Clinical utility of artificial intelligence-augmented endobronchial ultrasound elastography in lymph node staging for lung cancer.
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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.
