Sci-Thurs PM: Planning-02: Using a Second Treatment Planning System for Dose Calculation Verifications in IMRT Patient Specific Quality Assurance
Bibliographic record
Abstract
In this study, we used a second treatment planning system (TPS) for independent verification of the dose calculated by our primary TPS in the context of patient specific quality assurance (QA) for intensity modulated radiation therapy (IMRT). QA plans for 24 patients treated with inverse planned dynamic IMRT were generated using the Nomos Corvus TPS. The plans were calculated on a CT scan of our QA phantom that consists of three Solid Water slabs sandwiching radiochromic films, and an ion chamber inserted into the center slab. For the independent verification, the dose was recalculated using the Varian Eclipse TPS from the original plan. The absolute dose to the ion chamber volume was then compared, as well as relative dose on isodoses calculated at the film plane. The calculation results were also compared to measurements. For point doses the mean ratio was 0.999 (SD 0.010) for Eclipse versus Corvus, 0.988 (SD 0.020) for the chamber measurements versus Corvus, and 0.989 (SD 0.017) for the chamber measurements versus Eclipse. For 2D doses with gamma histogram the mean value of the percentage of pixels passing the criteria of 3%, 3 mm was 94.4 (SD 5.3) for Eclipse versus Corvus, 85.1 (SD 10.6) for Corvus versus film, and 93.7 (SD 4.1) for Eclipse versus film. We feel that it is feasible to use the Eclipse TPS as an independent, accurate, and time efficient method for dose calculations verifications IMRT QA in clinic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".