Assessment of Common Clinical Detectors Feasibility for Measuring Small-Field Output Factors in Stereotactic Radiosurgery using Eclipse TPS
Bibliographic record
Abstract
Objective: This study aimed to evaluate the comparative suitability of four widely available radiation detectors: the CC13 ionization chamber, Matrixx Evolution, Electronic Portal Imaging Device (EPID), and IBA DQA, for small-field output factor measurements, using the CC01 ionization chamber as the reference standard. Methods: The output factors were measured for asymmetric field sizes ranging from 1×1 cm ² to 10×10 cm². The collected OF data for each detector was then used to independently configure five separate beam models in the Eclipse TPS (version 17.0), ensuring that all other configuration parameters, such as the PDDs and profiles, remained constant. To assess the clinical impact of the differences in detector-based beam modeling, a retrospective cohort of 20 patient SRS treatment plans was recalculated using each of the five beam models. Comparative dosimetric analyses focused on dose-volume histogram parameters for planning target volumes, organs at risk, high- and intermediate-dose spillage, and total number of monitor units. Results: The results showed high consistency across all beam models, with no statistically significant differences observed between the CC01-based configuration and those derived from alternative detectors. Deviations in all dosimetric and treatment delivery parameters were minimal and clinically acceptable, indicating a negligible impact on treatment delivery. Conclusion: These findings suggest that despite the known limitations in small-field dosimetry, the CC13, Matrixx Evolution, EPID, and IBA DQA detectors can reliably derive output factors for TPS commissioning in the SRS context. This provides flexibility in detector selection, particularly in clinics, where access to CC01 may be limited. The dosimetric equivalence demonstrated supports their integration into the clinical workflow for small-field radiotherapy applications.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".