Re-Irradiation for Locally Recurrent Lung Cancer: A Single Center Retrospective Analysis
Bibliographic record
Abstract
The treatment of locally recurrent lung cancer is a major challenge for radiation-oncologists, especially with data on high-dose reirradiation being limited to small retrospective studies. The aim of the present study is to assess overall survival (OS) for patients with locally recurrent lung cancer after high-dose thoracic reirradiation. Thirty-nine patients who were re-irradiated for lung cancer relapse between October 2013 and February 2019 were eligible for the current retrospective analysis. All patients were re-irradiated with curative intent for in-field tumor recurrence. The diagnostic work-up included a mandatory 18F-FDG-PET-CT scan and—if possible—histological verification. The ECOG was ≤2, and the interval between initial and second radiation was at least nine months. Thirty patients (77%) had non-small cell lung cancer (NSCLC), eight (20%) had small cell lung cancer (SCLC), and in one patient (3%) histological confirmation could not be obtained. More than half of the patients (20/39, 51%) received re-treatment with dose differentiated accelerated re-irradiation (DART) at a median interval of 20.5 months (range: 6–145.3 months) after the initial radiation course. A cumulative EQD2 of 131 Gy (range: 77–211 Gy) in a median PTV of 46 mL (range: 4–541 mL) was delivered. Patients with SCLC had a 3 mL larger median re-irradiation volume (48 mL, range: 9–541) compared to NSCLC patients (45 mL, range: 4–239). The median cumulative EQD2 delivered in SCLC patients was 84 Gy (range: 77–193 Gy), while NSCLC patients received a median cumulative EQD2 of 135 Gy (range: 98–211 Gy). The median OS was 18.4 months (range: 0.6–64 months), with tumor volume being the only predictor (p < 0.000; HR 1.007; 95%-CI: 1.003–1.012). In terms of toxicity, 17.9% acute and 2.6% late side effects were observed, with a toxicity grade >3 occurring in only one patient. Thoracic high dose reirradiation plays a significant role in prolonging survival, especially in patients with small tumor volume at recurrence.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".