The effect of tislelizumab on complete and pathological complete response in non-small cell lung cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Immune checkpoint inhibitors have transformed non-small cell lung cancer (NSCLC) treatment, and while overall survival (OS) and progression-free survival (PFS) are well-established, a comprehensive meta-analysis focusing on complete response (CR) and pathological complete response (pCR) with tislelizumab-based therapies in NSCLC is lacking. Methods This systematic review and meta-analysis was conducted following PRISMA guidelines. A thorough literature search was performed across PubMed, Embase, and Web of Science. We included both randomized controlled trials and observational studies of tislelizumab in NSCLC, focusing on extracting data for radiological complete response (CR, based on RECIST 1.1 criteria) and pathological complete response (pCR, defined as absence of residual invasive cancer in resected surgical specimens). Risk of bias was assessed using the Cochrane Collaboration’s tool and the Newcastle-Ottawa Scale. Statistical analyses were performed using the ‘meta’ package in R. 95% confidence intervals (CIs) and odds ratios (ORs) were calculated for CR and pCR, and subgroup analyses were conducted. Results 7 studies were enrolled in the meta-analysis. The results on pCR showed significant heterogeneity (I2 = 92.5%), with a random effects OR of 2.1103 (95% CI: 0.5195 to 8.5727). Subgroup analysis for pCR by disease type revealed a statistically significant difference between NSCLC and SCC only subgroups under the common effect model (p < 0.001). Furthermore, the pCR subgroup analysis by comparator drug showed a statistically significant difference (p < 0.0001) between Pembrolizumab+Chemotherapy (OR 0.6968, 95% CI: 0.3803 to 1.2767) and Chemotherapy alone (OR 7.3123, 95% CI: 2.9204 to 18.3092). For CR, the meta-analysis demonstrated minimal heterogeneity (I2 = 0.0%), yielding a significant random effects OR of 2.6277 (95% CI: 1.2858 to 5.3699). Subgroup analysis for CR comparing tislelizumab plus chemotherapy to chemotherapy alone showed a significant advantage (OR 3.8690, 95% CI: 1.5423 to 9.7059). Conclusion Tislelizumab combined with chemotherapy significantly improves CR rates in NSCLC compared to chemotherapy alone. While pCR data exhibit high heterogeneity, the findings highlight tislelizumab’s role in achieving deep tumor responses.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".