The effect of voxel size on the accuracy of dose‐volume histograms of prostate seed implants
Bibliographic record
Abstract
Cumulative dose-volume histograms (DVH) are crucial in evaluating the quality of radioactive seed prostate implants. When calculating DVHs, the choice of voxel size is a compromise between computational speed (larger voxels) and accuracy (smaller voxels). We quantified the effect of voxel size on the accuracy of DVHs using an in-house computer program. The program was validated by comparison with a hand-calculated DVH for a single 0.4-U iodine-125 model 6711 seed. We used the program to find the voxel size required to obtain accurate DVHs of five iodine-125 prostate implant patients at our institution. One-millimeter cubes were sufficient to obtain DVHs that are accurate within 5% up to 200% of the prescription dose. For the five patient plans, we obtained good agreement with the VariSeed (version 6.7, Varian, USA) treatment planning software's DVH algorithm by using voxels with a sup-inf dimension equal to the spacing between successive transverse seed implant planes (5 mm). The volume that receives at least 200% of the target dose, V200, calculated by VariSeed was 30% to 43% larger than that calculated by our program with small voxels. The single-seed DVH calculated by VariSeed fell below the hand calculation by up to 50% at low doses (30 Gy), and above it by over 50% at high doses (>250 Gy).
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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 | 0.023 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".