Time to Next Treatment Following Sub-Ablative Progression Directed Radiation Therapy for Oligoprogressive Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
We aimed to evaluate whether progression-directed radiation therapy (PDRT) can prolong the initiation of a subsequent systemic therapy regimen in a cohort of patients with oligoprogressive NSCLC. A retrospective analysis was conducted on NSCLC patients who underwent PDRT for extracranial oligoprogressive NSCLC, defined as limited (up to five) progressing lesions following initial complete, partial, or stable response to systemic therapy according to REC1ST 1.1 and/or PERCIST 1.0 criteria. Cox proportional hazard regressions were performed to identify factors influencing time to next treatment (TTNT), which was considered the primary endpoint. Forty patients were analyzed. First, second, and ≥3 lines of systemic therapy were administered in 22 (58.2%), 14 (27.2%), and 4 (14.6%) cases, respectively. The median total dose was 36 Gy (range: 12-60) in five fractions (1-10), with a median biological effective dose for tumor control (BED10) of 52 Gy (26.4-151.2). After a median follow-up of 11 months (2-50), PDRT delayed further systemic therapy in 32 (80.0%) treatments. Median TTNT was not reached at 8 months (1-47) with a one-year Kaplan-Meier estimate of 81.4% (95% CI: 75.0% to 87.8%). No >grade 3 adverse event was observed. On multivariate analysis, patients with ≥3 lines of systemic therapy and/or with larger CTV volumes did not benefit from PDRT. Despite the use of sub-ablative doses, our findings show that PDRT represents an effective, safe, and viable option for oligoprogressive NSCLC. Patients irradiated early during their systemic treatment course, with a low volume of disease and nonmetastatic oligoprogression, could derive substantial benefits from PDRT.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".