Late metastatic presentation is associated with improved survival and delayed wide‐spread progression after ablative stereotactic body radiotherapy for oligometastasis
Bibliographic record
Abstract
BACKGROUND: Stereotactic body radiotherapy (SBRT) is increasingly used to treat oligometastatic disease (OMD), but the effect of metastasis timing on patient outcomes remains uncertain. METHODS: An international database of patients with OMD treated with SBRT was assembled with rigorous quality assurance. Early versus late metastases were defined as those diagnosed ≤24 versus >24 months from the primary tumor. Overall survival (OS), progression-free survival (PFS), and incidences of wide-spread progression (WSP) were estimated using multivariable Cox proportional hazard models stratified by primary tumor types. RESULTS: The database consists of 1033 patients with median follow-up of 24.1 months (0.3-104.7). Late metastatic presentation (N = 427) was associated with improved OS compared to early metastasis (median survival 53.6 vs. 33.0 months, hazard ratio [HR] 0.59, 95% confidence interval [CI]: 0.47-0.72, p < 0.0001). Patients with non-small cell lung cancer (NSCLC, N = 255, HR 0.49, 95% CI: 0.33-0.74, p = 0.0005) and colorectal cancer (N = 235, HR 0.50, 95% CI: 0.30-0.84, p = 0.008) had better OS if presenting with late metastasis. Late metastasis correlated with longer PFS (median 17.1 vs. 9.0 months, HR 0.71, 95% CI: 0.61-0.83, p < 0.0001) and lower 2-year incidence of WSP (26.1% vs. 43.6%, HR 0.60, 95% CI: 0.49-0.74, p < 0.0001). Fewer WSP were observed in patients with NSCLC (HR 0.52, 95% CI: 0.33-0.83, p = 0.006) and kidney cancer (N = 63, HR 0.37, 95% CI: 0.14-0.97, p = 0.044) with late metastases. Across cancer types, greater SBRT target size was a significant predictor for worse OS. CONCLUSION: Late metastatic presentation is associated with improved survival and delayed progression in patients with OMD treated with SBRT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".