Dose calculation validation of VMC++ for photon beams
Bibliographic record
Abstract
The radiation therapy specific Voxel Monte Carlo (VMC+ +) dose calculation algorithm achieves a dramatic improvement in MC dose calculation efficiency for radiation therapy treatment planning dose evaluation compared with other MC algorithms. This work aims to validate VMC+ + for radiation therapy photon beam planning. VMC++ was validated with respect to the well-benchmarked EGS-based DOSXYZnrc by comparing depth dose and lateral profiles for field sizes ranging from 1 X 1 to 40 x 40 cm(2) for 6 and 18 MV beams in a homogeneous water phantom and in a simulated bone-lung-bone phantom. Patient treatment plan dose distributions were compared for five prostate plans and five head-and-neck (H/N) plans, all using intensity-modulated radiotherapy beams. For all tests, the same incident particles were used in both codes to isolate differences due to modeling of the radiation source. Voxel-by-voxel observed differences were analyzed to distinguish between systematic and purely statistical differences. Dose-volume-histogram-derived dose indices were compared for the patient plans. For the homogeneous water phantom and the bone-lung-bone phantom, the depth dose curve predicted by VMC+ + agreed with that predicted by DOSXYZnrc within expected statistical uncertainty in all voxels except the surface voxel of the water phantom, where VMC+ + predicted a lower dose. When the electron cutoff parameter was decreased for both codes, the surface voxel agreed within expected statistical uncertainty. For prostate plans, the most severe difference between the codes resulted in 55% of the voxels showing a systematic difference of 0.32% of maximum dose. For H/N plans, the largest difference observed resulted in 2% of the voxels showing a systematic difference of 0.98% of maximum dose. For the prostate plans, the most severe difference in the planning target volume D95 was 0.4%, the rectum D35 was 0.2%, the rectum DI7 was 0.2%, the bladder D50 was 0.3% and the bladder D25 was 0.3%. For the H/N plans, the most severe difference in the gross tumor volume D98 was 0.4%, the clinical target volume D90 was 0.2%, the nodes D90 was 0.2%, the parotids D95 was 0.8%, and the cord D2 was 0.8%. All of these differences are clinically insignificant. VMC++ showed an average efficiency gain over DOSXYZnrc of at least an order of magnitude without introducing significant systematic bias. VMC + + can be used for photon beam MC patient dose computations without a clinically significant loss in accuracy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".