Immunotherapy in Early-Stage Non-Small Cell Lung Cancer (NSCLC): Current Evidence and Perspectives
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer deaths in the world. Surgery is the most potentially curative therapeutic option for patients with early-stage non-small cell lung cancer (NSCLC). The five-year survival for these patients remains poor and variable, depending on the stage of disease at diagnosis, and the risk of recurrence following tumor resection is high. During the last 20 years, there has been a modest improvement in the therapeutic strategies for resectable NSCLC. Immune checkpoint inhibitors (ICIs), alone or in combination with chemotherapy, have become the cornerstone for the treatment of metastatic NSCLC patients. Recently, their clinical development has been shifted in the neoadjuvant and adjuvant settings where they have demonstrated remarkable efficacy, leading to improved clinical outcomes. Based on the positive results from phase III trials, ICIs have become a therapeutic option in neoadjuvant and adjuvant settings. On October 2021 the Food and Drug Administration (FDA) approved atezolizumab as an adjuvant treatment following surgery and platinum-based chemotherapy for patients with NSCLC whose tumors express PD-L1 ≥ 1%. In March 2022, nivolumab in combination with platinum-doublet chemotherapy was approved for adult patients with resectable NSCLC in the neoadjuvant setting. The current review provides an updated overview of the clinical trials exploring the role of immunotherapy in patients with early-stage NSCLC, focusing on the biological rationale for their use in the perioperative setting. We will also discuss the role of potential predictive biomarkers to personalize therapy and optimize the incorporation of immunotherapy into the multimodality management of stage I-III NSCLC.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".