Comparison of four techniques for spine stereotactic body radiotherapy: Dosimetric and efficiency analysis
Bibliographic record
Abstract
PURPOSE: The aim of this study is to compare the dosimetric differences between four techniques for spine stereotactic body radiotherapy (SBRT): CyberKnife (CK), volumetric modulated arc therapy (VMAT), and helical tomotherapy (HT) with dynamic jaws (HT-D) and fixed jaws (HT-F). MATERIALS/METHODS: Data from 10 patients were utilized. All patients were planned for 24 Gy in two fractions, with the primary objectives being: (a) restricting the maximum dose to the cord to ≤ 17 Gy and/or cauda equina to ≤ 20 Gy, and (b) to maximize the clinical target volume (CTV) to receive the prescribed dose. Treatment plans were generated by separate dosimetrists and then compared using velocity AI. Parameters of comparison include target volume coverage, conformity index (CI), gradient index (GI), homogeneity index (HI), treatment time (TT) per fraction, and monitor units (MU) per fraction. RESULTS: PTV D2 and D5 were significantly higher for CK compared to VMAT, HT-F, and HT-D (P < 0.001). The average volume of CTV receiving the prescription dose (CTV D95) was significantly less for VMAT compared to CK, HT-F and HT-D (P = 0.036). CI improved for CK (0.69), HT-F (0.66), and HT-D (0.67) compared to VMAT (0.52) (P = 0.013). CK (41.86) had the largest HI compared to VMAT (26.99), HT-F (20.69), and HT-D (21.17) (P < 0.001). GI was significantly less for CK (3.96) compared to VMAT (6.76) (P = 0.001). Likewise, CK (62.4 min, 14059 MU) had the longest treatment time and MU per fraction compared to VMAT (8.5 min, 9764 MU), HT-F (13 min, 10822 MU), and HT-D (13.5 min, 11418 MU) (P < 0.001). CONCLUSION: Both CK and HT plans achieved conformal target coverage while respecting cord tolerance. Dose heterogeneity was significantly larger in CK. VMAT required the least treatment time and MU output, but had the least steep GI, CI, and target coverage.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".