CLINICAL OUTCOMES AND PROGNOSTIC FACTORS OF STEREOTACTIC BODY RADIATION THERAPY (SBRT) FOR CENTRAL AND ULTRACENTRAL EARLY-STAGE LUNG CANCER
Bibliographic record
Abstract
Stereotactic body radiation therapy (SBRT) is a standard treatment for inoperable early-stage non-small cell lung cancer (NSCLC). However, the optimal management of central and ultracentral tumours remains a challenge due to proximity to critical structures and potential toxicity. This study evaluates local control (LC), overall survival (OS), and progression-free survival (PFS) in patients with centrally and ultracentrally located tumours and assesses the impact of tumour size and radiation dose. A retrospective analysis was conducted on 103 patients (104 lesions) treated with SBRT. The median age was 76 years (range: 49-90), with a high prevalence of smoking (90.3%) and COPD (78.7%). Tumours were evenly distributed between central (50.5%) and ultracentral (49.5%) locations. Patients received either 50 Gy in 5 fractions (36.9%) or 60 Gy in 8 fractions (63.1%). Kaplan-Meier survival analyses and competing risk models were used to evaluate outcomes based on tumour location, size, and radiation dose. The 1-, 3-, and 5-year OS rates were 90.1%, 55.6%, and 31.4%, respectively. LC remained high, with 1-, 3-, and 5-year rates of 96.7%, 84.6%, and 77.8%. PFS at 1, 3, and 5 years was 86.9%, 60.5%, and 54.1%. No significant differences in LC (p=0.69), OS (p=0.46), or PFS (p=0.43) were observed between central and ultracentral tumours. However, ultracentral tumours showed a trend toward worse OS and PFS. Radiation dose (p=0.87) and tumour size (p=0.66) did not significantly impact outcomes. SBRT provides durable LC and acceptable survival for central and ultracentral lung tumours. While no significant differences were observed, ultracentral tumours demonstrated numerically worse survival, warranting further investigation to optimize treatment strategies and mitigate toxicity risks.
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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 | 0.003 |
| 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".