SU‐E‐T‐824: Accuracy of Low Doses in Lung for Locoregional Breast Irradiation with TomoTherapy and VMAT
Bibliographic record
Abstract
Purpose: To compare low lung doses in treatment plans for left side locoregional breast cancer with TomoTherapy and Volumetric Modulated Arc Therapy (VMAT) where VMAT is planned by Monaco® (Elekta‐CMS) treatment planning system which incorporates a Monte‐Carlo (MC) dose calculation engine. Methods: Four patients previously treated on TomoTherapy were replanned with the VMAT technique for treatment on Elekta Synergy. Plans were optimized and calculated using Pencil Beam (PB) and MC algorithms available in Monaco. As a benchmark, plans were also recomputed using a previously validated and published in‐house Monte Carlo model of TomoTherapy and VMAT. Particular attention was given to the low lung doses (V5Gy and V20Gy), which are considered as important clinical parameters in evaluating the acceptability of this type of treatment plan, but where accuracy is usually lacking with current planning system dose calculation algorithms. Results: Similar dose distribution and time of delivery could be achieved between TomoTherapy plans (V95%=99.1+/− 0.9%). and VMAT plans (V95%=98.5+/−0.6%). The average percentage (+/− 1SD) difference between our in‐house MC model lung doses and Monaco MC lung doses was (2+/−2%) for V5Gy and (4+/−1%) for V20Gy. Slightly larger average percentage differences were observed between our in house MC model for the V5Gy (−11+/−4%) and V20Gy (3+/−1%) for plans calculated using Collapsed Cone Convolution (CCC) algorithm in TomoTherapy. As expected, the largest average percentage differences for V5Gy (−28+/−1%) and V20Gy (−10+/−2%) were found between the in‐house MC model and the PB model in Monaco. Conclusions: We have demonstrated the feasibility of adapting our technique developed on TomoTherapy for treating left sided locoregional breast cancer to VMAT and compared the accuracy of the low isodoses calculated with the Monaco MC algorithm to the TomoTherapy CCC algorithm.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".