Magnetic Resonance Imaging Frequency After Stereotactic Body Radiation Therapy for Spine Metastases
Bibliographic record
Abstract
PURPOSE: Stereotactic body radiation therapy (SBRT) is increasingly being used to treat spine metastases. Current post-SBRT imaging surveillance strategies in this patient population may benefit from a more data-driven and personalized approach. The objective of this study was to develop risk-stratified post-SBRT magnetic resonance imaging (MRI) surveillance strategies using quantitative methods. METHODS AND MATERIALS: Adult patients with bony spine metastases treated with SBRT between 2008 and 2021 and who had at least 2 follow-up spine MRIs were reviewed retrospectively. A recursive partitioning analysis model was developed to separate patients into different risk categories for post-SBRT progression anywhere within the spine. Imaging intervals were derived for each risk category using parametric survival regression based on multiple expected spine progression rates per scan. RESULTS: A total of 446 patients and 1039 vertebral segments were included. Cumulative incidence of spine progression was 19.2% at 1 year, 26.7% at 2 years, and 35.3% at 4 years. The internally validated risk stratification model was able to divide patients into 3 risk categories based on epidural disease, paraspinal disease, and Spinal Instability Neoplastic Score category. The 4-year risk of spine progression was 23.4%, 39.0%, and 51.8%, respectively, for the low-, intermediate-, and high-risk groups. Using an expected per-scan spine progression rate of 3.75%, the low-risk group would require follow-up scans every 6.0 months (95% CI, 4.9-7.6) and the intermediate-risk group would require surveillance every 3.1 months (95% CI, 2.6-3.7). At an expected spine progression rate of 5%, the high-risk group would require surveillance every 1.3 months (95% CI, 1.1-1.6) during the first 13.2 months after SBRT and every 5.9 months thereafter (95% CI, 2.8-12.3). CONCLUSIONS: Data-driven follow-up MRI surveillance intervals at a range of expected spine progression rates have been determined for patients at different risks of spine progression based on an internally validated, single-institution risk stratification model.
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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 | 0.001 | 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) | 0.001 | 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".