Rapidly Changing Treatment Algorithms for Metastatic Nonsquamous Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Background: The treatment paradigm for metastatic nonsquamous non-small-cell lung cancer (NSCLC) continues to change. Algorithms published only 6 months ago are outdated today and are dramatically different from those published a few years ago. New driver mutations continue to be identified, and the development of therapies to inhibit oncogenic addiction is ongoing. Patient survival is improving as treatments become more personalized and effective. Methods: This review looks at the outcomes of recent trials and discusses treatment options for patients with metastatic NSCLC of nonsquamous histology. Algorithms continue to change quickly, and an attempt is made to keep the paradigm current and applicable into the near future. Results: Treatment algorithms for nsclc tumours with EGFR mutations, ALK rearrangements, and ROS1 rearrangements, and for wild-type tumours are presented. A future algorithm based on new immunotherapy data is proposed. The treatment algorithm for EGFR mutation is changing with the proven efficacy of osimertinib for the acquired T790M mutation. All patients taking first- or second-generation epidermal growth factor receptor tyrosine kinase inhibitors must be tested. Conclusions: The treatment algorithm for ALK rearrangement has changed with the proven superiority of alectinib compared with crizotinib in the first-line setting. The approval of crizotinib for ROS1 rearrangements now means that patients also must be tested for that mutation. The biomarker for checkpoint inhibitors continues to be PD-L1 by immunohistochemistry stain, but whether testing will be necessary for patient selection if chemotherapy combinations are implemented will be determined soon.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".