Assessing small-volume spinal cord dose for repeat spinal stereotactic body radiotherapy treatments
Bibliographic record
Abstract
Spinal cord biologically effective dose (BED) limits are critical to safe spine stereotactic body radiotherapy (SBRT) delivery. In particular, when repeating SBRT to the same site, the problem of adding non-uniform BED distributions within small volumes of spinal cord has yet to be solved. We report a probability-based generalized BED (gBED) model to guide repeat spine SBRT treatment planning. The gBED was formulated by considering the sequential damaging probabilities of repeat spine SBRT treatments. Parameters from the standard linear-quadratic model, such as α/β = 2 Gy for the spinal cord, were applied. We tested the model based on SBRT specific spinal cord tolerance using a simulated and ten clinical repeat SBRT cases. The gBED provides a consistent solution for superimposing non-uniform dose distributions from different fractionation schemes, analogous to the BED for uniform dose distributions. Based on ten clinical cases, the gBED was observed to eliminate discrepancies in the cumulative BED of approximately 5% to 20% within small volumes (e.g. 0.1-2.0 cc) of spinal cord, as compared to a conventional calculation method. When assessing spinal cord tolerance for repeat spinal SBRT treatments, caution should be exercised when applying conventional BED calculations for small volumes of spinal cord irradiated, and the gBED potentially provides more conservative and consistently derived dose surrogates to guide safe treatment planning and treatment outcome modeling.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".