BIOMARKER STATUS PREDICTS FOR LOCAL CONTROL (LC) FOLLOWING SPINE STEREOTACTIC BODY RADIOTHERAPY (SBRT) IN NON-SMALL CELL LUNG CANCER (NSCLC) PATIENTS WITH SPINAL METASTASES
Bibliographic record
Abstract
Abstract Report of spine SBRT outcomes in NSCLC patients and impact of death-ligand 1 (PD-L1) status and epidermal growth factor (EGFR) mutation on LC. METHODS: 165 patients and 389 spinal segments were retrospectively reviewed. Primary endpoint was LF and secondary outcomes included overall survival (OS) and vertebral compression fracture (VCF) rates. RESULTS: Median follow-up, OS and age was 13 months (range, 0.5-95 months), 18.4 months (95% CI 11.4-24.6), and 67 years (range, 28.2-89.9) respectively. 52% were female and 76% had adenocarcinoma. 29% had an EGFR mutation, 16% were PD-L1 ≥ 50%, 20% PD-L1 1-49% and 35% PD-L1 <1%. Of 389 segments, 79% were denovo. At baseline, 35% had VCF, 27% epidural disease and 27% paraspinal extension. 61% were treated with 24 or 28 Gy in 2 SBRT fractions. LF cumulative incidence (CI) at 2-years was 25.4% (95% CI 20.9%-30%). EGFR positivity (p<0.0001), PD-L1≥50% (p=0.013) and treatment with IO within 1 month of SBRT (p=0.004) predicted for LC on multivariable analysis (MVA). The 2-year LF rate in EGFR-positive vs. negative patients were 17.7% vs. 28.8%, and in those PD-L1 ≥50% vs PD-L1<50% were 7.8% vs. 38.1%. CI of VCF at 2-years were 8.8% (95% CI 6.1-12.0%). Prior SBRT to the same segment (P<0.0001) and baseline VCF (p<0.0001) were predictors on MVA. CONCLUSION: We identify predictability of EGFR mutation, PD-L1 ≥50% and peri-SBRT IO on LC following spine SBRT in NSCLC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".