Prognostic Effect of Tumor Lymphocytic Infiltration in Resectable Non–Small-Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: Tumor lymphocytic infiltration (TLI) has differing prognostic value among various cancers. The objective of this study was to assess the effect of TLI in lung cancer. PATIENTS AND METHODS: A discovery set (one trial, n = 824) and a validation set (three trials, n = 984) that evaluated the benefit of platinum-based adjuvant chemotherapy in non-small-cell lung cancer were used as part of the LACE-Bio (Lung Adjuvant Cisplatin Evaluation Biomarker) study. TLI was defined as intense versus nonintense. The main end point was overall survival (OS); secondary end points were disease-free survival (DFS) and specific DFS (SDFS). Hazard ratios (HRs) and 95% CIs associated with TLI were estimated through a multivariable Cox model in both sets. TLI-histology and TLI-treatment interactions were explored in the combined set. RESULTS: Discovery and validation sets with complete data included 783 (409 deaths) and 763 (344 deaths) patients, respectively. Median follow-up was 4.8 and 6 years, respectively. TLI was intense in 11% of patients in the discovery set compared with 6% in the validation set (P < .001). The prognostic value of TLI in the discovery set (OS: HR, 0.56; 95% CI, 0.38 to 0.81; P = .002; DFS: HR, 0.59; 95% CI, 0.42 to 0.83; P = .002; SDFS: HR, 0.56; 95% CI, 0.38 to 0.82; P = .003) was confirmed in the validation set (OS: HR, 0.45; 95% CI, 0.23 to 0.85; P = .01; DFS: HR, 0.44; 95% CI, 0.24 to 0.78; P = .005; SDFS: HR, 0.42; 95% CI, 0.22 to 0.80; P = .008) with no heterogeneity across trials (P ≥ .38 for all end points). No significant predictive effect was observed for TLI (P ≥ .78 for all end points). CONCLUSION: Intense lymphocytic infiltration, found in a minority of tumors, was validated as a favorable prognostic marker for survival in resected non-small-cell lung cancer.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".