Population-based analysis of curative therapies in stage II non-small cell lung cancer: the role of radiotherapy in medically inoperable patients
Bibliographic record
Abstract
OBJECTIVES: Curative intent therapy of stage II NSCLC may include surgical resection or definitive radiotherapy. Primary management with surgery or radiotherapy may be influenced by patient and disease characteristics. We sought to perform a comparison of patients receiving surgery or radical radiation therapy as their curative treatment, and explore the impact of known prognostic factors on outcome. MATERIALS AND METHODS: A retrospective review was completed of all patients with stage II NSCLC referred to the BC Cancer Agency from 2005 to 2012. Cases were filtered to identify those receiving curative intent therapy including surgery or radiotherapy. Information was collected on known prognostic and predictive factors. The primary outcome measure was overall survival. We compared survival among patients receiving curative intent radiotherapy versus surgical intervention. RESULTS: A total of 535 patients were referred. Of these, 245 (46%) received curative intent surgery, 132 (25%) curative intent radiotherapy, and 158 (30%) did not receive curative therapy. There were significant differences between cohorts with respect to median age, histology, ECOG PS, smoking status, and weight loss. Median OS was significantly different between cohorts: 61.4 m surgery, 26.5 m curative RT, and 13.1 m non-curative therapy. In a case-matched analysis, median OS remained superior for surgery at 101.6 m vs 28.1 m for curative RT. In a multivariate analysis, ECOG PS, weight loss, and treatment cohort all influenced survival. Among patients receiving curative intent radiotherapy, the use of concurrent chemotherapy and RT dose > = 60Gy were associated with improved outcomes. CONCLUSIONS: Among patients with stage II NSCLC, many are unable to undergo standard of care surgical resection. Radiotherapy provides an inferior yet still curative option in the management of inoperable patients. Further work is needed to optimize outcomes in this population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".