Immunotherapy in Extensive Stage Small-Cell Lung Cancer in First-Line and Second-Line Setting
Bibliographic record
Abstract
OBJECTIVES: Despite a good response to first-line chemotherapy, small-cell lung cancer (SCLC) has high relapse rates and a poor prognosis. We conducted a systematic review and meta-analysis to assess the role of immune checkpoint inhibitors (ICIs) in the treatment of extended stage SCLC (ES-SCLC), in different lines of therapy. METHODS: Medline (PubMed), EMBASE, and Cochrane Library databases between January 2010 and March 2025 and conference proceedings between 2018 and 2025 were searched for RCTs assessing ICIs versus chemotherapy in patients with ES-SCLC. Primary endpoints were overall survival (OS) and progression-free survival (PFS). Secondary endpoints included objective response rate (ORR) and grade 3+ adverse events. Pooled hazard ratios (HR) for OS and PFS were meta-analyzed using the generic inverse variance method, and random-effect models were used to compute pooled estimates. Subgroup analyses compared survival by line of therapy, sex, age, and ECOG status. RESULTS: ICIs decreased risk of death by 19% (HR: 0.81, 95% CI: 0.76-0.86). OS benefit was regardless of age, sex, or ECOG, but only in first-line treatment. ICIs decreased the risk of disease progression by 22% (HR: 0.78, 95% CI: 0.67-0.91), with PFS benefit restricted to first-line treatment with a detrimental effect in the second line. ICIs improved ORR (OR: 0.79, 95% CI: 0.66-0.95), but were associated with increased grade 3+diarrhea (OR: 3.63, 95% CI: 1.46-9.02). CONCLUSIONS: ICIs conferred efficacy benefits and an acceptable safety profile in the treatment of patients with ES-SCLC in the first-line, but should not be used in the second-line as single agents. Biomarkers predicting long-term benefit are needed to further improve outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".