Uncertainty in Measurements of 18F Blood Concentration and Its Effect on Simplified Dynamic PET Analysis
Bibliographic record
Abstract
UNLABELLED: The purpose of this study was to assess the accuracy and practicality of well counter- and thyroid probe-based methods, commonly available in nuclear medicine facilities, for measuring the concentration of (18)F-FDG in blood samples. The degree to which the accuracy of such methods influences quantitative analysis of dynamic PET scans was also assessed. METHODS: Thirty-five patients with cancer of the head and neck underwent dynamic PET imaging as part of a study intended to evaluate the utility of quantitative, image-based metrics for assessment of early treatment response. The activity in blood samples from the patients, necessary to provide an estimate of the input function for quantitative analysis, was measured both using a thyroid probe and using a well counter. Three calibration techniques were compared: single-point calibration using a standard solution for the thyroid probe (ProbePoint technique), single-point calibration using a standard solution for the well counter (WellPoint technique), and multiple-point calibration over the full range of expected blood activities for the well counter (WellCurve technique). The WellCurve method was assumed to provide the most accurate estimate of blood activity. The precision of measuring blood volume using a micropipette was also evaluated by obtaining multiple blood samples. Simplified-kinetic-analysis multiple-time-point (SKA-M) uptake rates for the primary tumor were calculated for all 35 patients using PET images and each of the 3 methods for assessing blood concentration. RESULTS: Errors in blood activity measurements ranging from -9.5% to 7.6% were found using the ProbePoint method, whereas the error range was much less (from -1.3% to 0.9%) for the WellPoint method. The precision in blood volume measurements ranged from -6% to 12% in the 10 patients assessed. The errors in blood activity and volume measurements were reflected in the SKA-M measurements in the same range. CONCLUSION: The WellPoint method provides a compromise between accuracy and clinical practicality. Random errors in both blood activity and volume measurements accumulate and may compromise parameters--such as the SKA-M estimate of tumor uptake rate--that depend not only on images but also on blood concentration data.
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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 | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".