Multinational validation of distant metastasis velocity as a post-progression prognostic score in patients with oligometastatic cancer treated with metastasis-directed stereotactic body radiotherapy
Bibliographic record
Abstract
BACKGROUND: Distant progression is the predominant failure pattern after metastasis-directed stereotactic body radiotherapy (SBRT) for oligometastatic disease, but prognostic tools to guide post-progression management are lacking. We aimed to validate the prognostic value of distant metastasis velocity (DMV) for overall survival (OS) and widespread failure-free survival (WFFS) after distant progression. METHODS: Two independent international cohorts of patients with extracranial oligometastatic disease (≤5 lesions) who developed distant progression after SBRT were analyzed. The primary outcome was OS; secondary outcome was WFFS in the subgroup of patients with repeat oligometastasis at distant progression. DMV was defined as the number of new or progressing metastases per month after initial metastasis-directed SBRT. RESULTS: Among 563 patients (median age 68 years, 56 % male), DMV stratified prognosis in both cohorts. In the prospective cohort (n = 221), median OS was 35.7 months for patients with DMV ≤ 0.5 metastases/month versus 20.6 months for DMV > 0.5 (P = 0.0001). In the retrospective cohort (n = 342), OS was 32.8 vs. 12.1 months (P < 0.0001). Similar trends were observed for WFFS (prospective cohort, n = 91: 6.8 vs. 3.0 months, p = 0.42; retrospective cohort, n = 341: 18.8 vs. 6.1 months, p < 0.0001). CONCLUSION: Higher DMV was consistently associated with worse outcomes after progression in oligometastatic disease. Its reproducibility across tumor types and independent cohorts supports DMV as a simple, dynamic, and clinically relevant prognostic marker. DMV should be further explored as a component of multimodal prognostic models to refine patient selection and guide post-progression treatment strategies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".