SU-F-T-569: Implementation of a Patient Specific QA Method Using EBT-XD for CyberKnife SRS/SBRT Plans
Bibliographic record
Abstract
Purpose: To implement the new EBT-XD Gafchromic films for accurate dosimetric and geometric validation of stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) CyberKnife (CK) patient specific QA. Methods: Film calibration was performed using a triplechannel film analysis on an Epson 10000XL scanner. Calibration films were irradiated using a Varian Clinac 21EX flattened beam (0 to 20 Gy), to ensure sufficient dose homogeneity. Films were scanned to a resolution of 0.3 mm, 24 hours post irradiation following a well-defined protocol. A set of 12 QA was performed for several types of CK plans: trigeminal neuralgia, brain metastasis, prostate and lung tumors. A custom made insert for the CK head phantom has been manufactured to yield an accurate measured to calculated dose registration. When the high dose region was large enough, absolute dose was also measured with an ionization chamber. Dose calculation is performed using MultiPlan Ray-tracing algorithm for all cases since the phantom is mostly made from near water-equivalent plastic. Results: Good agreement (<2%) was found between the dose to the chamber and the film, when a chamber measurement was possible The average dose difference and standard deviations between film measurements and TPS calculations were respectively 1.75% and 3%. The geometric accuracy has been estimated to be <1 mm, combining robot positioning uncertainty and film registration to calculated dose. Conclusion: Patient specific QA measurements using EBT-XD films yielded a full 2D dose plane with high spatial resolution and acceptable dose accuracy. This method is particularly promising for trigeminal neuralgia plan QA, where the positioning of the spatial dose distribution is equally or more important than the absolute delivered dose to achieve clinical goals.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".