Impact of dead time on quantitative 177Lu-SPECT (QSPECT) and kidney dosimetry during PRRT
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Background Dead time may affect the accuracy of quantitative SPECT (QPSECT), and thus of dosimetry. The aim of this study was to quantify the effect of dead time on 177 Lu-QSPECT and renal dosimetry following peptide receptor radionuclide therapy (PRRT) of neuroendocrine tumours. Methods QSPECT/CT was performed on days 1 and 3 during 564 personalized 177 Lu-octreotate cycles in 166 patients. The dead-time data for each scanning time point was compiled. The impact of not correcting QSPECT for the dead time was assessed for the kidney dosimetry. This was also estimated for empiric PRRT by simulating in our cohort a regime of 7.4 GBq/cycle. Results The probability to observe a larger dead time increased with the injected activity. A dead-time loss greater than 5% affected 14.4% and 5.7% of QSPECT scans performed at days 1 and 3, respectively. This resulted in renal absorbed dose estimates that would have been underestimated by more than 5% in 5.7% of cycles if no dead-time correction was applied, with a maximum underestimation of 22.1%. In the simulated empiric regime, this potential dose underestimation would have been limited to 6.2%. Conclusion Dead-time correction improves the accuracy of dosimetry in 177 Lu radionuclide therapy and is warranted in personalized PRRT.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it