Comparison of conventional and Monte Carlo dose calculations for prostate treatments
Bibliographic record
Abstract
Monte Carlo (MC) calculations are rapidly finding their place in clinical dose assessments. We investigated conformal prostate dose distributions as calculated by MC, and compared them to several analytical dose calculations. The treatment distributions for twenty prostate cancer patients, treated with 18 MV 3D conformal radiation therapy, were retrospectively assessed. The BEAM code based on EGSnrc was used to model the beam from which phase space files were used as input into the XVMC algorithm. This was compared to conventional treatment planning system calculations (CADPLAN) with and without inhomogeneity corrections. Results indicate that the CADPLAN generalized Batho Power Law, modified Batho Power Law, and equivalent tissue-air ratio methods contain inaccuracies in calculated dose to 95 % of the prostate planning target volume of 3.5 %, 3.3 %, and 2.9 %, respectively. The greatest discrepancies in the organs at risk were seen in the bladder where the inhomogeneity correction methods all predicted that 50 % of the prescribed dose covered an average of 8.2 % more of the bladder volume than that predicted from the MC calculation. Water equivalent MC and water equivalent CADPLAN calculations revealed important discrepancies on the same order as those between heterogeneous MC and heterogeneous CADPLAN calculations. The data indicate that the effect of inhomogeneities is greater in the target volume than the organs at risk, and that accurately modeling the dose deposition process is important for each patient geometry, and may have a greater impact on the dose distribution in the prostate region than correcting an analytical algorithm for the presence of inhomogeneities.
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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 | 0.002 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".