SU‐C‐BRA‐02: Modular Patient‐Specific Compensation for Co‐60 TBI Treatments Based on Monte Carlo Design
Bibliographic record
Abstract
Purpose: Using Monte Carlo (MC) simulations and measurements, to develop a new treatment technique for the delivery of TBI at extended SSD using a custom modified Co‐60 unit equipped with flattening filter and modular, patient‐specific compensators. Methods: An existing Eldorado‐78 Co‐60 teletherapy unit was stripped from its original collimator and equipped with beam‐defining cerrobend blocks for extended SSD TBI treatments. An acrylic flattening filter was numerically designed based on detailed mapping of the dose distribution of the large open field at 10 cm depth in water using a primary radiation attenuation calculation. An EGSnrc MC model of the resulting unit was developed and experimentally validated. The model was used to calculate MC dose in whole‐body supine and prone CT images of a patient. The total dose, calculated by summing prone and supine dose after deformable registration (VelocityAI™) was used to design modular, patient‐specific compensators, based on the premise that dose in the patient mid‐plane ought to be uniform. Results: The designed flattening filter flattens the beam to within ±2% over a 200 cm × 70 cm area at 10 cm depth in water. Experimental validation of the calculated dose profiles in the open and flattened beams shows agreement of better than 2% and 1%, respectively. Patient MC dose calculations in the flattened, uncompensated beam showed dose deviations from prescription dose most notably in lung, neck, arm and leg areas ranging from −5% to +25%. Patient‐specific compensation reduced non‐homogeneities in the patient to within −5% to +10%. The clinical implementation involves a modular, Lego®‐style realization of the compensator using orthogonal parallelepiped blocks on a plate that slides in the treatment head tray. Conclusions: This work demonstrates that a Co‐60 TBI setup combined with patient‐specific compensators numerically designed using MC calculations is clinically feasible and highly improves the quality of the treatment.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".