SU-E-T-546: Clinical Impact of Detector Selection in Small Photon Beam Modelling
Bibliographic record
Abstract
Purpose: Small photon fields are omnipresent in modern radiotherapy technique such as IMRT or Linac-based SRS. The specific purpose of this study is to evaluate the clinical differences in SRS treatment when different types of detectors are used for beam modelling. Methods: In this work, PDD, profiles and OF measurements were measured for a 6MV beam on an Elekta Synergy-S Linac for square field size ranging from 0.8×0.8 to 10.4×10.4 cm2. Five different detectors have been investigated: Gafchromic EBT2 film, PTW microLion liquid chamber, Sun Nuclear EDGE detector, IBA PFD Photon diode and IBA SFD stereotactic diode. To evaluate the effect of different detector-based models on SRS treatment, dose at isocenter was calculated independently for each detector in nine different cranial SRS treatment plan. All these plans had between 8 and 10 beams and field sizes ranging from 0.8 to 2.4 cm in order to simulate various tumor sizes.Results: For small field size (<4cm), the EDGE detector and the stereotactic diode SFD provided sharper penumbra when compared to microLion and photon diode PFD. The stereotactic diode was discarded of the cranial SRS study because it underestimated small field OF by 5%. For treatment plan with field size of 1.6 and 2.4 cm, the maximum discrepancy in dose calculations between all other detectors was 2.6%. For field size of 0.8 cm, the dose differences tended to be larger, going up to 3.6% Conclusions: For field size starting at 1.6×1.6cm2, PFD photon diode, EDGE detector, EBT2 gafchromic and microLion could provide reliable PDD and OF for beam modelling. A specific model for each of the detectors covered in this study is underway in Pinnacle TPS to evaluate detector-specific differences in OAR and PTV coverage.
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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 | 0.002 | 0.005 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".