Central location and risk of imaging occult mediastinal lymph node involvement in cN0T2-4 non-small cell lung cancer
Bibliographic record
Abstract
Background: Appropriate pre-operative staging is a cornerstone in the treatment of non-small cell lung cancer (NSCLC). Central location and size greater than 3 cm are amongst indications for pre-operative invasive mediastinal staging but the quality of the evidence behind this recommendation is low. Methods: We retrospectively reviewed all cases of cT2-4N0M0 NSCLCL after CT and TEP-CT which underwent surgical resection with lymph node dissection or had a positive invasive pre-operative mediastinal staging in our institution from 2014 to 2018. Results: Three hundred and ten patients met inclusion criteria, 79 (25.5%) central and 231 (74.5%) peripheral tumors. Central tumor location was associated with a higher prevalence of pN2-3 disease (17.7% vs. 6.1%, P<0.001). In a multivariate analysis, central tumor location remained the only factor statistically associated with imaging occult mediastinal disease (OR 3.23, 95% CI: 1.45–7.18). NPV of PET-CT for occult mediastinal disease was 0.83 (95% CI: 0.72–0.90) in central and 0.94 (95% CI: 0.90–0.97) in peripheral tumor. Central location was also associated with a higher prevalence of occult N1 to N3 disease (43.0% vs. 15.2%, P<0.001). Conclusions: This study suggests that invasive mediastinal staging is required in central cT2-4N0 NSCLC but can be questioned in peripheral one, especially in cT2N2 subgroup if the patient is a candidate for lobar resection.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".