Assessment of Cancer‐Associated Biomarkers by Positron Emission Tomography: Advances and Challenges
Bibliographic record
Abstract
Positron emission tomography (PET) provides a powerful means to non-invasively image and quantify protein expression and biochemical changes in living subjects at nano- and picomolar levels. As the field of molecular imaging develops, and as advances in the biochemistry, pharmacology, therapeutics, and molecular biology of disease are made, there is a corresponding increase in the number of clinically relevant, novel disease-associated biomarkers that are brought to the attention of those developing imaging probes for PET. In addition, due to the high specificity of the PET radiotracers being developed, there is a demand for PET cameras with higher sensitivity and resolution. This manuscript reviews advances over the past five years in clinical and pre-clinical PET instrumentation and in new PET probes and imaging methods associated with the latest trends in the molecular imaging of cancer. Included in the PET tracer review is a description of new radioligands for steroid receptors, growth factor receptors, receptor tyrosine kinases, sigma receptors, tumor-associated enzymes, gene reporter probes, markers for tumor hypoxia and metabolism, and sites associated with angiogenesis and cellular proliferation. The use of PET imaging in drug development, including the monitoring of cancer chemotherapy, also is discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".