Scintigraphic imaging of focal hypoxic tissue: development and clinical applications of 123I-IAZA
Bibliographic record
Abstract
Affected tissues in a number of diseases, including cancer, stroke, cardiac infarction and diabetes, develop focal tissue hypoxia during their progression. The presence of hypoxic tissue may make the disease refractory to therapy, as in the case of solid tumor therapy using low LET ionizing radiation. In other pathologies, the detection of viable but hypoxic tissues may serve as a prodromal indicator of developing disease (e.g. diabetes),or as a prognostic indicator for management of the disease (e.g. stroke). Over the past two decades, a number of hypoxia radioimaging agents have been developed and tested clinically. Of these, 18F-Fmiso and 123I-IAZA are the most widely used radiotracers for PET and SPECT/planar imaging, respectively. IAZA and Fmiso are a 2-nitroimidazoles that chemically bind to subcellular components of viable hypoxic tissues. They sensitize hypoxic tumour to the killing effects of ionizing radiation via mechanisms that mimic the radiosensitizing effects of oxygen, and are therefore called oxygen mimetics. The oxygen mimetic effect is attributable in large part to the covalent binding of reductively-activated nitroimidazole intermediates to critical cellular macromolecules. Nitroimidazoles labelled with gamma-emitting radionuclides (e.g. 18F-Fmiso and 123I-IAZA) have been used as scintigraphic markers of tumour hypoxia, based on the need to identify radioresistant hypoxic tumour cells as part of the radiotherapy planning process. Broader interest in non-invasive, imaging-based identification of focal hypoxia in a number of diseases has extended hypoxia studies to include peripheral vascular disease associated with diabetes, rheumatoid arthritis, stroke, myocardial ischaemia, brain trauma and oxidative stress. In this review, the current status of hypoxia-selective studies with 123I-IAZA , an experimental diagnostic radiopharmaceutical, is reviewed with respect to its pre-clinical development and clinical applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".