor over time. The scanning protocol begins with 15 or 30 minutes dynamic images of the brain followed by static images at 2 h, 3 h and 4 h. We used spectral analysis technique to decompose the whole 3D dynamic image into its components in order to isolate the hypoxic regions. The results show non-uniformity in tumor shape as a function of time. The tumor becomes uniform after 2 h resembling its shape on MRI image, even though, a difference in shape has been found with later frames. Some tumor regions show accumulative response while others, even appearing with comparable uptake of 18F-FMISO, they were not observed as hypoxic based on the results of spectral analysis. Clearly, blood volume, hypoxic and perfused tissues and even necrotic regions can be isolated and studied separately. Therefore, the pattern of changes in tumor shape over time can be explained for a better tumor treatment.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".