Clinical utility of artificial intelligence–augmented endobronchial ultrasound elastography in lymph node staging for lung cancer
Bibliographic record
Abstract
Objective: Endobronchial ultrasound elastography produces a color map of mediastinal lymph nodes, with the color blue (level 60) indicating stiffness. Our pilot study demonstrated that predominantly blue lymph nodes, with a stiffness area ratio greater than 0.496, are likely malignant. This large-scale study aims to validate this stiffness area ratio compared with pathology. Methods: This is a single-center prospective clinical trial where B-mode ultrasound and endobronchial ultrasound elastography lymph node images were collected from patients undergoing endobronchial ultrasound transbronchial needle aspiration for suspected or diagnosed non-small cell lung cancer. Images were fed to a trained deep neural network algorithm (NeuralSeg), which segmented the lymph nodes, identified the percent of lymph node area above the color blue threshold of level 60, and assigned a malignant label to lymph nodes with a stiffness area ratio above 0.496. Diagnostic statistics and receiver operating characteristic analyses were conducted. NeuralSeg predictions were compared with pathology. Results: B-mode ultrasound and endobronchial ultrasound elastography lymph node images (n = 210) were collected from 124 enrolled patients. Only lymph nodes with conclusive pathology results (n = 187) were analyzed. NeuralSeg was able to predict 98 of 143 true negatives and 34 of 44 true positives, resulting in an overall accuracy of 70.59% (95% CI, 63.50-77.01), sensitivity of 43.04% (95% CI, 31.94-54.67), specificity of 90.74% (95% CI, 83.63-95.47), positive predictive value of 77.27% (95% CI, 64.13-86.60), negative predictive value of 68.53% (95% CI, 64.05-72.70), and area under the curve of 0.820 (95% CI, 0.758-0.883). Conclusions: NeuralSeg was able to predict nodal malignancy based on endobronchial ultrasound elastography lymph node images with high area under the receiver operating characteristic curve and specificity. This technology should be refined further by testing its validity and applicability through a larger dataset in a multicenter trial.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".