<b><i>Background:</i></b> Endobronchial ultrasound (EBUS) imaging is valuable in diagnosing intrathoracic lymph nodes (LNs), but there has been little analysis of multimodal imaging. This study aimed to comprehensively compare the diagnostic performance of single and multimodal combinations of EBUS imaging in differentiating benign and malignant intrathoracic LNs. <b><i>Methods:</i></b> Subjects from July 2018 to June 2019 were consecutively enrolled in the model group and July 2019 to August 2019 in the validation group. Sonographic features of three EBUS modes were analysed in the model group for the identification of malignant LNs from benign LNs. The validation group was used to verify the diagnostic efficiency of single and multimodal diagnostic methods built in the model group. <b><i>Results:</i></b> 373 LNs (215 malignant and 158 benign) from 335 subjects and 138 LNs (79 malignant and 59 benign) from 116 subjects were analysed in the model and validation groups, respectively. For single mode, elastography had the best diagnostic value, followed by grayscale and Doppler. The corresponding accuracies in the validation group were 83.3%, 76.8%, and 71.0%, respectively. Grayscale with elastography had the best diagnostic efficiency of multimodal methods. When at least two of the three features (absence of central hilar structure, heterogeneity, and qualitative elastography score 4–5) were positive, the sensitivity, specificity, and accuracy in the validation group were 88.6%, 78.0%, and 84.1%, respectively. <b><i>Conclusions:</i></b> In both model and validation groups, elastography performed the best in single EBUS modes, as well as grayscale combined with elastography in multimodal imaging. Elastography alone or combined with grayscale are feasible to help predict intrathoracic benign and malignant LNs.
Deep learning with convex probe endobronchial ultrasound multimodal imaging: A validated tool for automated intrathoracic lymph nodes diagnosis
