SU‐E‐T‐253: Assessing Small‐Volume Cord Biological Effective Dose for Repeat Spinal Stereotactic Body Radiotherapy Treatments
Bibliographic record
Abstract
PURPOSE: Small-volume biologically effective (BED) dose limits are critical to safe spinal stereotactic body radiotherapy (SBRT) delivery. However, due to mismatch in spatial location of dose hot spots from non-uniform dose distributions inherent to SBRT, for repeat treatment courses they cannot be simply added by assuming a uniform dose distribution. This study aims to develop a probability-based biological equivalent dose formula to solve this problem. METHODS: A generalized biological equivalent dose (gBED) was formulated via computing damaging or survival probability of repeat spine SBRT treatments. Parameters from the linear-quadratic model such as α/β =2 Gy for the spinal cord were applied for the gBED calculations. The derived method was applied to both simulated and clinical treatment cases to demonstrate its applicability and usefulness for assessing spinal cord dose limits for repeated SBRT treatment courses. RESULTS: The gBED formula allows direct superposition of dose within a small volume of spinal cord from a non-uniform dose distribution of varying dose fractionation schemes of SBRT. From the studied examples, traditional BED calculations even with full voxel-by-voxel tracking calculations resulted in inconsistent BED values and can underestimate the biological dose to a small-volume spinal cord by as much as 20%. Such an error tends to increase rapidly with increasing volume of interests such as from 0.1 mL to 2.0 mL. CONCLUSIONS: When assessing spinal cord tolerance for repeat spinal SBRT treatments, consistent surrogates such as gBED are needed to avoid potential underestimation of treatment-induced complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".