The Addition of Atezolizumab to Chemotherapy in Non-Small Cell Lung Cancer: A Trial-Based Review and Meta-Analysis
Bibliographic record
Abstract
Background: Non-small cell lung cancer (NSCLC) stands as one of the most prevalent types of cancer worldwide, driving extensive research in oncologic therapeutic approaches. Atezolizumab, among the treatments under scrutiny, is undergoing evaluation as a potential first-line therapy for NSCLC. This review aims to assess the efficacy of atezolizumab in treating patients with NSCLC and to shed light on the ongoing quest for the most effective treatment. Methods: Multiple scientific databases, including PubMed, Cochrane, and ScienceDirect, were consulted. The literature identification utilized the strategic Boolean term method of keywords relating to "non-small cell lung cancer" and "atezolizumab" to suggest the analyzed population in our review without restricting the potential outcomes. The primary inclusion criterion is clinical studies that attempted to determine the efficacy of atezolizumab in NSCLC patients. Results: We included four trials to be analyzed in the final analysis, which we stratified into the programmed cell death-ligand 1 (PD-L1) expressivity status aside from the pooled intention-to-treat (ITT) population. We found the addition of atezolizumab may significantly improve the overall survival (OS) in the respective arm, remarkably among the high PD-L1 expression group (TC3 or IC3). The result of our meta-analysis presented the pooled OS of 0.79 (0.72, 0.87) in 95% confidence interval (CI) with a P value of < 0.05. Sub-analysis of the PD-L1's expression revealed TC3 population benefits the most (hazard ratio (HR): 0.55, 95% CI (0.42, 0.73)), compared to low (HR: 0.80, 95% CI (0.68, 0.93)) and negative expression (HR: 0.79, 95% CI (0.68, 0.93)); which is statistically meaningful (P < 0.05). Similar result was also observed in progression-free survival (PFS) analysis with the HR value of 0.63 (0.55, 0.72), with P value of < 0.05, favoring atezolizumab arm. Conclusions: Upon examination, the study reveals that the addition of atezolizumab demonstrates notable improvements in both OS and PFS among NSCLC patients. These findings present promising attributes for atezolizumab as a viable treatment for NSCLC. However, it is important to acknowledge that the future holds further revelations in this realm, and more insights are yet to be uncovered.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".