SU‐E‐T‐479: IMRT Plan Recalculation in Patient Based On Dynalog Data and the Effect of a Single Failing MLC Motor
Bibliographic record
Abstract
Purpose: Using Linac dynamic logs (Dynalogs) we evaluate the impact of a single failing MLC motor on the deliverability of an IMRT plan by assessing the recalculated dose volume histograms (DVHs) taking the delivered MLC positions and beam hold‐offs into consideration. Methods: This is a retrospective study based on a deteriorating MLC motor (leaf 36B) which was observed to be failing via Dynalog analysis. To investigate further, Eclipse‐importable MLC files were generated from Dynalogs to recalculate the actual delivered dose and to assess the clinical impact through DVHs. All deliveries were performed on a Varian 21EX linear accelerator equipped with Millennium‐120 MLC. The analysis of Dynalog files and subsequent conversion to Eclipse‐importable MLC files were all performed by in‐house programming in Python. Effects on plan DVH are presented in the following section on a particular brain‐IMRT plan which was delivered with a failing MLC motor which was then replaced. Results: Global max dose increased by 13.5%, max dose to the brainstem PRV increased by 8.2%, max dose to the optic chiasm increased by 7.6%, max dose to optic nerve increased by 8.8% and the mean dose to the PTV increased by 7.9% when comparing the original plan to the fraction with the failing MLC motor. The reason the dose increased was due to the failure being on the B‐bank which is the lagging side on a sliding window delivery, therefore any failures on this side will cause an over‐irradiation as the B‐bank leaves struggles to keep the window from growing. Conclusion: Our findings suggest that a single failing MLC motor may jeopardize the entire delivery. This may be due to the bad MLC motor drawing too much current causing all MLCs on the same bank to underperform. This hypothesis will be investigated in a future study.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".