Radiotracers for Noninvasive Molecular Imaging of Tumor Cell Death
Bibliographic record
Abstract
The need to monitor cancer therapy-induced cellular and tissue changes using noninvasive imaging techniques continues to stimulate both basic and clinical research. Monitoring changes in cellular proliferative capacity that occur after treatment with radiation and/or chemotherapy has the potential to provide longitudinal information on the cellular dynamics of tumors before, during, and after therapeutic intervention. Cells can lose their reproductive potential through one of several mechanisms, including apoptosis and autophagy (which are forms of programmed cell death), premature senescence, or necrosis. When a tumor responds to therapy, current imaging methods do not provide information about the exact mechanism of cell death executed. We are now beginning to develop the molecular imaging tools that will enable us to noninvasively image cell death mechanisms both in experimental models and in the clinical cancer environment. Studies with these imaging tools will contribute to a better understanding of therapeutic responses and assist in the design and evaluation of more effective treatments. This review examines the state-of-the-art in the use of (radio)tracers for the purpose of imaging mechanisms of tumor cell inactivation (cell death) in animal models and in clinical trials.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".