Surgical Resection With Radiation Treatment Planning of Spinal Tumors
Bibliographic record
Abstract
BACKGROUND: The clinical paradigm for spinal tumors with epidural involvement is challenging considering the rigid dose tolerance of the spinal cord. One effective approach involves open surgery for tumor resection, followed by stereotactic body radiotherapy (SBRT). Resection extent is often determined by the neurosurgeon's clinical expertise, without considering optimal subsequent post-operative SBRT treatment. OBJECTIVE: To quantify the effect of incremental epidural disease resection on tumor coverage for spine SBRT in an effort to working towards integrating radiotherapy planning within the operating room. METHODS: Ten patients having undergone spinal separation surgery with postoperative SBRT were retrospectively reviewed. Preoperative magnetic resonance imaging was coregistered to postoperative planning computed tomography to delineate the preoperative epidural disease gross tumor volume (GTV). The GTV was digitally shrunk by a series of fixed amounts away from the cord (up to 6 mm) simulating incremental tumor resection and reflecting an optimal dosimetric endpoint. The dosimetric effect on simulated GTVs was analyzed using metrics such as minimum biologically effective dose (BED) to 95% of the simulated GTV (D95) and compared to the unresected epidural GTV. RESULTS: Epidural GTV D95 increased at an average rate of 0.88 ± 0.09 Gy10 per mm of resected disease up to the simulated 6 mm limit. Mean BED to D95 was 5.3 Gy10 (31.2%) greater than unresected cases. All metrics showed strong positive correlations with increasing tumor resection margins (R2: 0.989-0.999, P < .01). CONCLUSION: Spine separation surgery provides division between the spinal cord and epidural disease, facilitating better disease coverage for subsequent post-operative SBRT. By quantifying the dosimetric advantage prior to surgery on actual clinical cases, targeted surgical planning can be implemented.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".