Differentiating nontuberculous mycobacterium pulmonary disease from pulmonary tuberculosis through the analysis of the cavity features in CT images using radiomics.

OBJECTIVE: To differentiate nontuberculous mycobacteria (NTM) pulmonary diseases from pulmonary tuberculosis (PTB) by analyzing the CT radiomics features of their cavity. METHODS: 73 patients of NTM pulmonary diseases and 69 patients of PTB with the cavity in Shandong Province Chest Hospital and Qilu Hospital of Shandong University were retrospectively analyzed. 20 patients of NTM pulmonary diseases and 20 patients of PTB with the cavity in Jinan Infectious Disease Hospitall were collected for external validation of the model. 379 cavities as the region of interesting (ROI) from chest CT images were performed by 2 experienced radiologists. 80% of cavities were allocated to the training set and 20% to the validation set using a random number generated by a computer. 1409 radiomics features extracted from the Huiying Radcloud platform were used to analyze the two kinds of diseases' CT cavity characteristics. Feature selection was performed using analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) methods, and six supervised learning classifiers (KNN, SVM, XGBoost, RF, LR, and DT models) were used to analyze the features. RESULTS: 29 optimal features were selected by the variance threshold method, K best method, and Lasso algorithm.and the ROC curve values are obtained. In the training set, the AUC values of the six models were all greater than 0.97, 95% CI were 0.95-1.00, the sensitivity was greater than 0.92, and the specificity was greater than 0.92. In the validation set, the AUC values of the six models were all greater than 0.84, 95% CI were 0.76-1.00, the sensitivity was greater than 0.79, and the specificity was greater than 0.79. In the external validation set, The AUC values of the six models were all greater than 0.84, LR classifier has the highest precision, recall and F1-score, which were 0.92, 0.94, 0.93. CONCLUSION: The radiomics features extracted from cavity on CT images can provide effective proof in distinguishing the NTM pulmonary disease from PTB, and the radiomics analysis shows a more accurate diagnosis than the radiologists. Among the six classifiers, LR classifier has the best performance in identifying two diseases.

CT­based radiomics analysis of consolidation characteristics in differentiating pulmonary disease of non­tuberculous mycobacterium from pulmonary tuberculosis.

Global incidence rate of non-tuberculous mycobacteria (NTM) pulmonary disease has been increasing rapidly. In some countries and regions, its incidence rate is higher than that of tuberculosis. It is easily confused with tuberculosis. The topic of this study is to identify two diseases using CT radioomics. The aim in the present study was to investigate the value of CT-based radiomics to analyze consolidation features in differentiation of non-tuberculous mycobacteria (NTM) from pulmonary tuberculosis (TB). A total of 156 patients (75 with NTM pulmonary disease and 81 with TB) exhibiting consolidation characteristics in Shandong Public Health Clinical Center were retrospectively analyzed. Subsequently, 305 regions of interest of CT consolidation were outlined. Using a random number generated via a computer, 70 and 30% of consolidations were allocated to the training and the validation cohort, respectively. By means of variance threshold, when investigating the effective radiomics features, SelectKBest and the least absolute shrinkage and selection operator regression method were employed for feature selection and combined to calculate the radiomics score. K-nearest neighbor (KNN), support vector machine (SVM) and logistic regression (LR) were used to analyze effective radiomics features. A total of 18 patients with NTM pulmonary disease and 18 with TB possessing consolidation characteristics in Jinan Infectious Disease Hospital were collected for external validation of the model. A total of three methods was used in the selection of 52 optimal features. For KNN, the area under the curve (AUC; sensitivity, specificity) for the training and validation cohorts were 0.98 (0.93, 0.94) and 0.90 (0.88, 083), respectively; for SVM, AUC was 0.99 (0.96, 0.96) and 0.92 (0.86, 0.85) and for LR, AUC was 0.99 (0.97, 0.97) and 0.89 (0.88, 0.85). In the external validation cohort, AUC values of models were all >0.84 and LR classifier exhibited the most significant precision, recall and F1 score (0.87, 0.94 and 0.88, respectively). LR classifier possessed the best performance in differentiating diseases. Therefore, CT-based radiomics analysis of consolidation features may distinguish NTM pulmonary disease from TB.

Using ultrasound guided needle biopsy in conjunction with GeneXpert MTB/RIF to diagnose epididymal tuberculosis.

This study aimed to investigate the diagnostic utility of percutaneous ultrasound-guided needle biopsy conjunction with GeneXpert MTB/RIF for epididymal tuberculosis. A retrospective analysis was conducted on the pathological and laboratory examinations of 20 patients with epididymal lesions undergoing ultrasound guided biopsy at Shandong Public Health Clinical Center. Laboratory examination included acid-fast staining, Mycobacterium tuberculosis culture by BACTEC MGIT 960, and GeneXpert MTB/RIF test. Diagnosis and complications were comprehensively analyzed. Among the 20 patients, 15 had epididymal tuberculosis and 5 had non-epididymal tuberculosis. Ten patients had granulomatous inflammation and necrotic tissues. The sensitivity and specificity of acid-fast staining, Mycobacterium tuberculosis culture, and GeneXpert MTB/RIF for the diagnosis of epididymis tuberculosis were 26.67% and 100.00%, 33.33% and 100.00%, and 86.67% and 100.00%, respectively. The diagnostic value analysis of the 3 detection techniques indicated that the GeneXpert MTB/RIF technique (Kappa = 0.765, Area under the curve (AUC) = 0.933) was superior to Mycobacterium tuberculosis culture (Kappa = 0.200, AUC = 0.667) and acid-fast staining (Kappa = 0.154, AUC = 0.633). Ultrasound-guided percutaneous biopsy is a safe procedure. The GeneXpert MTB/RIF test has high sensitivity, specificity, and superior AUC value, which are of great value in the diagnosis of epididymal tuberculosis and rifampicin resistance detection.

Value of ultrasound guided biopsy combined with Xpert Mycobacterium tuberculosis/resistance to rifampin assay in the diagnosis of chest wall tuberculosis.

BACKGROUND: The thoracic wall lesions, particularly chest wall tuberculosis, and chest wall tumors and other pyogenic wall and actinomycetes infections, almost always present as a diagnostic challenge. AIM: To explore the value of ultrasound-guided biopsy combined with the Xpert Mycobacterium tuberculosis/resistance to rifampin (MTB/RIF) assay to diagnose chest wall tuberculosis. METHODS: We performed a retrospective study of patients with chest wall lesions from March 2018 to March 2021. All patients received the ultrasound-guided biopsy for pathology examination, acid-fast Bacillus staining, mycobacterial culture, and Xpert MTB/RIF analysis. The sensitivity, specificity, and area under the curve (AUC) were calculated for these diagnostic tests, either individually or combined. Rifampicin resistance results were compared between the mycobacterial culture and the Xpert MTB/RIF assay. RESULTS: In 31 patients with the chest wall lesion biopsy, 22 patients were diagnosed with chest wall tuberculosis. Of them, 3, 6, and 21 patients tested positive for mycobacterial culture, acid-fast stain, and Xpert MTB/RIF assay, respectively. The rifampicin resistance results of the 3 culture-positive patients were consistent with their Xpert MTB/RIF assay results. When considering the sensitivity, specificity, and AUC value, the Xpert MTB/RIF assay (95.5%, 88.9%, and 0.92, respectively) was a better choice than the acid-fast Bacillus stain (27.3%, 100.0%, and 0.64, respectively) and mycobacterial culture (13.6%, 100.0%, 0.57, respectively). No complications were reported during the procedure. CONCLUSION: Ultrasound guided biopsy combined with Xpert MTB/RIF has high value in the diagnosis of chest wall tuberculosis, and can also detect rifampicin resistance.

The value of CT radiomic in differentiating mycoplasma pneumoniae pneumonia from streptococcus pneumoniae pneumonia with similar consolidation in children under 5 years.

Objective: To investigate the value of CT radiomics in the differentiation of mycoplasma pneumoniae pneumonia (MPP) from streptococcus pneumoniae pneumonia (SPP) with similar CT manifestations in children under 5 years. Methods: A total of 102 children with MPP (n = 52) or SPP (n = 50) with similar consolidation and surrounding halo on CT images in Qilu Hospital and Qilu Children's Hospital between January 2017 and March 2022 were enrolled in the retrospective study. Radiomic features of the both lesions on plain CT images were extracted including the consolidation part of the pneumonia or both consolidation and surrounding halo area which were respectively delineated at region of interest (ROI) areas on the maximum axial image. The training cohort (n = 71) and the validation cohort (n = 31) were established by stratified random sampling at a ratio of 7:3. By means of variance threshold, the effective radiomics features, SelectKBest and least absolute shrinkage and selection operator (LASSO) regression method were employed for feature selection and combined to calculate the radiomics score (Rad-score). Six classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT) were used to construct the models based on radiomic features. The diagnostic performance of these models and the radiomic nomogram was estimated and compared using the area under the receiver operating characteristic (ROC) curve (AUC), and the decision curve analysis (DCA) was used to evaluate which model achieved the most net benefit. Results: RF outperformed other classifiers and was selected as the backbone in the classifier with the consolidation + the surrounding halo was taken as ROI to differentiate MPP from SPP in validation cohort. The AUC value of MPP in validation cohort was 0.822, the sensitivity and specificity were 0.81 and 0.81, respectively. Conclusion: The RF model has the best classification efficiency in the identification of MPP from SPP in children, and the ROI with both consolidation and surrounding halo is most suitable for the delineation.