Perioperative Treatment Strategies for Lung Cancer in 2025: A Paradigm Shift
Bibliographic record
Abstract
Perioperative management of resectable non-small cell lung cancer (NSCLC) has evolved significantly with the integration of immune checkpoint inhibitors and targeted therapies. This review synthesizes current evidence from key clinical trials, highlighting the improved survival outcomes achieved with neoadjuvant and perioperative chemoimmunotherapy in oncogene-wildtype NSCLC, as well as adjuvant tyrosine kinase inhibitors (TKIs) in epidermal growth factor receptor (EGFR)‑ and anaplastic lymphoma kinase (ALK)-altered tumours. While neoadjuvant immunotherapy has demonstrated high pathological response rates and long-term survival benefits, perioperative strategies may offer added value in selected subgroups. The ADAURA and ALINA trials have established adjuvant osimertinib and alectinib as new standards of care in oncogene-driven disease. Unresolved questions remain regarding optimal treatment sequencing, duration, and patient selection. Emerging tools such as circulating tumour DNA and artificial intelligence hold promise for refining risk stratification and guiding individualized treatment approaches.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".