Surgery Versus Stereotactic Body Radiotherapy for Early-Stage Non-small Cell Lung Cancer (NSCLC): A Comprehensive Review of Survival and Local Control Outcomes
Bibliographic record
Abstract
This systematic review and meta-analysis aimed to compare the efficacy of stereotactic body radiotherapy (SBRT) and surgical resection in early-stage non-small cell lung cancer (NSCLC), focusing on overall survival (OS), cancer-specific survival (CSS), and local control (LC). A comprehensive literature search was conducted using PubMed, Cochrane Library, ScienceDirect, and Google Scholar, and eligible studies were selected according to PRISMA guidelines. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated for OS, CSS, and LC using fixed- or random-effects models, and the Newcastle-Ottawa Scale was used to assess study quality. A total of 41 studies involving 88,228 patients (58,366 treated with surgery and 29,862 with SBRT) were included. Surgical resection was significantly associated with improved three-year OS (HR = 1.39; 95% CI: 1.25-1.55; p < 0.00001) compared to SBRT. Subgroup analysis revealed greater survival benefits with lobectomy (HR = 1.50; p < 0.00001) than sublobar resection (HR = 1.27; p = 0.002) or mixed approaches (HR = 1.39; p = 0.007). CSS also favored surgery (HR = 1.22; p = 0.006), particularly lobectomy (HR = 1.46; p = 0.002). LC was comparable between SBRT and surgery (HR = 0.92; p = 0.06), although lobectomy showed a slight advantage (HR = 0.92; p = 0.04). These findings suggest that surgical resection, especially lobectomy, offers superior OS and CSS compared to SBRT in early-stage NSCLC, while LC outcomes are generally equivalent. SBRT remains an effective alternative for medically inoperable patients; however, in operable candidates, surgery should be considered the preferred approach to maximize long-term outcomes.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".