Reproducibility of a Noninvasive System for Eye Positioning and Monitoring in Stereotactic Radiotherapy of Ocular Melanoma
Bibliographic record
Abstract
PURPOSE: Our preferred treatment for juxtapapillary choroidal melanoma is stereotactic radiotherapy. We aim to describe our immobilization system and quantify its reproducibility. MATERIALS AND METHODS: Patients were identified in our radiosurgery database. Patients were imaged at computed tomography simulator with an in-house system which allows visual monitoring of the eye as the patient fixates a small target. All patients were reimaged at least once prior to and/or during radiotherapy. The patients were treated on the CyberKnife system, 60 Gy in 10 daily fractions, using skull tracking in conjunction with our visual monitoring system. In order to quantify the reproducibility of the eye immobilization system, computed tomography scans were coregistered using rigid 6-dimensional skull registration. Using the coregistered scans, x, y, and z displacements of the lens/optic nerve insertion were measured. From these displacements, 3-dimensional vectors were calculated. RESULTS: Thirty-four patients were treated from October 2010 to September 2015. Thirty-nine coregistrations were performed using 73 scans (2-3 scans per patient). The mean displacements of lens and optic nerve insertion were 0.1 and 0.0 mm. The median 3-dimensional displacements (absolute value) of lens and nerve insertion were 0.8 and 0.7 mm (standard deviation: 0.5 and 0.6 mm). Ninety-eight percent of 3-dimensional displacements were below 2 mm (maximum 2.4 mm). The calculated planning target volume (PTV) margins were 0.8, 1.4, and 1.5 mm in the anterior-posterior, craniocaudal, and right-left axes, respectively. Following this analysis, no further changes have been applied to our planning margin of 2 to 2.5 mm as it is also meant to account for uncertainties in magnetic resonance imaging to computed tomography registration, skull tracking, and also contouring variability. CONCLUSION: We have found our stereotactic eye immobilization system to be highly reproducible (<1 mm) and free of systematic error.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".