The Computed Tomography Findings and Follow-up Course of Pulmonary Nocardiosis.

OBJECTIVE: Our study aimed to elucidate the computed tomography (CT) features and follow-up course of pulmonary nocardiosis patients to improve the understanding and diagnostic accuracy of this disease. METHODS: The chest CT findings and clinical data of patients diagnosed with pulmonary nocardiosis by culture or histopathological examination in our hospital between 2010 and 2019 were retrospectively analyzed. RESULTS: A total of 34 cases of pulmonary nocardiosis were included in our study. Thirteen patients were on long-term immunosuppressant therapy, among whom 6 had disseminated nocardiosis. Among the immunocompetent patients, 16 had chronic lung diseases or a history of trauma. Multiple or solitary nodules represented the most common CT feature (n = 32, 94.12%), followed by ground-glass opacities (n = 26, 76.47%), patchy consolidations (n = 25, 73.53%), cavitations (n = 18, 52.94%), and masses (n = 11, 32.35%). There were 20 cases (61.76%) with mediastinal and hilar lymphadenopathy, 18 (52.94%) with pleural thickening, 15 (44.12%) with bronchiectasis, and 13 (38.24%) with pleural effusion. Significantly higher rates of cavitations were observed among immunosuppressed patients (85% vs 29%, P = 0.005). At follow-up, 28 patients (82.35%) clinically improved with treatment, while 5 (14.71%) had disease progression, and 1 (2.94%) died. CONCLUSIONS: Chronic structural lung diseases and long-term immunosuppressant use were found as risk factors for pulmonary nocardiosis. While the CT manifestations were highly heterogeneous, clinical suspicion should be raised upon findings of coexisting nodules, patchy consolidations, and cavitations, particularly in the presence of extrapulmonary infections such as those of the brain and subcutaneous tissues. A significant incidence of cavitations may be observed among immunosuppressed patients.

Radiomics Analysis of Multiphasic Computed Tomography Images for Distinguishing High-Risk Thymic Epithelial Tumors From Low-Risk Thymic Epithelial Tumors.

OBJECTIVES: The objective of this study is to preoperatively investigate the value of multiphasic contrast-enhanced computed tomography (CT)-based radiomics signatures for distinguishing high-risk thymic epithelial tumors (HTET) from low-risk thymic epithelial tumors (LTET) compared with conventional CT signatures. MATERIALS AND METHODS: Pathologically confirmed 305 thymic epithelial tumors (TETs), including 147 LTET (Type A/AB/B1) and 158 HTET (Type B2/B3/C), were retrospectively analyzed, and were randomly divided into training (n = 214) and validation cohorts (n = 91). All patients underwent nonenhanced, arterial contrast-enhanced, and venous contrast-enhanced CT analysis. The least absolute shrinkage and selection operator regression with 10-fold cross-validation was performed for radiomic models building, and multivariate logistic regression analysis was performed for radiological and combined models building. The performance of the model was evaluated by the area under the receiver operating characteristic curve (AUC of ROC), and the AUCs were compared using the Delong test. Decision curve analysis was used to evaluate the clinical value of each model. Nomogram and calibration curves were plotted for the combined model. RESULTS: The AUCs for radiological model in the training and validation cohorts were 0.756 and 0.733, respectively. For nonenhanced, arterial contrast-enhanced, venous contrast-enhanced CT and 3-phase images combined radiomics models, the AUCs were 0.940, 0.946, 0.960, and 0.986, respectively, in the training cohort, whereas 0.859, 0.876, 0.930, and 0.923, respectively, in the validation cohort. The combined model, including CT morphology and radiomics signature, showed AUCs of 0.990 and 0.943 in the training and validation cohorts, respectively. Delong test and decision curve analysis showed that the predictive performance and clinical value of the 4 radiomics models and combined model were greater than the radiological model ( P < 0.05). CONCLUSIONS: The combined model, including CT morphology and radiomics signature, greatly improved the predictive performance for distinguishing HTET from LTET. Radiomics texture analysis can be used as a noninvasive method for preoperative prediction of the pathological subtypes of TET.