The Role of Dynamic 11C-Acetate PET imaging in Early Detection of Response to Radiotherapy Treatment
Bibliographic record
Abstract
Positron Emission Tomography (PET) imaging with 11C-Acetate (ACE) is regularly used in cardiovascular and in cancer imaging. In the earlier stages of ACE developments, it has been mainly used for hepatocellular carcinoma, prostate cancer, and myocardial oxygen consumption. The previous studies compared the advantage of ACE with 18F-Fluorodeoxyglucose (18F-FDG) imaging using Standard Uptake Value (SUV) and the tissue-to-blood ratio (TBR) method. The current study proposes the application of dynamic ACE PET imaging in monitoring the early response to cancer treatment. We conducted two dynamic ACE PET scans on two patients suffering from Head and Neck Cancer (HNC) (Squamous Cell Carcinoma) in the base of the tongue. Pre-treatment dynamic ACE and static 18F-FDG PET were conducted before initiation of the treatment, and the second ACE dynamic scan was performed after four weeks of radiotherapy (after 35 Gy). We applied the two-tissue compartment model to represent the kinetics of ACE in HNC. 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. Compartmental modeling parameter k2 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, ACE 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".