Early Detection of Response to Radiotherapy Treatment with 11C-Acetate PET Imaging
Bibliographic record
Abstract
11C-Acetate radiotracer with Positron Emission Tomography (PET) imaging is currently used in cardiovascular imaging for perfusion and oxygen consumption measurement. It is also used, among other diseases, for prostate cancer as this radiotracer does not accumulate in the bladder. The present study reports the assessment of the radiotherapy treatment by measuring the tumor perfusion and oxygenation before and at mid-treatment by imaging with dynamic 11C-Acetate in patients with head and neck cancer. A pre-treatment dynamic 11C-Acetate and a clinical static 18F-FDG PET were conducted before initiation of the treatment, and the second 11C-Acetate dynamic scan was performed after four weeks of radiotherapy (i.e., after a dose of 35 Gy for a total of 70 Gy). The two-tissue compartment model was applied to 11C-Acetate images to extract the perfusion and oxygen consumption. The results showed a reduction in tumor volume by more than 50% compared to the initial volume in patient-1. Besides, patient-2 has displayed a more reduced tumor volume after 4 weeks of treatment. The 11C-Acetate rate constant k2 representing oxygen consumption increased after radiotherapy dose in both patients. This increase of k2 could reflect the reoxygenation process inside the tumor, and it can reflect the early treatment response. In conclusion, 11C-Acetate could predict the early changes in the tumor perfusion and the oxidative metabolism to optimally adjust the 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".