Concurrent Chemoradiation With or Without Durvalumab in Elderly Patients With Unresectable Stage III NSCLC: Safety and Efficacy
Bibliographic record
Abstract
Introduction The addition of durvalumab after chemoradiation therapy (CRT) in unresectable stage III NSCLC significantly improves survival. The benefit of this approach in elderly patients is controversial given the toxicity associated with CRT and, thus, may be underutilized. We sought to investigate the outcomes of elderly patients treated with CRT without or without durvalumab at our center. Methods We reviewed all stage III patients with NSCLC treated with CRT between 2018 and 2020. Patients were analyzed on the basis of age: less than 70 years and 70 years and older. The end points evaluated were treatment patterns, toxicity, progression-free survival, and overall survival. Results The baseline characteristics including Eastern Cooperative Oncology Group performance status and comorbidities were similar among the 115 patients (44 elderly, 71 young). Completion rates of CRT (100%, 97%) and chemotherapy dose intensity (97%, 97%) were high in elderly and young patients, respectively. There was a trend toward increased hospitalizations in elderly patients because of infections (27% versus 13%, p = 0.08). Of those who did not have primary progression after CRT, 78% of eldery and 81% of young patients received durvalumab. The incidence of grade 3 or higher immune-related adverse events was 9% in elderly and 6% in young patients ( p = 0.67). The median progression-free survival was similar (15.6 versus 10.5 mo, p = 0.10), even after adjusting for comorbidities (hazard ratio = 0.6, p = 0.09). The 12-month overall survival rates were 78% in the elderly and 76% in young patients ( p = 0.98). Conclusions Well-selected elderly patients can be treated safely with CRT followed by durvalumab with similar survival benefits compared with their younger counterparts. We would advocate for the referral of all elderly patients for oncologic assessment to avoid undertreatment.
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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) | 0.000 | 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".