Docetaxel and Ramucirumab as Subsequent Treatment After First-Line Immunotherapy-Based Treatment for Metastatic Non-Small-Cell Lung Cancer: A Retrospective Study and Literature Review
Bibliographic record
Abstract
Background: A combination of docetaxel and ramucirumab represents a standard of care in second-line treatment for patients with advanced NSCLC. Evidence of the regimen’s efficacy is based on the results of the REVEL trial conducted in the pre-immunotherapy (immune checkpoint inhibitors–ICIs) era. Given the lack of randomized trials after the use of ICIs in front-line therapy, a question remains regarding the impact of the combination when disease progresses after ICI-based therapy. Methods: From 1 January 2018 to 31 December 2024, 55 patients from three oncology centers who had documented progression on ICI-based therapy subsequently received docetaxel/ramucirumab, and we reviewed their outcomes. Results: The studied group’s median progression-free survival (PFS) was 5.8 months, while the median overall survival (OS) was 11.1 months. The objective response rate (ORR) and disease control rate (DCR) were 42% and 76%, respectively. Patients who had received ICI-based therapy for ≥6 months had a numerically better median PFS and statistically significant OS compared to those who had experienced progression on ICI-based therapy in <6 months. Regarding adverse events (AEs), 92.7% of patients experienced Grade 1–2 AEs, whereas 54.5% experienced Grade ≥ 3 AEs. One death due to GI bleeding was also recorded. Conclusion: Docetaxel/ramucirumab is an acceptable regimen for patients progressing on first-line ICI-based therapies. Our results are in concordance with the REVEL study and other retrospective studies of this combination after ICIs.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".