Poster — Thur Eve — 23: Dose and Position Quality Assurance using the RADPOS System for 4D Radiotherapy with CyberKnife
Bibliographic record
Abstract
Introduction: RADPOS 4D dosimetry system consists of a microMOSFET dosimeter combined with an electromagnetic positioning sensor, which allows for performing real‐time dose and position measurements simultaneously. In this report the use of RADPOS as an independent quality assurance (QA) tool during CyberKnife 4D radiotherapy treatment is described. In addition to RADPOS, GAFCHROMIC® films were used for simultaneous dose measurement. Methods: RADPOS and films were calibrated in a Solid Water® phantom at 1.5 cm depth, SAD= 80 cm, using 60 mm cone. CT based treatment plan was created for a Solid Water® breast phantom containing metal fiducials and RADPOS probe. Dose calculations were performed using iPlan pencil beam algorithm. Before the treatment delivery, GAFCHROMIC® film was inserted inside the breast phantom, next to the RADPOS probe. Then the phantom was positioned on the chest platform of the QUASAR, to which Synchrony LED optical markers were also attached. Position logging began for RADPOS and the Synchrony tracking system, the QUASAR motion was initiated and the treatment was delivered. Results: RADPOS position measurements very closely matched the LED marker positions recorded by the Synchrony camera tracking system. The RADPOS measured dose was 2.5% higher than the average film measured dose, which is within the experimental uncertainties. Treatment plan calculated dose was 4.1 and 1.6% lower than measured by RADPOS and film, respectively. This is most likely due to the inferior nature of the dose calculation algorithm. Conclusions: Our study demonstrates that RADPOS system is a useful tool for independent QA of CyberKnife treatments.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.017 |
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".