PET Metabolic Biomarkers for Cancer
Bibliographic record
Abstract
The body's main fuel sources are fats, carbohydrates (glucose), proteins, and ketone bodies. It is well known that an important hallmark of cancer cells is the overconsumption of glucose. Positron emission tomography (PET) imaging using the glucose analog (18)F-fluorodeoxyglucose ((18)F-FDG) has been a powerful cancer diagnostic tool for many decades. Apart from surgery, chemotherapy and radiotherapy represent the two main domains for cancer therapy, targeting tumor proliferation, cell division, and DNA replication-all processes that require a large amount of energy. Currently, in vivo clinical imaging of metabolism is performed almost exclusively using PET radiotracers that assess oxygen consumption and mechanisms of energy substrate consumption. This paper reviews the utility of PET imaging biomarkers for the detection of cancer proliferation, vascularization, metabolism, treatment response, and follow-up after radiation therapy, chemotherapy, and chemotherapy-related side effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".