TH‐E‐BRB‐03: Potential for a Monte Carlo Based Treatment Planning System to Replace Patient Specific QA Measurements in a Large University Hospital Setting
Bibliographic record
Abstract
Purpose: To asses the feasibility and benefits of replacing patient specific QA measurements and back‐up MU calculations with Monte Carlo based dose calculations for improved plan verification in radiation therapy. Methods: An in‐house Monte Carlo based planning system (MMCTP) has been clinically implemented at McGill University. Over 50 patient plans have been recalculated on the system by both dosimetrists and physicists. Initial plans chosen for recalculation were head and neck IMRT and lung SBRT plans, where simple monitor unit calculators and measurements in water equivalent phantoms are often insufficient to show agreement of the planned and delivered doses within 5%. Results: MMCTP was found to be easy to use by both dosimetrists and physicists. Eighteen head and neck IMRT plans were recalculated by a dosimetrist and over 35 lung SBRT plans have been calculated by the physics staff. As no effort was spent in implementing fast MC engines, the calculation time is long but all mechanical operations, i.e., the transfer of the plans from the clinical treatment planning system to the MMCTP system and starting the calculation, take only a few minutes. Differences between MMCTP and the clinical TPS (Varian Eclipse, AAA) were small in terms of dose delivered to the PTV for the head and neck IMRT group but differences of greater the 5% were seen among the lung SBRT group. For both groups, the largest differences were seen for anatomy close to the skin and near air cavities. Conclusions: Verifying the delivery accuracy of complex treatment plans can be challenging. Current techniques requiring recalculation and measurement of plans in uniform phantoms are cumbersome and do not take into account the patient specific heterogeneity effects. The MMCTP treatment planning system requires minimal physics resources and likely introduces a more comprehensive method of evaluating calculation accuracy throughout the treatment volume.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".