Sci-Thurs AM: YIS-05: Accuracy of Patient-Specific Dosimetry for Clinical Use in Targeted Radionuclide Therapy
Bibliographic record
Abstract
Introduction: Targeted radionuclide therapy (TRT) uses radiopharmaceuticals that target tumour tissue, potentially delivering large radiation doses to tumours, while minimizing the dose to surrounding healthy tissue. In this work we investigated how various approximations affect the accuracy of patient-specific dose calculations in TRT. Methods: Time-activity curves (TACs) were acquired from a series of nuclear medicine images, including one SPECT/CT image and multiple planar scans in two patients. Biodistribution of radiopharmaceutical was modeled using: an exponential fit, trapezoidal areas, and without the use of a long term scan to draw the TACs. Cumulated activities (area under TACs) were determined and used in three different dose calculation methods: OLINDA/EXM code, MIRD voxelized S-values (MVSV), and Monte Carlo simulation (MCS), considered here as the gold standard. Results: Different methods for drawing TACs showed that resulting areas under the curve differ by up to a factor of 4. For dose calculation, OLINDA and MVSV doses differed from the average MCS dose by 5% and 3% respectively. OLINDA does not provide details of dose distribution throughout the tumour, whereas MVSV does. The drawback of MVSV is that it assumes a source material of uniform density. Conclusions: An accurate determination of the TAC is essential for proper dosimetry. Both the OLINDA code and MVSV provide average tumour doses that match the MCS results. MVSV is more versatile than OLINDA and can be used to calculate dose distributions that closely resemble the MCS dose for tumours located in regions with reasonably uniform tissue density.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".