Perioperative Chemo-Immunotherapy in Non-Oncogene-Addicted Resectable Non-Small Cell Lung Cancer (NSCLC): Italian Expert Panel Meeting
Bibliographic record
Abstract
BACKGROUND: Immunotherapy (IO)-based strategies have been demonstrated to significantly prolong survival in the perioperative setting of non-oncogene-addicted non-small cell lung cancer (NSCLC). The adoption of such strategies in clinical practice depends on heterogeneous regulatory approvals and on the agreement between medical oncologists and thoracic surgeons on patients' selection. METHODS: An Expert Panel Meeting of medical oncologists and thoracic surgeons was held virtually by the Italian Association of Thoracic Oncology (AIOT) to discuss results of pivotal clinical trials with perioperative chemo-immunotherapy and reach agreement on open issues for the topic, formulating specific statements based on initially proposed discussion questions. RESULTS: Overall, panelists found agreement on seven statements. With regard to tissue and biomarker analysis, the role of increasing PD-L1 expression in predicting IO efficacy was recognized, whereas ctDNA and pCR were mainly attributed a prognostic role, in the absence of dedicated studies. The panelists acknowledged direct relationship between the benefit of neoadjuvant chemo-immunotherapy approaches and the local burden of disease/mediastinal node involvement, supporting the inclusion of these factors, together with PD-L1, in selecting upfront surgery or induction treatment. The panelists agreed that the current literature data do not answer the issue of assessing the role of the adjuvant phase within a perioperative treatment strategy. Surgical considerations on the role of pneumonectomy and other approaches were also discussed. CONCLUSIONS: This experience highlights the importance of a synergistic approach between oncologists and surgeons to leverage the unmet needs in translating results of IO-perioperative clinical trials into clinical practice in patients with resectable NSCLC.
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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 | 0.035 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| 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".