Contemporary real-world radiotherapy outcomes of unresected locally advanced non-small cell lung cancer
Bibliographic record
Abstract
Background: Radiotherapy (RT) is used as monotherapy in poor performance patients with unresected locally advanced non-small cell lung cancer (LA-NSCLC), but their outcomes are not well-described. As novel therapies are increasingly considered in this space, it is important to understand contemporary outcomes of RT alone. Here, in this retrospective cohort study we analyzed LA-NSCLC outcomes of RT alone in Ontario, Canada, and contrasted them against those of standard of care (SoC) treatment of concurrent chemo-radiotherapy (cCRT). Methods: Ontario provincial databases were searched through the Institute of Clinical Evaluative Sciences (IC/ES) for stage III NSCLC patients diagnosed between 2007 and 2017. Surgical patients were excluded, and all patients that received RT without or with chemotherapy were selected. Patients were divided in groups of RT dose received (<40 Gy, 40–55.9 Gy, and ≥56 Gy) and whether they underwent diagnostic 18F-deoxy-glucose (FDG)-positron emission tomography (PET). Results: Five thousand five hundred and seventy-seven stage III patients that received chest RT without surgery between January 2007 and March 2017 were included in this analysis. Within this group, 39.8% (2,225) received RT alone, 47.4% (2,645) cCRT and 12.6% (707) received sequential chemo-radiotherapy (sCRT). Median OS with RT alone in three dose groups <40/40–55.9/≥56 Gy was 7.2, 8.5 and 13.3 months compared to 16.5, 15.8 and 22 months for cCRT patients. Higher RT dose and PET utilization were independently associated with improved survival in multivariate analysis. Conclusions: Radiation monotherapy remains a widely used treatment modality in LA-NSCLC. RT dose and utilization of FDG-PET imaging are associated with improved survival in this group. These findings help improve clinical decision making and serve as basis for future trials.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".