Deep learning based CT images automatic analysis model for active/non-active pulmonary tuberculosis differential diagnosis.

Active pulmonary tuberculosis (ATB), which is more infectious and has a higher mortality rate compared with non-active pulmonary tuberculosis (non-ATB), needs to be diagnosed accurately and timely to prevent the tuberculosis from spreading and causing deaths. However, traditional differential diagnosis methods of active pulmonary tuberculosis involve bacteriological testing, sputum culturing and radiological images reading, which is time consuming and labour intensive. Therefore, an artificial intelligence model for ATB differential diagnosis would offer great assistance in clinical practice. In this study, computer tomography (CT) scans images and corresponding clinical information of 1160 ATB patients and 1131 patients with non-ATB were collected and divided into training, validation, and testing sets. A 3-dimension (3D) Nested UNet model was utilized to delineate lung field regions in the CT images, and three different pre-trained deep learning models including 3D VGG-16, 3D EfficientNet and 3D ResNet-50 were used for classification and differential diagnosis task. We also collected an external testing set with 100 ATB cases and 100 Non-ATB cases for further validation of the model. In the internal and external testing set, the 3D ResNet-50 model outperformed other models, reaching an AUC of 0.961 and 0.946, respectively. The 3D ResNet-50 model reached even higher levels of diagnostic accuracy than experienced radiologists, while the CT images reading and diagnosing speed was 10 times faster than human experts. The model was also capable of visualizing clinician interpretable lung lesion regions important for differential diagnosis, making it a powerful tool assisting ATB diagnosis. In conclusion, we developed an auxiliary tool to differentiate active and non-active pulmonary tuberculosis, which would have broad prospects in the bedside.

Distribution and identification of Mycobacterium tuberculosis lineage in Kashgar prefecture.

OBJECTIVES: Kashgar prefecture is an important transportation and trade hub with a high incidence of tuberculosis. The following study analyzed the composition and differences in Mycobacterium tuberculosis (M.tb) lineage and specific tags to distinguish the lineage of the M.tb in Kashgar prefecture, thus providing a basis for the classification and diagnosis of tuberculosis in this area. METHODS: Whole-genome sequencing (WGS) of 161 M.tb clinical strains was performed. The phylogenetic tree was constructed using Maximum Likelihood (ML) based on single nucleotide polymorphisms (SNPs) and verified through principal component analysis (PCA). The composition structure of M.tb in different regions was analyzed by combining geographic information. RESULTS: M.tb clinical strains were composed of lineage 2 (73/161, 45.34%), lineage 3 (52/161, 32.30%) and lineage 4 (36/161, 22.36%). Moreover, the 3 lineages were subdivided into 11 sublineages, among which lineage 2 included lineage 2.2.2/Asia Ancestral 1 (9/73, 12.33%), lineage 2.2.1-Asia Ancestral 2 (9/73, 12.33%), lineage 2.2.1-Asia Ancestral 3 (18/73, 24.66%), and lineage 2.2.1-Modern Beijing (39/73, 53.42%). Lineage 3 included lineage 3.2 (14/52, 26.92%) and lineage 3.3 (38/52, 73.08%), while lineage 4 included lineage 4.1 (3/36, 8.33%), lineage 4.2 (2/36, 5.66%), lineage 4.4.2 (1/36, 2.78%), lineage 4.5 (28/36, 77.78%) and lineage 4.8 (2/36, 5.66%), all of which were consistent with the PCA results. One hundred thirty-six markers were proposed for discriminating known circulating strains. Reconstruction of a phylogenetic tree using the 136 SNPs resulted in a tree with the same number of delineated clades. Based on geographical location analysis, the composition of Lineage 2 in Kashgar prefecture (45.34%) was lower compared to other regions in China (54.35%-90.27%), while the composition of Lineage 3 (32.30%) was much higher than in other regions of China (0.92%-2.01%), but lower compared to the bordering Pakistan (70.40%). CONCLUSION: Three lineages were identified in M.tb clinical strains from Kashgar prefecture, with 136 branch-specific SNP. Kashgar borders with countries that have a high incidence of tuberculosis, such as Pakistan and India, which results in a large difference between the M.tb lineage and sublineage distribution in this region and other provinces of China.

Effects of Mycobacterium tuberculosis lineages and regions of difference (RD) virulence gene variation on tuberculosis recurrence.

Background: China ranks second in the incidence of tuberculosis (TB), and the virulence and infectivity of Mycobacterium tuberculosis (M.tb) in different lineages are different. The variation of virulence genes in the M.tb regions of difference (RD) may be the reason for differences in pathogenicity. Studying the relationship between virulence gene mutations in the RD region of clinical strains of M.tb and TB relapse can provide basic data for the study of TB prevention and control. Methods: A total of 155 M.tb clinical strains were collected in Kashgar Prefecture. Whole-genome sequencing (WGS) was conducted, and mutations in virulence genes in the M.tb RD region were analyzed. The maximum likelihood method was implemented using IQ-TREE software. Logistic regression was used to analyze the relationship between lineage, RD region virulence gene variation, and patient relapse. Results: The 155 strains of M.tb in Kashgar Prefecture belong to 3 M.tb lineages: L2 (45.80%), L3 (32.90%), and L4 (21.30%). In relapsed patients, L2 (70.83%, 17/24) was significantly higher than the other lineages (29.17%, 7/24; P<0.05). Relapse was significantly correlated with L2 [odds ratio (OR) =3.505; 95% confidence interval (CI): 1.341-9.158; P=0.011]. In the virulence genes of the RD region, g.4357804 (T→G, OR =4.278; 95% CI: 1.594-11.481; P=0.004), g.4359653 (C→T, OR =3.356; 95% CI: 1.303-8.644; P=0.012), and g.2627618 (C→A, OR =2.676; 95% CI: 1.101-6.502; P=0.030) mutations were significantly associated with patient relapse. The mutation frequencies of g.4357804, g.4359653, and g.2627618 in L2 were significantly higher than those in the non-L2 group (P<0.05). Conclusions: Patients infected with L2 are more prone to relapse, and RD region virulence gene variation is an important factor for the strong pathogenicity and easy relapse after infection associated with L2.