Real-World Effectiveness and Safety of Immune Checkpoint Inhibitors Combined with Chemotherapy in Taiwanese Patients with Extensive-Stage Small Cell Lung Cancer
Bibliographic record
Abstract
Purpose: Extensive-stage small cell lung cancer (ES-SCLC) has poor prognosis. While immune checkpoint inhibitors (ICIs) with chemotherapy show survival benefits in trials, real-world data from Asia are scarce. This study evaluates real-world efficacy and safety of chemotherapy with or without ICIs in Taiwanese patients with ES-SCLC and identifies survival predictors. Materials and Methods: A retrospective cohort study analyzed 114 patients with ES-SCLC treated between 2017 and 2023 at four Taiwanese medical centers. Patients received first-line chemotherapy alone (n = 68) or with ICIs (atezolizumab, durvalumab, pembrolizumab; n = 46). Primary endpoints were overall survival (OS) and progression-free survival (PFS), assessed via Kaplan–Meier methods and Cox regression. Results: Baseline characteristics were comparable, except poorer ECOG performance (≥2) in the chemotherapy group (27% vs. 9%; p = 0.021). IO–chemotherapy significantly improved OS (16.1 vs. 9.4 months; HR = 0.32, 95% CI: 0.20–0.52; p < 0.001) and PFS (7.8 vs. 5.5 months; HR = 0.40, 95% CI: 0.26–0.63; p < 0.001). Multivariate analysis confirmed IO–chemotherapy as an independent positive predictor (OS adjusted HR = 0.25, 95% CI: 0.14–0.44; PFS adjusted HR = 0.37, 95% CI: 0.22–0.61; both p < 0.001). Skin rash was more common with IO–chemotherapy (24% vs. 3%; p < 0.001). Immune-related adverse events (AEs) correlated with improved survival (median OS: 21.4 months with 1–2 AEs, 16.6 months with 3–4 AEs, 12.5 months without AEs). Conclusion: Immunochemotherapy significantly improves survival in Taiwanese patients with ES-SCLC, with manageable toxicity, supporting ICIs’ incorporation into standard treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".