Study of Tumor Perfusion with 11C-Acetate during Radiotherapy Treatment in Head and Neck Cancer
Bibliographic record
Abstract
Radiotherapy of head and neck cancer in adult humans is generally conducted with the same protocol in terms of radiation dose and number of treatments. The response to the treatment is known to depend on the individuals, and on the stage of the cancer at the start of the treatment. In the last decade, some authors suggested to estimate the tumor response to the treatment at certain time during the treatment, then to adjust the radiation dosage typically to hypoxic tumors. In the present work, we report the assessment of tumor perfusion with the 11C-Acetate radiotracer before and after 4 weeks of radiotherapy treatment. Four volunteers were recruited and imaged twice with a PET/CT scanner for head and neck cancer in dynamic mode for 30 min. A compartmental model was applied to the tumor time-activity curves (TACs). The tumors were first identified on the initial 11C-Acetate image. The images were decomposed in blood and tissue with the independent component analysis (ICA) technique. Since the tumors have different behavior in each patient, the values are reported individually with the rate constants and the influx rate constant. Also, the images show the shrinkage of the tumor after 4 weeks of treatment. Typically, the rate constants K1, k2 and k3 were found, for a single patient, before treatment: tumor: 0.0350, 0.3241, 0.2289; Ganglion: 0.0494, 0.5955, 0.3830, and at mid-treatment: Tumor: 0.7642, 0.2482, 0.0147; Ganglion: 0.6501, 0.2958, 0.0541. By calculating the influx rate constant Ki=K(1)*k(3)/(k(2)+k(3)), this gave a gain in perfusion of 2.95 and 5.2, respectively for the tumor and the ganglion. In conclusion, the assessment of the perfusion is more adequate to estimate tumor response to treatment, tumor hypoxia and necrosis.
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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".