Copper-64 based radiopharmaceuticals for brain tumors and hypoxia imaging
Bibliographic record
Abstract
INTRODUCTION: The most common and aggressive primary malignancy of the central nervous system is Glioblastoma that, as a wide range of malignant solid tumor, is characterized by extensive hypoxic regions. A great number of PET radiopharmaceuticals have been developed for the identification of hypoxia in solid tumors, among these, we find copper-based tracers. The aim of the current review paper was to provide an overview of radiocopper compounds applied for preclinical and clinical research in brain tumors and hypoxia imaging or therapy.EVIDENCE ACQUISITION: Copper offers a wide variety of isotopes, useful for nuclear medicine applications, but only 64Cu and 67Cu are under the spotlight of the scientific community since being good candidates for theranostic applications. Between the two, 64Cu availability and production cost have attracted more interest of the scientific community.EVIDENCE SYNTHESIS: In order to better understand the application of copper-bis thiosemicarbazones in hypoxia imaging, an overview of the role of hypoxia in cancer, existing non-imaging and imaging techniques for hypoxia identification and promising future avenues regarding hypoxia is necessary. Different proposed uptake mechanisms of [64Cu][Cu(ATSM)] inside the cell will be discussed and other 64Cu-based tracers for brain tumors described.CONCLUSIONS: Among radio copper compounds [64Cu][Cu(ATSM)] is the most studied radiopharmaceutical for imaging and treatment of brain tumors. Experimental evidence suggested that [64Cu][Cu(ATSM)] could be more appropriately considered as a marker of over-reduced intracellular state rather than a pure hypoxia agent. Moreover, preliminary clinical data suggested that [64Cu]CuCl2 can be a potentially useful diagnostic agent for malignancies of the central nervous system (CNS).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".