SU-G-BRC-09: Experimental Validation of a VMAT Planning Technique for Patients with Hip Prosthesis
Bibliographic record
Abstract
Purpose: To validate and evaluate the dosimetric accuracy of two different VMAT planning techniques for patients with hip prosthesis of unknown composition. Methods: A phantom was designed to model a patient with one stainless steel hip prosthesis. PTVs and critical structures contours were copied from a prostate cancer patient. Simultaneous integrated boost VMAT plans covering the prostate and pelvic lymph nodes (50.4Gy and 70Gy in 28 fractions) and sparing the rectum and bladder were generated in Eclipse using AAA dose calculation. Our clinical dose constraints were used for PTV coverage and rectum and bladder sparing. Three VMAT plans were generated. First an unconstrained plan (UN) was created. Two other plans reducing the entrance dose through the prosthesis were developed: a plan using avoidance sector (AV) and a plan with maximum dose constraint on the hip prosthesis (MP). The three plans were measured with gafchromic EBT3 film and analyzed with FilmQAPro software. Results: The three VMAT plans resulted in an average gamma 2%/2mm of 48.8%, 95.4%, 98.4% (UN, AV and MP plan respectively) and an average gamma 3%/3mm of 76.3%, 99.7%, 99.9% (UN, AV and MP plan respectively). The avoidance sector plan exhibited higher measured doses of up to 3% of the prescription dose at the prosthesis interface. The plan with constraints on the prosthesis showed excellent agreement at the prosthesis interface. Conclusion: For the phantom presented in this study, VMAT plans with a maximum dose constraint on the prosthesis resulted in best agreement with measured doses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".