Poster — Thur Eve — 39: SBRT imaging analysis — patient results and QA of imaging systems
Bibliographic record
Abstract
Our centre began offering stereotactic body radiation therapy (SBRT) treatments for peripheral lung lesions in 2011. As a high-precision technique, SBRT requires precise positioning of the target, and precise quality assurance (QA) of the imaging systems; these may be dependent on local equipment and procedures. We aimed to maintain target position within 3 mm throughout each treatment, and imaging and mechanical systems to at least 2 mm accuracy. A retrospective analysis was done of patient cone-beam (CB) data, and of our imaging system QA, to assess our spatial objectives and look for opportunities for improvement. The data indicated that, using our immobilization and imaging procedures, target position was maintained within 3 mm 96% of the time, and 75% within 2 mm, similar to results from other centres. Imaging system QA using the standard ball-bearing test showed system accuracy was maintained well within 1 mm. These results were compared with a simpler daily QA procedure using a Pentaguide phantom. The mean and standard deviation of the radial difference in the kV-MV isocenter coincidence for the two techniques was 0.62mm +/- 0.23mm. With appropriate choice of tolerance and action level, the morning QA was sufficient for identifying outliers requiring further investigation. This analysis gives us confidence in understanding the performance of our SBRT lung treatments, and gives baselines for analyzing changes to patient immobilization or imaging procedures.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".