Outcomes of Clinical Trials on the Roles of Immune Checkpoint Inhibitors in the Management of Lung Cancers: A Comprehensive Review
Bibliographic record
Abstract
Lung cancer is a deadly clinical condition that necessitates critical clinical intervention for its management. This comprehensive review summarizes findings from clinical trials that assessed the efficacy and safety of immune checkpoint inhibitors (ICIs) for the management of lung cancer. Scientific databases were explored for reports from clinical trials on ICI roles in lung cancer management. Small cell lung cancer mostly develops from chronic smoking, and it is refractory to treatment, resulting in complications and death. On the other hand, many treatment modalities are available for non-small-cell lung cancer, including surgery, radiotherapy, and systemic therapy. ICIs, classified into programmed death-1, programmed death ligand 1 (PD-L1), and cytotoxic T-lymphocyte antigen 4 inhibitors, are used either alone or in combination with other targeted therapies or chemotherapy perioperatively to improve surgical outcomes for resectable lung cancer. Additionally, ICIs are also used in advanced unresectable metastatic lung cancer to reduce tumor growth or as palliative treatment to prolong survival and improve patients’ quality of life. ICI monotherapy, compared to placebo and platinum-based chemotherapy, elicited positive clinical outcomes in patients with lung cancer, resulting in longer overall survival and progression-free survival with tolerable side effect profiles. Similarly, combination therapy comprising ICIs alongside tyrosine kinase inhibitors, platinum-based chemotherapy, or radiotherapy resulted in superior efficacy compared to drug combinations without ICIs but with higher incidences of adverse events. Notably, higher tumor expression of PD-L1 improved clinical response to 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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".