Apparatus-Dependent Dosimetric Differences in Spine Stereotactic Body Radiotherapy
Bibliographic record
Abstract
The purpose of this investigation was to study apparatus-dependent dose distribution differences specific to spine stereotactic body radiotherapy (SBRT) treatment planning. This multi-institutional study was performed evaluating an image-guided robotic radiosurgery system (CK), intensity modulated protons (IMP), multileaf collimator (MLC) fixed-field IMRT with 5 mm (11 field), 4 mm (9 field), and 2.5 mm (8- and 9-field) leaf widths and intensity modulated volumetric arc therapy (IMVAT) with a 2.5 mm MLC. Treatment plans were systematically developed for targets consisting of one, two and three consecutive thoracic vertebral bodies (VBs) with the esophagus and spinal cord contoured as the organs at risk. It was found that all modalities achieved acceptable treatment planning constraints. However, following normalization fixed field IMRT with a 2.5 mm MLC, IMVAT and IMP systems yielded the smallest ratio of maximum dose divided by the prescription dose (MD/PD) for one-, two- and three-VB PTVs (ranging from 1.1-1.16). The 2.5 mm MLC 9-field IMRT, IMVAT and CK plans resulted in the least dose to 0.1 cc volumes of spinal cord and esophagus. CK plans had the greatest degree of target dose inhomogeneity. As the level of complexity increased with an increasing number of vertebral bodies, distinct apparatus features such as the use of a high number of beams and a finer leaf size MLC were favored. Our study quantified apparatus-dependent dose-distribution differences specific to spine SBRT given strict, but realistic, constraints and highlights the need to benchmark physical dose distributions for multi-institutional clinical trials.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".