Assessment of Cancer‐Associated Biomarkers by Positron Emission Tomography: Advances and Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 it