Measuring hypoxia in chronic limb-threatening ischemia using 18F-FAZA kinetic modelling – a pilot study
Bibliographic record
Abstract
Chronic limb- threatening ischemia (CLTI) is a serious condition that can lead to amputation, and in some cases, it can be associated with mortality. Current clinical evaluation methods have several limitations. Therefore, new methods to assess CLTI are needed to better understand and measure underlying causes and functionality, and hence potentially improve the treatment. In this study, we use dynamic 18F-FAZA PET-imaging as a method of measuring hypoxia as a marker associated with CLTI, on twelve patients identified with CLTI who underwent 18F-FAZA PET-MR imaging. The kinetic modelling goodness-of-fit metrics using AIF from independent limb with the irreversible-2TC3K model distinguished between index and contralateral limbs better than the reversable-2TC4K model. The Spearman correlation coefficients between the standardized uptake value (SUV) SUV-to-SUVmed ratio and the perfusion parameter, $$\:{K}_{1}$$ , was rs = -0.07 for index and rs = 0.22 for contralateral limbs. For the SUV-to-SUVmed ratio correlation with diffusion parameter, $$\:{\:k}_{3}$$ , it is found to be negative for both index (rs = -0.16) and contralateral (rs = -0.11). The kinetic modelling of 18F-FAZA dynamic PET-MR was able to differentiate between index and contralateral limbs in CLTI patients, and the diffusion metric from the kinetic modelling can potentially be used as a metric to measure hypoxia in CLTI. ClinicalTrials.gov, NCT04054609. Registered 20,190,611, https//clinicaltrials.gov/study/NCT04054609.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".