Prediction of Lymph Node Metastasis in Lung Cancer Using Deep Learning of Endobronchial Ultrasound Images With Size on CT and PET‐CT Findings

ABSTRACT Background and Objective Echo features of lymph nodes (LNs) influence target selection during endobronchial ultrasound‐guided transbronchial needle aspiration (EBUS‐TBNA). This study evaluates deep learning's diagnostic capabilities on EBUS images for detecting mediastinal LN metastasis in lung cancer, emphasising the added value of integrating a region of interest (ROI), LN size on CT, and PET‐CT findings. Methods We analysed 2901 EBUS images from 2055 mediastinal LN stations in 1454 lung cancer patients. ResNet18‐based deep learning models were developed to classify images of true positive malignant and true negative benign LNs diagnosed by EBUS‐TBNA using different inputs: original images, ROI images, and CT size and PET‐CT data. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and other diagnostic metrics. Results The model using only original EBUS images showed the lowest AUROC (0.870) and accuracy (80.7%) in classifying LN images. Adding ROI information slightly increased the AUROC (0.896) without a significant difference ( p = 0.110). Further adding CT size resulted in a minimal change in AUROC (0.897), while adding PET‐CT (original + ROI + PET‐CT) showed a significant improvement (0.912, p = 0.008 vs. original; p = 0.002 vs. original + ROI + CT size). The model combining original and ROI EBUS images with CT size and PET‐CT findings achieved the highest AUROC (0.914, p = 0.005 vs. original; p = 0.018 vs. original + ROI + PET‐CT) and accuracy (82.3%). Conclusion Integrating an ROI, LN size on CT, and PET‐CT findings into the deep learning analysis of EBUS images significantly enhances the diagnostic capability of models for detecting mediastinal LN metastasis in lung cancer, with the integration of PET‐CT data having a substantial impact.