Molecular Nodal Staging Using miRNA Expression in Lung Cancer Patients by Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration
Bibliographic record
Abstract
BACKGROUND: The limited negative predictive value of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has often been discussed. OBJECTIVE: The aim of this study was to identify a highly sensitive molecular biomarker for lymph node staging by EBUS-TBNA. METHODS: Five microRNAs (miRNAs) (miR-200a, miR-200b, miR-200c, miR-141, and let-7e) were selected as biomarker candidates for the detection of nodal metastasis in a miRNA expression analysis. After having established a cutoff level of expression for each marker to differentiate malignant from benign lymph nodes among surgically dissected lymph nodes, the cutoff level was applied to snap-frozen EBUS-TBNA samples. Archived formalin-fixed paraffin- embedded (FFPE) samples rebiopsied by EBUS-TBNA after induction chemoradiotherapy were also analyzed. RESULTS: The expression of all candidate miRNAs was significantly higher in metastatic lymph nodes than in benign ones (p < 0.05) among the surgical samples. miR-200c showed the highest diagnostic yield, with a sensitivity of 95.4% and a specificity of 100%. When the cutoff value for miR-200c was applied to the snap-frozen EBUS-TBNA samples, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 97.4, 81.8, 95.0, 90.0, and 94.0%, respectively. For restaging FFPE EBUS- TBNA samples, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 100, 60.0, 80.0, 100, and 84.6%, respectively. Among the restaged samples, 4 malignant lymph nodes were false negative by EBUS-TBNA, but they were accurately identified by miR-200c. CONCLUSIONS: miR-200c can be used as a highly sensitive molecular staging biomarker that will enhance nodal staging of 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".