Total body irradiation dose optimization based on radiological depth
Bibliographic record
Abstract
We have previously demonstrated the use of Eclipse fluence optimization to define aperture sizes for a novel aperture modulated translating bed total body irradiation (TBI) technique. The purposes of the present study were to identify, characterize, and correct for sources of error inherent in our previous fluence optimization technique, and to develop a clinically viable fluence optimization module for the translating bed TBI technique. Aperture modulated TBI is delivered by translating the patient at constant speed on a custom bed under a modulated radiation beam. The patient is then turned from supine to prone and the process repeated, resulting in an AP-PA treatment. Radiological depths were calculated along divergent ray lines through individual CT slices of a RANDO phantom. Beam apertures, defined using a dynamic multileaf collimator (DMLC), were generated using calculated radiological depths and calibration factors that relate fluence to aperture size in a dynamic environment. These apertures were defined every 9 mm along the phantom superior-inferior axis. The calculated beam apertures were further modified to account for scatter within the patient. For dose calculation purposes the individual MLC files were imported into Eclipse. For treatment delivery, dynamic MLC files for both AP and PA beams were generated and delivered dynamically. Dose homogeneity in the head and neck region of the RANDO phantom was within ± 4% of the prescribed dose with this novel technique compared to -5% to +7% with our previous aperture modulated technique based on Eclipse fluence optimization. Fluence optimization and beam aperture calculation using the new technique offers a ten-fold reduction in planning time and significantly reduces the likelihood of user error during the planning process. In conclusion, a clinically viable aperture modulated translating bed TBI technique that employs dynamically shaped MLC-defined beam apertures based on radiological depth calculations, has been developed.
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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 | 0.000 | 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".