Surgery after neoadjuvant immunotherapy in patients with resectable non-small cell lung cancer
Bibliographic record
Abstract
Abstract: Surgery is the standard of care for patients with operable non-small cell lung cancer (NSCLC). However, as a single modality, surgery for early stage or locally advanced NSCLC remains associated with high rates of local and distant recurrence. The addition of neoadjuvant or adjuvant chemotherapy has modestly improved outcomes. While systemic therapy paired with surgery for other malignancies such as breast cancer have resulted in far better outcomes for equivalent stage designations, outcome improvements for operable NSCLC have lagged in part as a result of trials where adjuvant chemotherapy seemed to incur harm for stage IA patients and only modest survival benefit for stage IB–IIIA patients (AJCC 7th ed.). In recent years, immunotherapy for NSCLC has emerged as a systemic therapy with significant benefit over traditional chemotherapy regimens. These advances with immune checkpoint inhibitors (ICIs) have opened the door to administering peri-operative immunotherapy for operable NSCLC. As a result, a great multitude of studies investigating the use of immunotherapy in combination with surgery for NSCLC as well as several other malignancies have emerged. In this review, we outline the rationale for neoadjuvant immunotherapy in the treatment of operable NSCLC and summarize the available evidence that include preoperative ICI as a single modality or in combination with systemic agents and/or radiotherapy. Further, we summarize how such treatment trajectories open multiple unique windows of opportunity for scientific discovery and potential therapeutic gains for these vulnerable patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".