Utility of Molecular Markers in Predicting Local Control Specific to Lung Cancer Spine Metastases Treated with Stereotactic Body Radiotherapy
Bibliographic record
Abstract
Abstract Background and purpose: We report outcomes following spine stereotactic body radiotherapy (SBRT) in metastatic non-small cell lung cancer (NSCLC) and the significance of programmed death-ligand 1 (PD-L1) status, epidermal growth factor receptor (EGFR) mutation and timing of immune check point inhibitors (ICI) on local failure (LF). Materials and methods: 165 patients and 389 spinal segments were retrospectively reviewed from 2009 to 2021. Baseline patient characteristics, treatment and outcomes were abstracted. Primary endpoint was local failure (LF) and secondary, overall survival (OS) and vertebral compression fracture (VCF). Multivariable analysis (MVA) evaluated factors predictive of LF and VCF. Results: The median follow-up and OS were: 13.0 months (range, 0.5–95.3 months) and 18.4 months (95% CI 11.4–24.6). 52.1% were male and 76.4% had adenocarcinoma. Of the 389 segments, 30.3% harboured an EGFR mutation and 17.0% were PD-L1 ≥ 50%. The 24 months LF rate in PD-L1 ≥ 50% vs PD-L1 < 50% was 10.7% vs. 38.0%, and in EGFR-positive vs. negative was 18.1% vs. 30.0%. On MVA, PD-L1 status of ≥ 50% (HR 0.32, 95% CI 0.15–0.69, p = 0.004) significantly predicted for lower LF compared to PD-L1 < 50%. Lower LF trend was seen with ICI administration peri and post SBRT (HR 0.41, 95% CI 0.16–1.05, p = 0.062). On MVA, polymetastatic disease (HR 3.28, 95% CI 1.84–5.85, p < 0.0001) and ECOG ≥ 2 (HR 1.87, 95% CI 1.16–3.02, p = 0.011) significantly predicted for worse OS and absence of baseline VCF predicted for lower VCF rate (HR 0.20, 95% CI 0.10–0.39, p < 0.0001). Conclusion: We report a significant association of PD-L1 ≥ 50% status on improved LC rates from spine SBRT in NSCLC patients.
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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) | 0.001 | 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".