Cone Beam CT (CBCT) Evaluation of Inter- and Intra-Fraction Motion for Patients Undergoing Brain Radiotherapy Immobilized using a Commercial Thermoplastic Mask on a Robotic Couch
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Bibliographic record
Abstract
Patients receiving fractionated intensity-modulated radiation therapy (IMRT) for brain tumors are often immobilized with a thermoplastic mask; however, masks do not perfectly re-orient the patient due to factors including the maximum pressure which can be applied to the face, deformations of the mask assembly, patient compliance, etc. Consequently, ~3-5mm PTV margins (beyond the CTV) are often recommended. We aimed to determine if smaller PTV margins are feasible using mask immobilization coupled with 1) a gantry mounted CBCT image guidance system and 2) position corrections provided by a full six-degree of freedom (6-DOF) robotic couch. A cohort of 34 brain tumor patients was treated with fractionated IMRT. After the mask set-up, an initial CBCT was obtained and registered to the planning CT. The robotic couch corrected the misalignments in all 6-DOF and a pre-treatment verification CBCT was then obtained. The results indicated a repositioning alignment within our threshold of 1.5 mm (3D). Treatment was subsequently delivered. A post-treatment CBCT was obtained to quantify intra-fraction motion. Initial, pre-treatment and post-treatment CBCT image data was analyzed. A total of 505 radiation fractions were delivered to the 34 patients resulting in ~1800 CBCT scans. The initial median 3D (magnitude) set-up positioning error was 2.60 mm. Robotic couch corrections reduced the 3D median error to 0.53 mm prior to treatment. Intra-fraction movement was responsible for increasing the median 3D positioning error to 0.86 mm, with 8% of fractions having a 3D positioning error greater than 2 mm. Clearly CBCT image guidance coupled with a robotic 6-DOF couch dramatically improved the positioning accuracy for patients immobilized in a thermoplastic mask system; however, such intra-fraction motion would be too large for single fraction radiosurgery.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it