Differentiation of pulmonary sclerosing pneumocytoma from solid malignant pulmonary nodules by radiomic analysis on multiphasic CT

Purpose To investigate the diagnostic value and feasibility of radiomics-based texture analysis in differentiating pulmonary sclerosing pneumocytoma (PSP) from solid malignant pulmonary nodules (SMPN) on single- and three-phase computed tomography (CT) images. Materials and Methods A total of 25 PSP patients and 35 SMPN patients with pathologically confirmed results were retrospectively included in this study. For each patient, the tumor regions were manually labeled in images acquired at the noncontrast phase (NCP), arterial phase (AP), and venous phase (VP). The least absolute shrinkage and selection operator (LASSO) method was used to select the most useful predictive features extracted from the CT images. The predictive models that discriminate PSP from SMPN based on single-phase CT images (NCP, AP, and VP) or three-phase CT images (Combined model) were developed and validated through fivefold cross-validation using a logistic regression classifier. Model performance was evaluated using receiver operating characteristic (ROC) analysis. The predictive performance was also compared between the Combined model and human readers. Results Four, five, and five features were selected from NCP, AP, and VP CT images for the development of radiomic models, respectively. The NCP, AP, and VP models exhibited areas under the curve (AUCs) of 0.748 (95% confidence interval [CI], 0.620-0.852), 0.749 (95% CI, 0.620-0.852), and 0.790 (95% CI, 0.665-0.884) in the validation dataset, respectively. The Combined model based on three-phase CT images outperformed the NCP, AP, and VP models (all p < 0.05), yielding an AUC of 0.882 (95% CI, 0.773-0.951) in the validation dataset. The Combined model displayed noninferior performance compared to two senior radiologists; however, it outperformed two junior radiologists (p = 0.004 and 0.001, respectively). Conclusion The Combined model based on radiomic features extracted from three-phase CT images achieved radiologist-level performance and could be used as promising noninvasive tool to differentiate PSP from SMPN.