Diagnostic value of the dual-modal imaging radiomics model for subpleural pulmonary lesions

• Published in: European journal of radiology 2023-09, Vol.166, p.111000-111000, Article 111000
• Main Authors: Tang, Wei, Zhang, Ying, Yu, Xiu-Lei, Zhang, Wen-zhi, Chen, Pei-Jun, Ying, Wang, Zhang, Xu, Yang, Gao-Yi
• Format: Article
• Language: English
• Subjects: Area Under Curve; Contrast-enhanced ultrasound; Humans; Pulmonary lesion; Radiomics; Retrospective Studies; ROC Curve; Tuberculosis; Tuberculosis, Extrapulmonary - diagnostic imaging; Tuberculosis, Pulmonary - diagnostic imaging; Ultrasound
• ISSN: 0720-048X; 1872-7727
• DOI: 10.1016/j.ejrad.2023.111000

•Ultrasound radiomics model showed good efficiency in distinguishing tuberculous from nontuberculous peripheral pulmonary lesions.•Dual-modality radiomics model exhibits higher diagnostic accuracy than the single-modality radiomics model.•Dual-modality radiomics model provides a new method for timely pulmonary tuberculosis diagnosis. To investigate the clinical value of the radiomics model of grayscale ultrasound (GUS) and contrast-enhanced ultrasound (CEUS) to diagnosis subpleural pulmonary tuberculosis and nonpulmonary tuberculosis based on GUS and CEUS images. This study included 221 patients with 228 lesions diagnosed using the composite reference standard. The patients were randomly divided into training (n = 183) and test (n = 45) cohorts in an 8:2 ratio. The regions of interest of the GUS and CEUS images were manually segmented to extract the radiomic features. The GUS, CEUS and GUS+CEUS radiomics models were constructed via the multistep selection of highly correlated features. Receiver operating characteristic curves of the different models were plotted, and the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value (NPV) of the different models were compared. Following Least Absolute Shrinkage and Selection Operator dimension reduction we selected 4, 9, and 11 features to construct the GUS, CEUS, and GUS+CEUS radiomics models, respectively. The AUC values of the three groups in the test cohort were 0.689, 0.748 and 0.779, respectively, and they did not differ significantly. In the test cohort, the GUS+CEUS radiomics model exhibited the highest AUC (0.779), accuracy (75.56%), and NPV (68.7%) of the three models. The GUS+CEUS radiomics model possesses good clinical value in diagnosing pulmonary tuberculosis.