How radiology can help pulmonary tuberculosis diagnosis: analysis of 49 patients

Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study

OBJECTIVES

Multidrug-resistant TB (MDR-TB) is a severe burden and public health threat worldwide. This study aimed to develop a radiomics model based on the tree-in-bud (TIB) sign and nodules and validate its predictive performance for MDR-TB.

METHODS

We retrospectively recruited 454 patients with proven active TB from two hospitals and classified them into three training and testing cohorts: TIB (n = 295, 102), nodules (n = 302, 97), and their combination (n = 261, 81). Radiomics features relating to TIB and nodules were separately extracted. The maximal information coefficient and recursive feature elimination were used to select informative features per the two signs. Two radiomics models were constructed to predict MDR-TB using a random forest classifier. Then, a combined model was built incorporating radiomics features based on these two signs. The capability of the models in the combined training and testing cohorts was validated with ROC curves.

RESULTS

Sixteen features were extracted from TIB and 15 from nodules. The AUCs of the combined model were slightly higher than those of the TIB model in the combined training cohort (0.911 versus 0.877, p > 0.05) and testing cohort (0.820 versus 0.786, p < 0.05) and similar to the performance of the nodules model in the combined training cohort (0.911 versus 0.933, p > 0.05) and testing cohort (0.820 versus 0.855, p > 0.05).

CONCLUSIONS

The CT-based radiomics models hold promise for use as a non-invasive tool in the prediction of MDR-TB.

CLINICAL RELEVANCE STATEMENT

Our study revealed that complementary information regarding MDR-TB can be provided by radiomics based on the TIB sign and nodules. The proposed radiomics models may be new markers to predict MDR in active TB patients.

KEY POINTS

• This is the first study to build, validate, and apply radiomics based on tree-in-bud sign and nodules for the prediction of MDR-TB. • The radiomics model showed a favorable performance for the identification of MDR-TB. • The combined model holds potential to be used as a diagnostic tool in routine clinical practice.

Development and validation of a 3D-convolutional neural network model based on chest CT for differentiating active pulmonary tuberculosis from community-acquired pneumonia

PURPOSE

To develop and validate a 3D-convolutional neural network (3D-CNN) model based on chest CT for differentiating active pulmonary tuberculosis (APTB) from community-acquired pneumonia (CAP).

MATERIALS AND METHODS

Chest CT images of APTB and CAP patients diagnosed in two imaging centers (n = 432 in center A and n = 61 in center B) were collected retrospectively. The data in center A were divided into training, validation and internal test sets, and the data in center B were used as an external test set. A 3D-CNN was built using Keras deep learning framework. After the training, the 3D-CNN selected the model with the highest accuracy in the validation set as the optimal model, which was applied to the two test sets in centers A and B. In addition, the two test sets were independently diagnosed by two radiologists. The 3D-CNN optimal model was compared with the discrimination, calibration and net benefit of the two radiologists in differentiating APTB from CAP using chest CT images.

RESULTS

The accuracy of the 3D-CNN optimal model was 0.989 and 0.934 with the internal and external test set, respectively. The area-under-the-curve values with the 3D-CNN model in the two test sets were statistically higher than that of the two radiologists (all P < 0.05), and there was a high calibration degree. The decision curve analysis showed that the 3D-CNN optimal model had significantly higher net benefit for patients than the two radiologists.

CONCLUSIONS

3D-CNN has high classification performance in differentiating APTB from CAP using chest CT images. The application of 3D-CNN provides a new automatic and rapid diagnosis method for identifying patients with APTB from CAP using chest CT images.

Comparison of pediatric empyema secondary to tuberculosis or non-tuberculosis community-acquired pneumonia in those who underwent surgery in high TB burden areas

INTRODUCTION

Tuberculous empyema (TE) in children is common in high-TB burden and medical resource-limited areas. However, studies that evaluate the characteristics of TE in children are sparse. This study aimed to analyze the clinical features of pediatric TE receiving surgical intervention.

METHODS

We performed a retrospective study of children with empyema secondary to community-acquired pneumonia who underwent surgery in our institution. The clinical characteristics were compared between TE and empyema secondary non-tuberculosis infection (non-tuberculosis empyema, NTE).

RESULTS

One hundred patients were included (27 with TE and 73 with NTE). Stage 3 empyema occupied 81.5% and 45.2% of TE and NTE in this study. The TE children had older age, longer duration of illness, and milder symptoms. Pleural fluid culture was positive for Mycobacterium tuberculosis in 7.4% of patients with TE. Lymph node enlargement, lymph node calcification, and pleural nodules presented in TE with high specificity (93.2%, 98.6%, and 98.5%) but low sensitivity (33.3%, 14.8%, and 29.6%) on CT scan. Thoracoscopy surgery was performed in 14 (51.9%) in TE and 39 (53.4%) in NTE. Postoperative chest-tube indwelling time was longer (7.85 ± 5.00 vs. 4.89 ± 1.81 days, p < .001), and more patients had incomplete lung expansion after 3 months in TE.

CONCLUSION

Tuberculosis infection should be screened in management of children with empyema in high-TB burden areas. Pediatric TE usually presented at older age and with milder respiratory symptoms. Pleural biopsy during surgery is often necessary to confirm the cause of infection. Thoracotomy is still required in some pediatric TE or NTE with delayed treatment in medical resource-limited area.

A valuable computed tomography-based new diagnostic tool for severe chest lesions in active pulmonary tuberculosis: combined application of influencing factors

The objective of the present investigation was to explore the influencing factors and value of computed tomography (CT) for diagnosing severe chest lesions in active pulmonary tuberculosis (APTB). This retrospective investigation included 463 patients diagnosed with APTB. Relevant clinical features were collected. Patients were assigned to mild/moderate group or advanced group depending on the lesion severity on chest CT, severe chest CT lesion refers to the moderately dense or less diffuse lesion that exceeds the total volume of one lung, or the dense fusion lesion greater than one third of the volume of one lung, or the lesion with cavity diameter ≥4 cm. Independent risk factors for severe lesions were determined by univariate and multivariate logistic regression analyses, and the diagnostic efficiency of the risk factors was assessed by receiver operating characteristic curve (ROC). Chest CT demonstrated that there were 285 (61.56%) cases with severe lesions; multivariate Logistic regression analysis showed dust exposure [odds ratio (OR) = 4.108, 95% confidence interval (CI) 2.416–6.986], patient classification (OR = 1.792, 95% CI 1.067–3.012), age (OR = 1.018, 95% CI 1.005–1.030), and albumin-globulin ratio (OR = 0.179, 95% CI 0.084–0.383) to be independently correlated with severe lesions on chest CT. ROC curve analysis showed the cutoff values of age, albumin-globulin ratio and combined score to be 39 years, 0.918 and −0.085, respectively. The predictive value of combined score area under the curve 0.753 (95% CI 0.708–0.798) was higher than that of any single factor. The combined score of these four factors further improved the predictive efficacy.