Diagnostic Accuracy of Mediastinal Lymph Node Staging Techniques in the Preoperative Assessment of Nonsmall Cell Lung Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Nonsmall cell lung cancer (NSCLC) treatment is based on an accurate staging. Mediastinal lymph nodes staging has a critical impact on treatment management. METHODS: The objective was to assess the current accuracy of preoperative tools for predicting mediastinal and hilar lymph nodes staging with NSCLC. Retrospective analysis of 997 biopsy-proven NSCLC patients treated at a single academic medical center between January 2006 and April 2012. Mediastinal lymph nodes were evaluated with preoperatively with: computed tomography (CT), positron emission tomography (PET), endobronchial ultrasound-guided fine needle aspiration, and endoscopic ultrasound-guided fine needle aspiration (EUS-FNA). Results are compared with pathologic surgical biopsy. RESULTS: A total of 217 cervical mediastinoscopies, 15 anterior mediastinotomies, and 952 surgical lymphadenectomies were performed. The sensitivity of CT scan for mediastinal lymph nodes detection was 18.9% and PET-CT scan was 33.8%. Specificities were 94.9% and 93.8%, respectively. For hilar lymph nodes detection, CT was less sensitive (17.0% vs. 39.7%); however, more specific (94.7% vs. 80.3%) than PET-CT. Endobronchial ultrasound-guided fine needle aspiration (72.7% sensitivity and 100% specificity) and endoscopic ultrasound-guided fine needle aspiration (51.9% sensitivity and 100% specificity) both demonstrated superior results. CONCLUSIONS: The majority of biopsy-proven mediastinal lymph nodes metastases are not associated with positive results on preoperative CT or PET. CT and PET have low positive predictive value for mediastinal lymph node. This study supports the routine utilization of invasive mediastinal lymph nodes staging in NSCLC, especially for patients with tumors of >4 cm diameter, regardless of CT or PET-CT results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".