Poster — Thur Eve — 34: Estimation of true cumulative dose to the spinal canal in retreated spine SBRT
Bibliographic record
Abstract
Decisions on repeat spine SBRT are based on maximum dose constraints to the spinal cord region. The total dose that is assumed to be tolerable is typically the cumulative maximum biological effective dose (BED) calculated irrespective of location of this dose in the two plans. We explore the true spinal cord region BED taking into account BED position in the two plans. Treatment plans of 16 repeat spine SBRT patients were retrospectively evaluated. TBEDmax and TBED0.35cc (sum from two plans) of spinal cord planning organ‐at‐risk volume (SCPRV) and thecal sac (TS) were calculated using α/β of 2Gy. To assess the ‘true’ dose, the first CT was deformably‐ or rigidly‐registered to the second CT. Image registration integrity was visually tested using the modified volumes from the first plan on the recent CT. Using the acquired registration parameters, the voxel‐based BED(VBEDmax, VBED0.35cc) from the two plans were added together and compared to the TBEDmax and TBED0.35cc. The median(range) SCPRV TBEDmax was 98.7(72.5–135.7)Gy and VBEDmax was 76.6(57.3–129.6)Gy (p=0.01). The median(range) TS TBEDmax was 123.2(108.7–261.6)Gy compared to VBEDmax, which was 93.2(59.6–161.2)Gy (p=0.002). The median(range) SCPRV TBED0.35cc was 72.5(53.1–112.1)Gy and VBED0.35cc is 62.2(37.9–93.3)Gy (p=0.01). The median(range) TS TBED0.35cc was 80.3(64.7–120.6)Gy compared to VBED0.35cc was 68.2(45.2–92.1)Gy (p=0.01). When taking location into account, true additive BED to the SCPRV/TS is lower than the current method of estimating BED for repeat spinal SBRT. By incorporating the dose location information, increase in dose to target volume adjacent to SC may be achievable with accurate and precise image registration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".