Radiological signs associated with pulmonary multi-drug resistant tuberculosis: an analysis of published evidences

Prediction of active drug-resistant pulmonary tuberculosis based on CT radiomics: construction and validation of independent models and combined models for residual pulmonary parenchyma

Background

Drug-resistant tuberculosis (DR-TB) is a severe public health threat and burden worldwide. This study seeks to develop and validate both independent and combined radiomic models using pulmonary cavity (PC), tree-in-bud sign (TIB), total lung lesions (TLL), and residual pulmonary parenchyma (RPP) to evaluate their effectiveness in predicting DR-TB.

Methods

We recruited 306 confirmed active pulmonary tuberculosis cases from two hospitals, comprising 142 drug-resistant and 164 drug-sensitive cases. Patients were assigned to five training and testing cohorts: PC (n = 109, 47), TIB (n = 214, 92), TLL (n = 214, 92), RPP (n = 214, 92), and their combination (n = 109, 47). Radiomic features were extracted using variance thresholding, K-best, and LASSO techniques. We developed four separate radiomic models with random forest (RF) for DR-TB prediction and created a combined model integrating all features from the four indicators. Model performance was validated using ROC curves.

Results

We extracted 10, 2, 10, 3, and 9 radiomic features from PC, TIB, TLL, RPP, and the combined model, respectively. The combined model achieved AUC values of 0.886 (95% CI: 0.827-0.945) in the training set and 0.865 (95% CI: 0.764-0.966) in the testing set. It slightly surpassed the PC model in the training set (0.886 vs. 0.850, p < 0.05) and was comparable in the testing set (0.865 vs. 0.850, p > 0.05). The combined model showed similar performance to the TIB, TLL, and RPP models in both sets (p > 0.05).

Conclusion

The newly defined and developed RPP model and the combined model demonstrated robust performance in identifying DR-TB, highlighting the potential of CT-based radiomic models as effective non-invasive tools for DR-TB prediction.

A CT-based radiomics analyses for differentiating drug‑resistant and drug-sensitive pulmonary tuberculosis

BACKGROUND

To explore the value of computed tomography based radiomics in the differential diagnosis of drug-sensitive and drug-resistant pulmonary tuberculosis.

METHODS

The clinical and computed tomography image data of 177 patients who were diagnosed with pulmonary tuberculosis through sputum culture and completed drug-susceptibility testing from April 2018 to December 2020 at the Second Hospital of Nanjing were retrospectively analyzed. Patients with drug-resistant pulmonary tuberculosis (n = 78) and drug-sensitive pulmonary tuberculosis (n = 99) were randomly divided into a training set (n = 124) and a validation set (n = 53) at a ratio of 7:3. Regions of interest were drawn to delineate the lesions and radiomics features were extracted from non-contrast computed tomography images. A radiomics signature based on the valuable radiomics features was constructed and a radiomics score was calculated. Demographic data, clinical symptoms, laboratory results and computed tomography imaging characteristics were evaluated to establish a clinical model. Combined with the Rad-score and clinical factors, a radiomics-clinical model nomogram was constructed.

RESULTS

Thirteen features were used to construct the radiomics signature. The radiomics signature showed good discrimination in the training set (area under the curve (AUC), 0.891; 95% confidence interval (CI), 0.832-0.951) and the validation set (AUC, 0.803; 95% CI, 0.674-0.932). In the clinical model, the AUC of the training set was 0.780(95% CI, 0.700-0.859), while the AUC of the validation set was 0.692 (95% CI, 0.546-0.839). The radiomics-clinical model showed good calibration and discrimination in the training set (AUC, 0.932;95% CI, 0.888-0.977) and the validation set (AUC, 0.841; 95% CI, 0.719-0.962).

CONCLUSIONS

Simple radiomics signature is of great value in differentiating drug-sensitive and drug-resistant pulmonary tuberculosis patients. The radiomics-clinical model nomogram showed good predictive, which may help clinicians formulate precise treatments.

Multidrug-resistant tuberculosis treatment outcomes and associated factors at Yirgalem General Hospital, Sidama Region, South Ethiopia: a retrospective cohort study

BACKGROUND

The spread of multidrug-resistant tuberculosis (MDR-TB) poses a significant challenge to TB control efforts. This study evaluated the treatment outcomes and associated factors among patients receiving treatment for MDR-TB in southern Ethiopia.

METHODS

A retrospective follow-up study covering ten years, from 2014 to 2023, analyzed the records of confirmed cases of pulmonary TB admitted to Yirgalem General Hospital, an MDR-TB treatment initiation center in the Sidama Region. To compare the successful treatment outcomes across the years, a chi-square test of independence was conducted. Bivariate and multivariable logistic regression models were used to identify factors associated with treatment outcomes for MDR-TB.

RESULTS

Out of 276 confirmed MDR-TB cases, 4(1.4%) were diagnosed with resistance to second-line drugs (SLDs). Overall, 138 patients achieved favourable treatment outcomes, resulting in a treatment success rate of 50.0% [95% CI 44.1-55.9%]. Among these 138 patients, 105(76.1%, 95 CI 68.7-83.5%) were cured, while 33(23.9%, 95 CI 16.5-31.3%) completed their treatment. The successful treatment outcomes varied significantly across the years, ranging from 3.6% in 2020 to 90% in 2021. The analysis indicated a statistically significant difference in treatment outcomes when considering data from 2014 to 2023 (χ2 = 44.539, p = 0.001). The proportion of patients with deaths, lost-to-follow-up (LTFU), treatment failures and not evaluated were 7.9% [95% CI 4.8-11.2%], 10.9% [95% CI 7.2-14.6%), 2.2% [95% CI 1.1-3.3%), and 28.9% [95% CI 23.7-34.2%] respectively. Individuals with a positive HIV status had significantly lower odds of a favorable treatment outcome [AOR = 0.628, 95% CI (0.479-0.824), p = 0.018]. Similarly, patients with a BMI of less than 18 are more likely to have unfavorable treatment outcomes compared to those with a BMI of 18 or higher [AOR = 2.353, 95% CI 1.404-3.942, p < 0.001].

CONCLUSION

The study revealed a concerning 1.4% prevalence of additional resistance to SLDs. The 50% rate of unfavorable treatment among MDR-TB cases exceeds the target set by the WHO. A significant number of patients (10.9%) were LTFU, and the 28.9% categorized as 'not evaluated' is also concerning. Enhanced strategic interventions are needed to reduce such cases, and factors associated with poor treatment outcomes should receive greater attention. Future prospective studies can further explore the factors influencing improved treatment success.

Differences in pulmonary nodular consolidation and pulmonary cavity among drug-sensitive, rifampicin-resistant and multi-drug resistant tuberculosis patients: the Guangzhou computerized tomography study

Background

Pulmonary nodular consolidation (PN) and pulmonary cavity (PC) may represent the two most promising imaging signs in differentiating multidrug-resistant (MDR)-pulmonary tuberculosis (PTB) from drug-sensitive (DS)-PTB. However, there have been concerns that literature described radiological feature differences between DS-PTB and MDR-PTB were confounded by that MDR-PTB cases tend to have a longer history. This study seeks to further clarify this point.

Methods

All cases were from the Guangzhou Chest Hospital, Guangzhou, China. We retrieved data of consecutive new MDR cases [n=46, inclusive of rifampicin-resistant (RR) cases] treated during the period of July 2020 and December 2021, and according to the electronic case archiving system records, the main PTB-related symptoms/signs history was ≤3 months till the first computed tomography (CT) scan in Guangzhou Chest Hospital was taken. To pair the MDR-PTB cases with assumed equal disease history length, we additionally retrieved data of 46 cases of DS-PTB patients. Twenty-two of the DS patients and 30 of the MDR patients were from rural communities. The first CT in Guangzhou Chest Hospital was analysed in this study. When the CT was taken, most cases had anti-TB drug treatment for less than 2 weeks, and none had been treated for more than 3 weeks.

Results

Apparent CT signs associated with chronicity were noted in 10 cases in the DS group (10/46) and 9 cases in the MDR group (10/46). Thus, the overall disease history would have been longer than the assumed <3 months. Still, the history length difference between DS patients and MDR patients in the current study might not be substantial. The lung volume involvement was 11.3%±8.3% for DS cases and 8.4%±6.6% for MDR cases (P=0.022). There was no statistical difference between DS cases and MDR cases both in PN prevalence and in PC prevalence. For positive cases, MDR cases had more PN number (mean of positive cases: 2.63 vs. 2.28, P=0.38) and PC number (mean of positive cases: 2.14 vs. 1.38, P=0.001) than DS cases. Receiver operating characteristic curve analysis shows, PN ≥4 and PC ≥3 had a specificity of 86% (sensitivity 25%) and 93% (sensitivity 36%), respectively, in suggesting the patient being a MDR cases.

Conclusions

A combination of PN and PC features allows statistical separation of DS and MDR cases.

Chinese expert consensus on imaging diagnosis of drug-resistant pulmonary tuberculosis

Tuberculosis (TB) remains one of the major infectious diseases in the world with a high incidence rate. Drug-resistant tuberculosis (DR-TB) is a key and difficult challenge in the prevention and treatment of TB. Early, rapid, and accurate diagnosis of DR-TB is essential for selecting appropriate and personalized treatment and is an important means of reducing disease transmission and mortality. In recent years, imaging diagnosis of DR-TB has developed rapidly, but there is a lack of consistent understanding. To this end, the Infectious Disease Imaging Group, Infectious Disease Branch, Chinese Research Hospital Association; Infectious Diseases Group of Chinese Medical Association of Radiology; Digital Health Committee of China Association for the Promotion of Science and Technology Industrialization, and other organizations, formed a group of TB experts across China. The conglomerate then considered the Chinese and international diagnosis and treatment status of DR-TB, China's clinical practice, and evidence-based medicine on the methodological requirements of guidelines and standards. After repeated discussion, the expert consensus of imaging diagnosis of DR-PB was proposed. This consensus includes clinical diagnosis and classification of DR-TB, selection of etiology and imaging examination [mainly X-ray and computed tomography (CT)], imaging manifestations, diagnosis, and differential diagnosis. This expert consensus is expected to improve the understanding of the imaging changes of DR-TB, as a starting point for timely detection of suspected DR-TB patients, and can effectively improve the efficiency of clinical diagnosis and achieve the purpose of early diagnosis and treatment of DR-TB.

Computed Tomography Manifestations in Patients with Rifampin Primary Drug-Resistant Tuberculosis in an Infectious Disease Hospital in the Yi Autonomous Prefecture, China

Purpose

This study aimed to investigate clinical features and computed tomography (CT) manifestations of rifampicin primary drug-resistant pulmonary tuberculosis in Liangshan Yi Autonomous Prefecture.

Patients and Methods

A total of 100 inpatients with confirmed primary rifampicin-resistant pulmonary tuberculosis were recruited from January 2020 to December 2022 at an infectious disease hospital located in the Liangshan Yi Autonomous Prefecture. Additionally, 100 inpatients with confirmed drug-susceptible pulmonary tuberculosis during the same period were matched to the rifampicin-resistant group based on gender, age, and ethnicity. The clinical characteristics of the two groups were recorded separately. Furthermore, the CT manifestations in these patients were independently analyzed by three radiologists.

Results

The results showed that comorbid diabetes mellitus was more prevalent in the drug-resistant tuberculosis (DR-TB) group than in the drug-susceptible tuberculosis (DS-TB) group (9% vs 0%, p=0.0032). In terms of imaging presentation, DR-TB patients exhibited a higher frequency of calcifications (55% vs 35.00%, p=0.0068), greater median number of cavities (5 vs 2, p=0.0027), and larger maximum cavity diameter (52.08±25.55 mm vs 42.72±17.48 mm, p=0.0097). Additionally, bilateral involvement was more common in DR-TB patients at the site of the lesion (89% vs 76%, p=0.0246), with a higher prevalence in the right middle (82% vs 68%, p=0.0332), right lower (82% vs 68%, p=0.0332), left upper (91% vs 77%, p=0.0113), and left lower lobes (92% vs 66%, p<0.0001). Conversely, the involvement of only one lobe was less frequent in patients with DR-TB than in those with DS-TB (4% vs 13%, p=0.0398), whereas the involvement of all five lobes was more common (68% vs 51%, p=0.0209).

Conclusion

Patients with DR-TB exhibit a higher prevalence of severe imaging manifestations, highlighting the importance of CT in the early detection and diagnosis of DR-TB.

Utility of Machine Learning and Radiomics Based on Cavity for Predicting the Therapeutic Response of MDR-TB

Background

Sputum culture result at the sixth month is essential for predicting therapeutic response to longer multidrug-resistant tuberculosis (MDR-TB) regimens. This study aimed to construct a predictive model using cavity-based radiomics to predict sputum status at the sixth month for MDR-TB patients treated with longer regimens.

Methods

This retrospective study recruited 315 MDR-TB patients treated with longer regimens from two centers (250 patients from center 1 and 65 patients from center 2), who were divided into persistently positive and conversion to negative sputum culture groups according to sputum results. Radiomics features were extracted based on the cavity, and a radiomics model was selected and established using a random forest classifier. The clinical characteristics and primary CT signs with significant differences were integrated to build a clinical model. A combined model was generated using the radiomics and clinical model. ROC curves, F1-score and DCA curves were used to assess the predictive performance of the models.

Results

Twenty-eight radiomics features were selected to build a radiomics model for predicting the sputum status. The radiomics model achieved good performance, with AUCs of 0.892 and 0.839 in the training and testing cohort, respectively, which was similar to the performance of the combined model (0.913 and 0.815) and much higher than that of the clinical model (0.688 and 0.525) in the two cohorts.

Conclusion

The cavity-based radiomics model has the potential to predict sputum culture status for MDR-TB patients receiving longer regimens, which could guide follow-up treatment effectively.

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.

CeRNA network identified hsa-miR-17-5p, hsa-miR-106a-5p and hsa-miR-2355-5p as potential diagnostic biomarkers for tuberculosis

This study aims to analyze the regulatory non-coding RNAs in the pathological process of tuberculosis (TB), and identify novel diagnostic biomarkers. A longitudinal study was conducted in 5 newly diagnosed pulmonary tuberculosis patients, peripheral blood samples were collected before and after anti-TB treatment for 6 months, separately. After whole transcriptome sequencing, the differentially expressed RNAs (DE RNAs) were filtrated with |log2 (fold change) | > log2(1.5) and P value < .05 as screening criteria. Then functional annotation was actualized by gene ontology enrichment analysis, and enrichment pathway analysis was conducted by Kyoto Encyclopedia of Genes and Genomes database. And finally, the competitive endogenous RNA (ceRNA) regulatory network was established according to the interaction of ceRNA pairs and miRNA-mRNA pairs. Five young women were recruited and completed this study. Based on the differential expression analysis, a total of 1469 mRNAs, 996 long non-coding RNAs, 468 circular RNAs, and 86 miRNAs were filtrated as DE RNAs. Functional annotation demonstrated that those DE-mRNAs were strongly involved in the cellular process (n = 624), metabolic process (n = 513), single-organism process (n = 505), cell (n = 651), cell part (n = 650), organelle (n = 569), and binding (n = 629). Enrichment pathway analysis revealed that the differentially expressed genes were mainly enriched in HTLV-l infection, T cell receptor signaling pathway, glycosaminoglycan biosynthesis-heparan sulfate/heparin, and Hippo signaling pathway. CeRNA networks revealed that hsa-miR-17-5p, hsa-miR-106a-5p and hsa-miR-2355-5p might be regarded as potential diagnostic biomarkers for TB. Immunomodulation-related genes are differentially expressed in TB patients, and hsa-miR-106a-5p, hsa-miR-17-5p, hsa-miR-2355-5p might serve as potential diagnostic biomarkers.

Clinical and Computed Tomography Features Associated with Multidrug-Resistant Pulmonary Tuberculosis: A Retrospective Study in China

Purpose

To explore the value of integrating clinical and computed tomography (CT) features to predict multidrug-resistant pulmonary tuberculosis (MDR-PTB).

Patients and Methods

The study included 212 patients with MDR-PTB and 180 patients with drug-sensitive pulmonary tuberculosis (DS-PTB) who referred to our institute in China between January 2016 and March 2021. The clinical and CT characteristics were analyzed and compared between both groups. Multivariable logistic regression analysis was performed to identify independent factors that can be used to predict MDR-PTB. Furthermore, 115 patients admitted to another center from January 2019 to January 2022 were included as external validation cohort.

Results

For clinical characteristics, five parameters were significantly different between the two groups (all P < 0.05). With regard to CT features, nine parameters were significantly different between the two groups (all P < 0.05). Multivariable logistic regression analysis using the aforementioned differential features showed that male sex, retreated history, longer duration of previous anti-TB treatment, lower CD4⁺ T lymphocyte count, thick-walled cavity, centrilobular micronodules and tree-in-bud sign, bronchial stenosis, pleural and pericardial thickening were the most effective variations associated with MDR-PTB with an area under the curve (AUC) of 0.849 and accuracy of 78.6%. Furthermore, the external validation cohort that contains 115 patients obtained an AUC of 0.933 and accuracy of 81.7%.

Conclusion

MDR-PTB and DS-PTB have different clinical and imaging characteristics. A combined model incorporating these differential features can promptly diagnose MDR-PTB and develop subsequent therapeutic strategies.

Sputum smear conversion and treatment outcomes among drug-resistant pulmonary tuberculosis patients in eastern Ethiopia: A 9-years data analysis

Background

Drug-resistant tuberculosis (DR-TB) has become a public health problem throughout the world and about one-third of deaths were attributed to DR-TB from antimicrobial resistance which contributes to 10% of all TB deaths. Sub-Saharan Africa, particularly Ethiopia accounts for a significant number of TB cases. However, the scanty evidence on DR-TB contributing factors could affect the level of this deadly case tackling program. Therefore, this study aimed to assess the factors affecting sputum smear conversion and treatment outcomes among patients with DR-TB in Health facilities in Eastern Ethiopia.

Methods and materials

A cross-sectional study design was employed from 10 October to 10 November 2021, in the health facilities providing DR-TB services in Harari Region and Dire Dawa city administration. The medical records of 273 DR-TB patients from 10 January 2013 to 27 December 2021, were reviewed using structured checklists. Data were entered into Epidata 3.1 version and exported to STATA 14 version for analysis. The outcome variables were Initial Sputum conversion (converted vs. not-converted) and treatment outcome (Unfavorable vs. Favorable). Sputum examination was performed using both Acid-fast bacillus (AFB) smear microscopy and Löwenstein–Jensen (LJ) culture technique. A binary logistic regression analysis was used to assess the association of independent variables with the first month sputum smear conversion, while a conditional logistic regression model was used to assess the association of treatment outcome with explanatory variables. The associations were reported using adjusted odds ratios (AORs) at a 95% confidence interval.

Results

A total of 273 DR-TB patients were included in this study. The unfavorable DR-TB treatment outcome was significantly associated with the history of chewing khat (AOR = 4.38, 95% CI = 1.62, 11.84), having bilateral lung cavity on baseline chest X-ray (AOR = 12.08, 95% CI = 1.80, 2.57), having greater than 2+ smear result at baseline (AOR = 3.79, 95% CI = 1.35, 10.59), and poor adherence (AOR = 2.9, 95% CI = 1.28, 6.82). The sputum smear non-conversion at first month was significantly associated with being Human Immune Virus (HIV)-negative (AOR = 0.37, 0.17, 0.82), having low baseline BMI (AOR = 0.54, 95% CI = 0.29, 0.97), baseline culture &gt; 2++ (AOR = 0.15, 95% CI = 0.05, 0.49) and having greater than 2+ sputum smear result (AOR = 0.09, 95% CI = 0.012, 0.67). Patients with normal chest X-ray at baseline had 3.8 times higher chance of sputum smear conversion on first month (AOR = 3.77, 1.11, 12.77).

Conclusion

The overall initial sputum smear conversion and the treatment success rate among DR-TB patients were 52.75 and 66.30%, respectively. The Baseline underweight, HIV-negative, baseline smear &gt; 2+, baseline culture &gt; 2++, and clear lung on baseline X-ray were associated with smear conversion and history of khat chewing, bilateral lung cavity at baseline, having greater than 2+ smear results at baseline, and patients with poor treatment adherence had hostile treatment outcomes. So, strengthening and implementing nutrition assessment and patient counseling during directly observed therapies (DOTs) service and drug compliance could result in early sputum conversion and better treatment outcomes. DR-TB patients with high bacterial load and abnormal lungs on radiologic examination at baseline could need special attention during their course of treatment.

Characteristics of Drug-sensitive and Drug-resistant Tuberculosis Cases among Adults at Tuberculosis Referral Hospitals in Indonesia

As Indonesia's rifampin resistance testing rates are lower than global testing rates per the 2020 WHO global tuberculosis (TB) report, prevalence of multidrug-resistant TB may be underestimated. Our study aimed to evaluate prevalence and patterns of TB drug resistance (DR) within Indonesia. We conducted a cross-sectional analysis of baseline data collected from 2017-2018 as part of a cohort study of adults with presumed pulmonary TB at 7 DR-TB referral hospitals in Indonesia. Bacteriological examinations (acid-fast bacilli, GeneXpert, sputum culture) and drug-susceptibility testing were performed following the guidelines of the National TB Program. Of 447 participants with complete bacteriological examinations, 312 (69.8%) had positive sputum cultures for Mycobacterium tuberculosis. The proportion of MDR and pre-extensively drug-resistant was higher in previously treated compared with newly diagnosed participants (52.5% [73/139] versus 15% [26/173]). Compared with drug-sensitive case, drug-resistant TB was associated with cavities. Given the difference between rates of DR in TB referral hospitals from our study compared with the WHO survey in 2019 that showed 17.7% and 3.3% DR among previously treated and newly diagnosed participants globally, further characterization of Indonesia's TB epidemiology in the general population is needed. Strategies, including public policies to optimize case finding, strengthen capacity for resistance testing, and prevent loss to follow-up will be critical to reduce the burden of TB in Indonesia.

Chest X-ray Features in Drug-Resistant Tuberculosis Patients in Nigeria; a Retrospective Record Review

Chest X-ray (CXR) characteristics of patients with drug-resistant tuberculosis (DR-TB) depend on a variety of factors, and therefore, identifying the influence of these factors on the appearance of DR-TB in chest X-rays can help physicians improve diagnosis and clinical suspicion. Our aim was to describe the CXR presentation of patients with DR-TB and its association with clinical and demographic factors. A retrospective analysis of the CXRs of DR-TB patients in Nigeria between 2010 and 2016 was performed, reviewing features of chest radiographs, such as cavitation, opacity and effusion, infiltration and lung destruction. The association of these abnormal CXR findings with clinical and demographic characteristics was evaluated using bivariate and multivariate models, and a p-value < 0.05 was considered statistically significant with a 95% confidence interval. A total of 2555 DR-TB patients were studied, the majority (66.9%) were male, aged 29−38 years (36.8%), previously treated (77%), from the South West treatment zone (43.5%), HIV negative (76.7%) and bacteriologically diagnosed (89%). X-ray findings were abnormal in 97% of the participants, with cavitation being the most common (41.5%). Cavitation, effusion, fibrosis, and infiltration were higher in patients presenting in the South West zone and in those previously treated for DR-TB, while lung destruction was significantly higher in patients who are from the South South zone, and in those previously treated for DR-TB. Patients from the South East zone (AOR: 6.667, 95% CI: 1.383−32.138, p = 0.018), the North East zone (AOR: 6.667, 95% CI: 1.179−37.682, p = 0.032) and the North West zone (AOR: 6.30, 95% CI: 1.332−29.787, p = 0.020) had a significantly increased likelihood of abnormal chest X-ray findings, and prior TB treatment predisposed the patient to an increased likelihood of abnormal chest X-ray findings compared to new patients (AOR: 8.256, 95% CI: 3.718−18.330, p = 0.001). The finding of a significantly higher incidence of cavities, effusions and fibrosis in DR-TB patients previously treated could indicate late detection or presentation with advanced DR-TB disease, which may require a more individualized regimen or surgical intervention.

The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis

With the increasing incidence and mortality of pulmonary tuberculosis, in addition to tough and controversial disease management, time-wasting and resource-limited conventional approaches to the diagnosis and differential diagnosis of tuberculosis are still awkward issues, especially in countries with high tuberculosis burden and backwardness. In the meantime, the climbing proportion of drug-resistant tuberculosis poses a significant hazard to public health. Thus, auxiliary diagnostic tools with higher efficiency and accuracy are urgently required. Artificial intelligence (AI), which is not new but has recently grown in popularity, provides researchers with opportunities and technical underpinnings to develop novel, precise, rapid, and automated implements for pulmonary tuberculosis care, including but not limited to tuberculosis detection. In this review, we aimed to introduce representative AI methods, focusing on deep learning and radiomics, followed by definite descriptions of the state-of-the-art AI models developed using medical images and genetic data to detect pulmonary tuberculosis, distinguish the infection from other pulmonary diseases, and identify drug resistance of tuberculosis, with the purpose of assisting physicians in deciding the appropriate therapeutic schedule in the early stage of the disease. We also enumerated the challenges in maximizing the impact of AI in this field such as generalization and clinical utility of the deep learning models.

CT Imaging Characteristics of Nontuberculous Mycobacteria Lung Disease, Active Tuberculosis and Multi-Drug Resistant Tuberculosis

Background

The differential diagnosis of nontuberculous mycobacteria (NTM) lung disease, active tuberculosis (ATB) and multi-drug resistant tuberculosis (MDR-TB) remains difficult.

Objectives

To explore the CT imaging characteristics of NTM lung disease, ATB and MDR-TB for differential diagnosis.

Methods

Patients with NTM lung disease (n=200), ATB (n=200) and MDR-TB (n=200) who were examined and treated from August 2013 to May 2021 were included. Their chest CT imaging results were retrospectively analyzed, and the imaging characteristics were compared.

Results

The proportion of cases complicated with underlying lung disease, cough and hemoptysis was significantly higher in NTM group than those in ATB and MDR-TB groups (P<0.05). Compared with ATB and MDR-TB groups, NTM group had significantly more cases of nodule-bronchus dilation type, but significantly fewer cases of nodule-mass type and other types (P<0.05). In NTM group, the cases of thin-wall cavity, bronchiectasis and centrilobular nodules increased, but the detection rate of thick-wall cavity, lung consolidation, atelectasis, lung damage, lung volume reduction, intrapulmonary calcification, hilar and mediastinal lymph node calcification, acinar nodules, pleural thickening and pleural effusion declined compared with ATB and MDR-TB groups (P<0.05). The detection rates of lesions, cavities and bronchiectasis in the lingual lobe of left lung and middle lobe of right lung were significantly higher in NTM group than those in ATB and MDR-TB groups (P<0.05).

Conclusions

The imaging characteristics of NTM lung disease are quite similar to those of ATB and MDR-TB, but they can be differentially diagnosed through the types of cavities and nodules, incidence rate of bronchiectasis, and differences in lung consolidation, lung damage, calcification, pleural thickening and pleural effusion.

Chest X-ray findings in drug-sensitive and drug-resistant pulmonary tuberculosis patients in Uganda

Background

Methods

Results

Conclusions

Generalization Challenges in Drug-Resistant Tuberculosis Detection from Chest X-rays

Classification of drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) from chest radiographs remains an open problem. Our previous cross validation performance on publicly available chest X-ray (CXR) data combined with image augmentation, the addition of synthetically generated and publicly available images achieved a performance of 85% AUC with a deep convolutional neural network (CNN). However, when we evaluated the CNN model trained to classify DR-TB and DS-TB on unseen data, significant performance degradation was observed (65% AUC). Hence, in this paper, we investigate the generalizability of our models on images from a held out country's dataset. We explore the extent of the problem and the possible reasons behind the lack of good generalization. A comparison of radiologist-annotated lesion locations in the lung and the trained model's localization of areas of interest, using GradCAM, did not show much overlap. Using the same network architecture, a multi-country classifier was able to identify the country of origin of the X-ray with high accuracy (86%), suggesting that image acquisition differences and the distribution of non-pathological and non-anatomical aspects of the images are affecting the generalization and localization of the drug resistance classification model as well. When CXR images were severely corrupted, the performance on the validation set was still better than 60% AUC. The model overfitted to the data from countries in the cross validation set but did not generalize to the held out country. Finally, we applied a multi-task based approach that uses prior TB lesions location information to guide the classifier network to focus its attention on improving the generalization performance on the held out set from another country to 68% AUC.

Differentiating between drug-sensitive and drug-resistant tuberculosis with machine learning for clinical and radiological features

Background

Tuberculosis (TB) drug resistance is a worldwide public health problem that threatens progress made in TB care and control. Early detection of drug resistance is important for disease control, with discrimination between drug-resistant TB (DR-TB) and drug-sensitive TB (DS-TB) still being an open problem. The objective of this work is to investigate the relevance of readily available clinical data and data derived from chest X-rays (CXRs) in DR-TB prediction and to investigate the possibility of applying machine learning techniques to selected clinical and radiological features for discrimination between DR-TB and DS-TB. We hypothesize that the number of sextants affected by abnormalities such as nodule, cavity, collapse and infiltrate may serve as a radiological feature for DR-TB identification, and that both clinical and radiological features are important factors for machine classification of DR-TB and DS-TB.

Methods

We use data from the NIAID TB Portals program (https://tbportals.niaid.nih.gov), 1,455 DR-TB cases and 782 DS-TB cases from 11 countries. We first select three clinical features and 26 radiological features from the dataset. Then, we perform Pearson's chi-squared test to analyze the significance of the selected clinical and radiological features. Finally, we train machine classifiers based on different features and evaluate their ability to differentiate between DR-TB and DS-TB.

Results

Pearson's chi-squared test shows that two clinical features and 23 radiological features are statistically significant regarding DR-TB vs. DS-TB. A ten-fold cross-validation using a support vector machine shows that automatic discrimination between DR-TB and DS-TB achieves an average accuracy of 72.34% and an average AUC value of 78.42%, when combing all 25 statistically significant features.

Conclusions

Our study suggests that the number of affected lung sextants can be used for predicting DR-TB, and that automatic discrimination between DR-TB and DS-TB is possible, with a combination of clinical features and radiological features providing the best performance.

Identifying Drug-Resistant Tuberculosis in Chest Radiographs: Evaluation of CNN Architectures and Training Strategies

Tuberculosis (TB) is a serious infectious disease that mainly affects the lungs. Drug resistance to the disease makes it more challenging to control. Early diagnosis of drug resistance can help with decision making resulting in appropriate and successful treatment. Chest X-rays (CXRs) have been pivotal to identifying tuberculosis and are widely available. In this work, we utilize CXRs to distinguish between drug-resistant and drug-sensitive tuberculosis. We incorporate Convolutional Neural Network (CNN) based models to discriminate the two types of TB, and employ standard and deep learning based data augmentation methods to improve the classification. Using labeled data from NIAID TB Portals and additional non-labeled sources, we were able to achieve an Area Under the ROC Curve (AUC) of up to 85% using a pretrained InceptionV3 network.

New Approaches in the Classification and Prognosis of Sign Clusters on Pulmonary CT Images in Patients With Multidrug-Resistant Tuberculosis

Background: To date, radiographic sign clusters of multidrug-resistant pulmonary tuberculosis (MDR-TB) patients have not been reported. We conducted a study to investigate the classification and prognosis of sign clusters in pulmonary Computed Tomography (CT) images from patients with MDR-TB for the first time by using principal component analysis (PCA).

Methods: The clinical data and pulmonary CT findings of 108 patients with MDR-TB in the Liupanshui Third Hospital were collected (from January 2018 to December 2020). PCA was used to analyze the sign clusters on pulmonary CT, and receiver operating characteristic (ROC) analysis was used to analyze the predictive value of the treatment outcome of MDR-TB patients.

Results: Six cluster signs of MDR-TB were determined by PCA: nodules, infiltration, consolidation, cavities, destroyed lung and non-active lesions. Nine months after treatment, the area under the ROC curve (AUC) of MDR-TB patients with a cavity sign cluster was 0.818 (95% CI: 0.733–0.886), and the positive predictive value (PPV) and negative predictive value (NPV) of the treatment outcome were 79.6% (95% CI: 65.7–89.8%) and 72.9% (95% CI: 59.7–83.6%), respectively.

Conclusion: PCA plays an important role in the classification of sign groups on pulmonary CT images of MDR-TB patients, and the sign clusters obtained from PCA are of great significance in predicting the treatment outcome.

Illustration of a number of atypical computed tomography manifestations of active pulmonary tuberculosis

Tuberculosis is a serious public health challenge facing mankind and one of the top ten causes of death. Diagnostic imaging plays an important role, particularly for the diagnosis and treatment planning of tuberculosis patients with negative microbiology results. This article illustrates a number of atypical computed tomography (CT) appearances of pulmonary tuberculosis (PTB), including (I) clustered micronodules (CMNs) sign; (II) reversed halo sign (RHS); (III) tuberculous pneumatocele; (IV) hematogenously disseminated PTB with predominantly diffuse ground glass opacity manifestation; (V) hematogenously disseminated PTB with randomly distributed non-miliary nodules; (VI) PTB changes occur on the background of emphysema or honeycomb changes of interstitial pneumonia; and (VII) PTB manifesting as organizing pneumonia. While the overall incidence of PTB is decreasing globally, the incidence of atypical manifestations of tuberculosis is increasing. A good understanding of the atypical CT imaging changes of active PTB shall help the diagnosis and differential diagnosis of PTB in clinical practice.

Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia

BACKGROUND AND OBJECTIVES

Diagnosis of Pulmonary Rifampicin Resistant Tuberculosis (RR-TB) with the Drug-Susceptibility Test (DST) is costly and time-consuming. Furthermore, GeneXpert for rapid diagnosis is not widely available in Indonesia. This study aims to develop and evaluate the CUHAS-ROBUST model performance, an artificial-intelligence-based RR-TB screening tool.

METHODS

A cross-sectional study involved suspected all type of RR-TB patients with complete sputum Lowenstein Jensen DST (reference) and 19 clinical, laboratory, and radiology parameter results, retrieved from medical records in hospitals under the Faculty of Medicine, Hasanuddin University Indonesia, from January 2015-December 2019. The Artificial Neural Network (ANN) models were built along with other classifiers. The model was tested on participants recruited from January 2020-October 2020 and deployed into CUHAS-ROBUST (index test) application. Sensitivity, specificity, and accuracy were obtained for assessment.

RESULTS

A total of 487 participants (32 Multidrug-Resistant/MDR 57 RR-TB, 398 drug-sensitive) were recruited for model building and 157 participants (23 MDR and 21 RR) in prospective testing. The ANN full model yields the highest values of accuracy (88% (95% CI 85-91)), and sensitivity (84% (95% CI 76-89)) compare to other models that show sensitivity below 80% (Logistic Regression 32%, Decision Tree 44%, Random Forest 25%, Extreme Gradient Boost 25%). However, this ANN has lower specificity among other models (90% (95% CI 86-93)) where Logistic Regression demonstrates the highest (99% (95% CI 97-99)). This ANN model was selected for the CUHAS-ROBUST application, although still lower than the sensitivity of global GeneXpert results (87.5%).

CONCLUSION

The ANN-CUHAS ROBUST outperforms other AI classifiers model in detecting all type of RR-TB, and by deploying into the application, the health staff can utilize the tool for screening purposes particularly at the primary care level where the GeneXpert examination is not available.

TRIAL REGISTRATION

NCT04208789.

A Comparative Study of Chest Computed Tomography Findings: 1030 Cases of Drug-Sensitive Tuberculosis versus 516 Cases of Drug-Resistant Tuberculosis

Purpose

To investigate the CT features of drug-resistant pulmonary tuberculosis (DR-PTB) and the diagnostic value of CT in DR-PTB diagnosis to provide imaging evidence for the timely detection of drug-resistant Mycobacterium tuberculosis.

Materials and Methods

A total of 1546 cases of pulmonary tuberculosis (PTB) with complete clinical data, chest CT images and defined drug sensitivity testing results were consecutively enrolled; 516 cases of DR-PTB were included in the drug-resistant group, and 1030 cases of drug-sensitive pulmonary tuberculosis (DS-PTB) were included in the drug-sensitivity group. Comparative analyses of clinical symptoms and imaging findings were conducted. Univariate and logistic regression analyses were performed, a regression equation model was developed, and the receiver operating characteristic (ROC) curve was constructed.

Results

In the univariate analysis, some features, including whole-lung involvement, multiple cavities, thick-walled cavities, collapsed lung, disseminated lesions along the bronchi, bronchiectasis, emphysema, atelectasis, calcification, proliferative lesions, encapsulated effusion, etc., were observed more frequently in the DR-PTB group than in the DS-PTB group, and the differences were statistically significant (p<0.05). Exudative lesions and pneumoconiosis were observed more frequently in the drug-sensitivity group than in the drug-resistant group (p<0.05). Logistic regression analysis indicated that whole-lung involvement, multiple cavities, thick-walled cavities, disseminated lesions along the bronchi, bronchiectasis, and emphysema were independent risk factors for DR-PTB, and exudative diseases were protective factors. The total prediction accuracy of the regression model was 80.6%, and the area under the ROC curve (AUC) was 82.6%.

Conclusion

Chest CT manifestations of DR-PTB had certain characteristics that significantly indicated the possibility of drug resistance in tuberculosis patients, specifically when multifarious imaging findings, including multiple cavities, thick-walled cavities, disseminated lesions along the bronchi, whole-lung involvement, etc., coexisted simultaneously. These results may provide imaging evidence for timely drug resistance detection in suspected drug-resistant cases and contribute to the early diagnosis of DR-PTB.

Obstructive lung disease and quality of life after cure of multi-drug-resistant tuberculosis in Uganda: a cross-sectional study

Background

Pulmonary multi-drug-resistant tuberculosis (MDR TB) alters lung architecture and involves lengthy treatment duration, high pill burden, drug adverse effects, travel restrictions, and stigma. Literature about pulmonary function and health-related quality of life (QoL) of patients treated for MDR TB is limited. This study sought to determine the prevalence of chronic obstructive pulmonary disease (COPD) and QoL of patients who were treated for pulmonary MDR TB.

Methods

Participants who completed 18 months of pulmonary MDR TB treatment and considered cured were eligible to be evaluated in a cross-sectional study. We performed post-bronchodilator spirometry to measure forced expiratory volume in 1 s (FEV₁) and forced vital capacity (FVC). COPD was defined as FEV₁/FVC < 0.7; health-related QoL was assessed using the Medical Outcomes Survey for HIV (MOS-HIV) and St. George's Respiratory Questionnaire (SGRQ). Linear and logistic regression models were used to assess associations with COPD, health-related QoL, and other characteristics of the cohort.

Results

A total of 95 participants were enrolled. Median age of the cohort was 39 years (interquartile range (IQR), 29-45), and 55 (58%) were HIV-positive. COPD prevalence was 23% (22/95). Median SGRQ score was normal at 7.8 (IQR, 3.1-14.8). Median mental and physical health summary scores were significantly impaired, at 58.6 (IQR, 52.0-61.5) and 52.9 (IQR, 47.8-57.9), respectively, on a scale of 0 to 100 where 100 represents excellent physical or mental health. In this sample, 19% (18/95) of participants were in the lowest relative socioeconomic position (SEP) while 34% (32/95) were in the highest relative SEP. Belonging in the lowest SEP group was the strongest predictor of COPD.

Conclusion

Individuals who have completed MDR TB treatment have a high prevalence of COPD and low mental and physical health summary scores. Our study highlights the need for pulmonary rehabilitation programs in patients with a low socioeconomic position (SEP) after MDR TB treatment.

Immunometabolism during Mycobacterium tuberculosis Infection

Over a quarter of the world's population is infected with Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB). Approximately 3.4% of new and 18% of recurrent cases of TB are multidrug-resistant (MDR) or rifampicin-resistant. Recent evidence has shown that certain drug-resistant strains of Mtb modulate host metabolic reprogramming, and therefore immune responses, during infection. However, it remains unclear how widespread these mechanisms are among circulating MDR Mtb strains and what impact drug-resistance-conferring mutations have on immunometabolism during TB. While few studies have directly addressed metabolic reprogramming in the context of drug-resistant Mtb infection, previous literature examining how drug-resistance mutations alter Mtb physiology and differences in the immune response to drug-resistant Mtb provides significant insights into how drug-resistant strains of Mtb differentially impact immunometabolism.

Computerised tomography scan in multi-drug-resistant versus extensively drug-resistant tuberculosis

Purpose

Multi-drug-resistant tuberculosis (MDR-TB) is simultaneously resistant to isoniazid and rifampin. Of course, this germ may also be resistant to other anti-tuberculosis drugs. Patients with extensively drug-resistant tuberculosis (XDR-TB) are also resistant to all types of fluoroquinolone and at least one of the three injectable medications: amikacin, clarithromycin, or kanamycin, in addition to isoniazid and rifampin. Therefore, the main objective of the current study was to evaluate and compare the computed tomography (CT) scan findings of MDR-TB and XDR-TB patients.

Material and methods

In this comparative descriptive cross-sectional study 45 consecutive TB patients who referred to Masih Daneshvari Hospital, Tehran, Iran from 2013 to 2019 were enrolled. TB was diagnosed based on sputum smear and sensitive molecular and microbial tests. Patients were divided into two groups (MDR-TB and XDR-TB) based on two types of drug resistance. CT scan findings were compared for cavitary, parenchymal, and non-parenchymal disorders. The early diagnostic values of these factors were also calculated.

Results

Findings related to cavitary lesions including the pattern, number, size of the largest cavity, maximum thickness of the cavity, lung involvement, number of lobes involved, and the air-fluid levels in the two patient groups were similar (p > 0.05). Parenchymal findings of the lung also included fewer and more nodules of 10 mm in the MDR-TB and XDR-TB groups, respectively. Tree-in-bud, ground-glass-opacity, bronchiectasis, cicatricial emphysema, and lobar atelectasis were similar in the two patient groups (p > 0.05). Findings outside the parenchymal lung, including mediastinal lymphadenopathy and pericardial effusion, showed no statistically significant difference between the MDR-TB and XDR-TB groups (p > 0.05). Parenchymal calcification was more common in the XDR group than in the MDR group (64.7% and 28.6%, respectively) with a significant difference (p = 0.01).

Conclusions

CT scan findings in patients with XDR-TB are similar to those of patients with MDR-TB for cavitary, parenchymal, and non-parenchymal lung characteristics. However, patients with XDR-TB tend to have more parenchymal calcification and left-sided plural effusion. CT characteristics overlap between XDR-TB and those with MDR-TB. It can be concluded that CT scan features are not sensitive to the diagnosis.

Clinical features of pulmonary mucormycosis in patients with different immune status

Background

Pulmonary mucormycosis (PM) is a relatively rare but often fatal and rapidly progressive disease. Most studies of PM are case reports or case series with limited numbers of patients, and focus on immunocompromised patients. We investigated the clinical manifestations, imaging features, treatment, and outcomes of patients with PM with a focus on the difference in clinical manifestations between patients with different immune status.

Methods

Clinical records, laboratory results, and computed tomography scans of 24 patients with proven or probable PM from January 2005 to December 2018 in Peking Union Medical College Hospital were retrospectively analyzed.

Results

Ten female and 14 male patients were included (median age, 43.5 years; range, 13-64 years). Common presenting symptoms were fever (70.8%), cough (70.8%), sputum production (54.2%), and hemoptysis (41.7%). Radiological findings included consolidation (83.3%), ground-glass opacities (58.3%), nodules (50.0%), masses (37.5%), cavities (33.3%), mediastinal lymphadenopathy (29.2%), and halo sign (12.5%); one patient had a reversed halo sign. Seven patients (29.2%) had no obvious predisposing risk factors, and 17 (70.8%) had underlying diseases including diabetes, hematological malignancy, and use of immunosuppressants. Compared with immunocompromised patients, immunocompetent patients with PM were younger {23 [13-46] vs. 48 [17-64] years, P=0.023}, comprised a higher proportion of men (100.0% vs. 41.2%, P=0.019), had a longer disease course {34 [8-47] vs. 9 [2-102] weeks, P=0.033}, had a higher eosinophil count [0.66 (0.07-2.00) ×10⁹/L vs. 0.04 (0.00-0.23) ×10⁹/L, P=0.001], and had a lower erythrocyte sedimentation rate {12 [1-88] vs. 74 [9-140] mm/h, P=0.032}.

Conclusions

PM can occur in heterogeneous patients with different immune status, and the clinical phenotype differs between immunocompetent and immunocompromised patients. Because of the lack of specific clinic and imaging manifestations, aggressive performance of invasive procedures to obtain histopathological and microbial evidence is crucial for a definitive diagnosis.

Tuberculous pleural effusion

Tuberculous effusion is a common disease entity with a spectrum of presentations from a largely benign effusion, which resolves completely, to a complicated effusion with loculations, pleural thickening and even frank empyema, all of which may have a lasting effect on lung function. The pathogenesis is a combination of true pleural infection and an effusive hypersensitivity reaction, compartmentalized within the pleural space. Diagnostic thoracentesis with thorough pleural fluid analysis including biomarkers such as adenosine deaminase and gamma interferon achieves high accuracy in the correct clinical context. Definitive diagnosis may require invasive procedures to demonstrate histological evidence of caseating granulomas or microbiological evidence of the organism on smear or culture. Drug resistance is an emerging problem that requires vigilance and extra effort to acquire a complete drug sensitivity profile for each tuberculous effusion treated. Nucleic acid amplification tests such as Xpert MTB/RIF can be invaluable in this instance; however, the yield is low in pleural fluid. Treatment consists of standard anti-tuberculous therapy or a guideline-based individualized regimen in the case of drug resistance. There is low-quality evidence that suggests possible benefit from corticosteroids; however, they are not currently recommended due to concomitant increased risk of adverse effects. Small studies report some short- and long-term benefit from interventions such as therapeutic thoracentesis, intrapleural fibrinolytics and surgery but many questions remain to be answered.

Clinical predictors of drug-resistant tuberculosis in Mexico

Drug-resistant tuberculosis (DR-TB) remains a major global health problem. Early treatment of TB is critical; in the absence of rapid- susceptibility testing, the empiric selection of drugs should be guided by clinical data. This study aimed to determine the clinical predictors of DR-TB. From September 2010 to August 2017, sociodemographic and clinical characteristics were collected from 144 patients with tuberculosis at the Hospital Civil de Guadalajara, Mexico. Isolates were subjected to drug-susceptibility testing. Clinical predictors of DR-TB were determined using univariate and multivariate analysis. Any drug, isoniazid, and rifampin resistance rates were 47.7, 23.0, and 11.6%, respectively. The visualization of cavities and nodules through either chest radiography or computed tomography were independent predictors of DR-TB. In conclusion, early detection of DR-TB in this population could be based on multiple cavities being observed using chest imaging. This study’s results can be applied to future patients with TB in our community to optimize the DR-TB diagnostic process.

Updates on 18F-FDG-PET/CT as a clinical tool for tuberculosis evaluation and therapeutic monitoring

Tuberculosis (TB) is currently the world's leading cause of infectious mortality. The complex immune response of the human body to Mycobacterium tuberculosis (M.tb) results in a wide array of clinical manifestations, thus the clinical and radiological diagnosis can be challenging. ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG-PET) scan with/without computed tomography (CT) component images the whole body and provides a metabolic map of the infection, enabling clinicians to assess the disease burden. ¹⁸F-FDG-PET/CT scan is particularly useful in detecting the disease in previously unknown sites, and allows the most appropriate site of biopsy to be selected. ¹⁸F-FDG-PET/CT is also very valuable in assessing early disease response to therapy, and plays an important role in cases where conventional microbiological methods are unavailable and for monitoring response to therapy in cases of multidrug-resistant TB or extrapulmonary TB. ¹⁸F-FDG-PET/CT cannot reliably differentiate active TB lesion from malignant lesions and false positives can also be due to other infective or inflammatory conditions. ¹⁸F-FDG PET is also unable to distinguish tuberculous lymphadenitis from metastatic lymph node involvement. The lack of specificity is a limitation for ¹⁸F-FDG-PET/CT in TB management.

TB DEPOT (Data Exploration Portal): A multi-domain tuberculosis data analysis resource

The NIAID TB Portals Program (TBPP) established a unique and growing database repository of socioeconomic, geographic, clinical, laboratory, radiological, and genomic data from patient cases of drug-resistant tuberculosis (DR-TB). Currently, there are 2,428 total cases from nine country sites (Azerbaijan, Belarus, Moldova, Georgia, Romania, China, India, Kazakhstan, and South Africa), 1,611 (66%) of which are multidrug- or extensively-drug resistant and 1,185 (49%), 863 (36%), and 952 (39%) of which contain X-ray, computed tomography (CT) scan, and genomic data, respectively. We introduce the Data Exploration Portal (TB DEPOT, https://depot.tbportals.niaid.nih.gov) to visualize and analyze these multi-domain data. The TB DEPOT leverages the TBPP integration of clinical, socioeconomic, genomic, and imaging data into standardized formats and enables user-driven, repeatable, and reproducible analyses. It furthers the TBPP goals to provide a web-enabled analytics platform to countries with a high burden of multidrug-resistant TB (MDR-TB) but limited IT resources and inaccessible data, and enables the reusability of data, in conformity with the NIH's Findable, Accessible, Interoperable, and Reusable (FAIR) principles. TB DEPOT provides access to "analysis-ready" data and the ability to generate and test complex clinically-oriented hypotheses instantaneously with minimal statistical background and data processing skills. TB DEPOT is also promising for enhancing medical training and furnishing well annotated, hard to find, MDR-TB patient cases. TB DEPOT, as part of TBPP, further fosters collaborative research efforts to better understand drug-resistant tuberculosis and aid in the development of novel diagnostics and personalized treatment regimens.

Improving treatment outcomes for MDR-TB - Novel host-directed therapies and personalised medicine of the future

Multidrug-resistant TB (MDR-TB) is a major threat to global health security. In 2017, only 50% of patients with MDR-TB who received WHO-recommended treatment were cured. Most MDR-TB patients who recover continue to suffer from functional disability due to long-term lung damage. Whilst new MDR-TB treatment regimens are becoming available, conventional drug therapies need to be complemented with host-directed therapies (HDTs) to reduce tissue damage and improve functional treatment outcomes. This viewpoint highlights recent data on biomarkers, immune cells, circulating effector molecules and genetics which could be utilised for developing personalised HDTs. Novel technologies currently used for cancer therapy which could facilitate in-depth understanding of host genetics and the microbiome in patients with MDR-TB are discussed. Against this background, personalised cell-based HDTs for adjunct MDR-TB treatment to improve clinical outcomes are proposed as a possibility for complementing standard therapy and other HDT agents. Insights into the molecular biology of the mechanisms of action of cellular HDTs may also aid to devise non-cell-based therapies targeting defined inflammatory pathway(s) in Mtb-driven immunopathology.