PET-CT compared to invasive mediastinal staging in non-small cell lung cancer (NSCLC)
Bibliographic record
Abstract
7575 Background: In patients with NSCLC, preoperative staging tests including mediastinoscopy (M) are important in defining which patients are surgical candidates. 18FDG PET-CT is useful in identifying patients with mediastinal disease not evident by CT. Alternatively, M may not be required if PET-CT is negative. We have previously reported reduced rates of unnecessary thoracotomy (T) in the PET-CT arm of a trial which compared staging with PET-CT versus conventional imaging (bone scan and CT liver and adrenals) in patients with clinical stage I, II, or IIIA NSCLC being considered for surgery (J Clin Oncol 26 May 20 suppl: abstr 7502). Methods: In this analysis, we determined the accuracy of PET-CT in mediastinal staging compared to invasive surgical staging either by M alone or by M and T. Patients in the PET-CT arm had invasive mediastinal staging either by M or mediastinal nodal sampling at T. PET-CT was considered positive if N2 or N3 nodes exhibited increased 18FDG uptake. Results: M was performed in 81 of 143 patients in the PET-CT arm; the remainder had mediastinal nodal staging at T. Combining M with T, the sensitivity and specificity of PET-CT were 0.70 [95% CI: 0.48–0.85] and 0.94 [95%CI: 0.89–0.97], respectively. Of 21 patients with a positive PET-CT, 7 did not have tumor. The positive predictive value (PPV) and negative predictive value (NPV) were 0.67 [95% CI: 0.45–0.83] and 0.95 [95% CI: 0.90–0.98], respectively. The results for PET-CT versus M alone were: sensitivity, 1.0 [95% CI: 0.76–1.0]; specificity, 0.88 [95%CI: 0.79–0.94]; PPV, 0.60 [95%CI: 0.39–0.78]; NPV, 1.0 [95% CI: 0.94–1.0]. Based on PET-CT alone, 7 patients would have been denied T if PET-CT abnormalities had not been evaluated with invasive mediastinal staging. Conclusions: Mediastinal abnormalities on PET-CT should be confirmed by invasive mediastinal staging because of the risk of a false positive test. Patients should not be denied potentially curative therapy based on PET-CT alone. If PET-CT is negative in the mediastinum, the likelihood of occult metastatic disease in the mediastinum is very low and invasive staging may not be required depending on the clinical context. No significant financial relationships to disclose.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".