Sci—Thur AM: YIS ‐ 03: irtGPUMCD: a new GPU‐calculated dosimetry code for <sup>177</sup>Lu‐octreotate radionuclide therapy of neuroendocrine tumors
Bibliographic record
Abstract
In peptide receptor radionuclide therapy (PRRT), huge inter‐patient variability in absorbed radiation doses per administered activity mandates the utilization of individualized dosimetry to evaluate therapeutic efficacy and toxicity. We created a reliable GPU‐calculated dosimetry code (irtGPUMCD) and assessed 177Lu‐octreotate renal dosimetry in eight patients (4 cycles of approximately 7.4 GBq). irtGPUMCD was derived from a brachytherapy dosimetry code (bGPUMCD), which was adapted to 177Lu PRRT dosimetry. Serial quantitative single‐photon emission computed tomography (SPECT) images were obtained from three SPECT/CT acquisitions performed at 4, 24 and 72 hours after 177Lu‐octreotate administration, and registered with non‐rigid deformation of CT volumes, to obtain 177Lu‐octreotate 4D quantitative biodistribution. Local energy deposition from the β disintegrations was assumed. Using Monte Carlo gamma photon transportation, irtGPUMCD computed dose rate at each time point. Average kidney absorbed dose was obtained from 1‐cm3 VOI dose rate samples on each cortex, subjected to a biexponential curve fit. Integration of the latter time‐dose rate curve yielded the renal absorbed dose. The mean renal dose per administered activity was 0.48 ± 0.13 Gy/GBq (range: 0.30–0.71 Gy/GBq). Comparison to another PRRT dosimetry code (VRAK: Voxelized Registration and Kinetics) showed fair accordance with irtGPUMCD (11.4 ± 6.8 %, range: 3.3–26.2%). These results suggest the possibility to use the irtGPUMCD code in order to personalize administered activity in PRRT. This could allow improving clinical outcomes by maximizing per‐cycle tumor doses, without exceeding the tolerable renal dose.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".