Locally Advanced Non-Small Cell Lung Cancer: Clinical Outcome, Toxicity and Predictive Factors in Patients Treated with Hypofractionated Sequential or Exclusive Radiotherapy
Bibliographic record
Abstract
Background: This study evaluated the outcome, toxicity and predictive factors in patients unfit for concurrent chemo-radiotherapy (CT-RT) treated with hypofractionated sequential CT-RT or exclusive radiotherapy (RT) for locally advanced non-small cell lung cancer (LA-NSCLC). Methods: We included patients affected by LA-NSCLC (stage IIA-IVA) treated with a total dose of 50–60 Gy in 20 fractions. The primary outcomes were local control (LC), distant metastasis-free survival (DMFS), progression-free survival (PFS) and overall survival (OS). Univariate analysis was used to correlate outcomes with prognostic factors. Results: Between 2011 and 2019, 210 patients were treated, 113 (53.8%) with sequential CT-RT and 97 (46.2%) with exclusive RT. After a median follow-up of 15.3 months, 74 patients (35.2%) had a local progression and 133 (63.3%) had a distant progression. The one-, two- and five-year LC were 73.6%, 55.3% and 47.9%, respectively. At the time of analysis, 167 patients (79.5%) died. The one-, two- and five-year OS were 64.7%, 36% and 20%, respectively. PTV volume correlated with PFS (p = 0.001) and LC (p = 0.005). Acute and late toxicity occurred in 82% and 26% of patients. Conclusions: Albeit with the known limitations of a retrospective and heterogeneous study, our work shows that hypofractionated sequential CT-RT or exclusive RT offer a good local control and toxicity profile and a promising survival rate in LA-NSCLC patients unfit for the concurrent CT-RT scheme.
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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 | 0.001 |
| 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".