Outcomes of Patients with Non-Small Cell Lung Cancer and Brain Metastases Treated with the Upfront Single Agent Pembrolizumab: A Retrospective and Multicentric Study of the ESCKEYP GFPC Cohort
Bibliographic record
Abstract
Non-small cell lung cancer (NSCLC) is the most common cause of brain metastasis (BM). Little is known about immune checkpoint inhibitor activity in the central nervous system, especially in patients receiving monotherapy for tumors with a tumor proportion score (TPS) ≥ 50%. This noninterventional, retrospective, multicenter study, conducted with the GFPC, included treatment-naïve patients strongly positive for PD-L1 (TPS ≥ 50%) with BM receiving first-line single-agent pembrolizumab treatment between May 2017 and November 2019. The primary endpoints were centrally reviewed intracranial overall response rates (ORRs), centrally reviewed intracranial progression-free survival (cPFS), extracranial PFS, and overall survival were secondary endpoints. Forty-three patients from five centers were included. Surgical or local radiation therapy was administered to 31 (72%) patients, mostly before initiating ICI therapy (25/31). Among 38/43 (88.4%) evaluable patients, the intracranial ORR was 73%. The median PFS was 8.3 months. The cerebral and extracerebral median PFS times were 9.2 and 5.3 months, respectively. The median OS was 25.5 months. According to multivariate analysis, BM surgery before ICI therapy was the only factor significantly associated with both improved PFS (HR = 0.44) and OS (HR = 0.45). This study revealed the feasibility and outcome of front-line pembrolizumab treatment in this population with BM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".