Clinical and Radiological Presentation of Patients with Smear Positive Pulmonary Tuberculosis.

Chest X-ray is an important diagnostic aid frequently used alongside microscopic smear of sputum for the confirmation of pulmonary tuberculosis (TB). However, there is a dearth of literature investigating the clinical and radiological pattern of sputum positive pulmonary TB among adults in Bangladesh. The current study explored these patterns in presentation. This descriptive cross-sectional study was conducted at outpatients in department of medicine of a tertiary care hospital. A total of 50 newly diagnosed adult cases of smear positive pulmonary TB attending at the Directly Observed Treatment Short-course (DOTS) corners were consecutively included. Informed written consent was taken before inclusion. Data were collected through face-to-face interview. Radiological presentation was explored using chest X-ray. Data were analyzed by SPSS version 26.0. The average age of patients was 41.0±17.12 years and majority were male (78.0%). The most prevalent respiratory symptom was cough (80.0%) followed by constitutional symptom like fever (70.0%) and weight loss (72.0%). Wasting was the predominant sign (60.0%). Radiologically both lungs were involved in 32.0%, left lung were involved in 30.0% cases and right lung were involved in 26.0% of cases. Twelve percent (12.0%) of patients had normal chest X-ray. Upper zone involvement was commonly observed in this study's patients (66.0%). The predominant pattern was consolidation (46.0%) followed by fibrosis (26.0%), nodular opacity (12.0%), collapse (10.0%), cavity (6.0%), pleural effusion (2.0%) and bronchiectasis (2.0%). Findings of this study would help familiarize and identify the common clinical and radiological presentations of sputum positive pulmonary TB patients in day-to-day practice.

A multicentre study to evaluate the diagnostic performance of a novel CAD software, DecXpert, for radiological diagnosis of tuberculosis in the northern Indian population.

Tuberculosis (TB) is the leading cause of mortality among infectious diseases globally. Effectively managing TB requires early identification of individuals with TB disease. Resource-constrained settings often lack skilled professionals for interpreting chest X-rays (CXRs) used in TB diagnosis. To address this challenge, we developed "DecXpert" a novel Computer-Aided Detection (CAD) software solution based on deep neural networks for early TB diagnosis from CXRs, aiming to detect subtle abnormalities that may be overlooked by human interpretation alone. This study was conducted on the largest cohort size to date, where the performance of a CAD software (DecXpert version 1.4) was validated against the gold standard molecular diagnostic technique, GeneXpert MTB/RIF, analyzing data from 4363 individuals across 12 primary health care centers and one tertiary hospital in North India. DecXpert demonstrated 88% sensitivity (95% CI 0.85-0.93) and 85% specificity (95% CI 0.82-0.91) for active TB detection. Incorporating demographics, DecXpert achieved an area under the curve of 0.91 (95% CI 0.88-0.94), indicating robust diagnostic performance. Our findings establish DecXpert's potential as an accurate, efficient AI solution for early identification of active TB cases. Deployed as a screening tool in resource-limited settings, DecXpert could enable early identification of individuals with TB disease and facilitate effective TB management where skilled radiological interpretation is limited.

A retrospective study differentiating nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis on computed tomography using radiomics and machine learning algorithms.

OBJECTIVE: To evaluate the effectiveness of a machine learning based on computed tomography (CT) radiomics to distinguish nontuberculous mycobacterial pulmonary disease (NTM-PD) from pulmonary tuberculosis (PTB). METHODS: In this retrospective analysis, medical records of 99 individuals afflicted with NTM-PD and 285 individuals with PTB in Zhejiang Chinese and Western Medicine Integrated Hospital were examined. Random numbers generated by a computer were utilized to stratify the study cohort, with 80% designated as the training cohort and 20% as the validation cohort. A total of 2153 radiomics features were extracted using Python (Pyradiomics package) to analyse the CT characteristics of the large disease areas. The identification of significant factors was conducted through the least absolute shrinkage and selection operator (LASSO) regression. The following four supervised learning classifier models were developed: random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGBoost). For assessment and comparison of the predictive performance among these models, receiver-operating characteristic (ROC) curves and the areas under the ROC curves (AUCs) were employed. RESULTS: The Student's t-test, Levene test, and LASSO algorithm collectively selected 23 optimal features. ROC analysis was then conducted, with the respective AUC values of the XGBoost, LR, SVM, and RF models recorded to be 1, 0.9044, 0.8868, and 0.7982 in the training cohort. In the validation cohort, the respective AUC values of the XGBoost, LR, SVM, and RF models were 0.8358, 0.8085, 0.87739, and 0.7759. The DeLong test results noted the lack of remarkable variation across the models. CONCLUSION: The CT radiomics features can help distinguish between NTM-PD and PTB. Among the four classifiers, SVM showed a stable performance in effectively identifying these two diseases.

Chest X-Ray Comparison Between Drug-Resistant and Drug-Sensitive Pulmonary Tuberculosis in Children.

INTRODUCTION: Chest X-ray (CXR) remains one of the tools used in diagnosing tuberculosis (TB). However, few studies about such tools exist, specifically in children in Indonesia. We aim to investigate and compare the CXR findings of children with pulmonary drug-resistant TB (DR-TB) and drug-sensitive TB (DS-TB) that could help in the evaluation and management of TB cases in children. METHODS: Retrospective analysis with cross-sectional approach was conducted in children (<18 years old) diagnosed with pulmonary DR-TB and DS-TB from January 2018 to December 2021. Documented data were collected from the Paediatric Respirology Registry and Tuberculosis Information System at Dr. Hasan Sadikin General Hospital Bandung. Characteristics of children, CXR findings, and TB severity were assessed and compared using the chi-square and Fisher's exact tests with significance levels set at p value <0.05. RESULTS: Sixty-nine children (DR-TB 31 children vs. DS-TB 38 children) were assessed. Of the 31 children with DR-TB, 65% were classified as multidrug-resistant TB (MDR-TB), followed by rifampicin-resistant TB (RR-TB), pre-extensively drug-resistant TB (pre-XDR-TB), and extensively drug-resistant TB (XDR-TB). The most common CXR findings in DR-TB are consolidation (68%), fibrosis (42%), and cavity (29%), whereas in DS-TB, it is pleura effusion (37%). Severe TB accounts for 50% of DR-TB (p = 0.008). CONCLUSIONS: Consolidation, fibrosis, cavities, and findings of severe TB are most common in DR-TB. Pleural effusion is the most common in DS-TB. These findings have the potential to be considered in further examination of children with pulmonary DR-TB and DS-TB; hence, more extensive studies are needed to confirm these results.

Breaking the threshold: Developing multivariable models using computer-aided chest X-ray analysis for tuberculosis triage.

OBJECTIVES: Computer-aided detection (CAD) software packages quantify tuberculosis (TB)-compatible chest X-ray (CXR) abnormality as continuous scores. In practice, a threshold value is selected for binary CXR classification. We assessed the diagnostic accuracy of an alternative approach to applying CAD for TB triage: incorporating CAD scores in multivariable modeling. METHODS: We pooled individual patient data from four studies. Separately, for two commercial CAD, we used logistic regression to model microbiologically confirmed TB. Models included CAD score, study site, age, sex, human immunodeficiency virus status, and prior TB. We compared specificity at target sensitivities ≥90% between the multivariable model and the current threshold-based approach for CAD use. RESULTS: We included 4,733/5,640 (84%) participants with complete covariate data (median age 36 years; 45% female; 22% with prior TB; 22% people living with human immunodeficiency virus). A total of 805 (17%) had TB. Multivariable models demonstrated excellent performance (areas under the receiver operating characteristic curve [95% confidence interval]: software A, 0.91 [0.90-0.93]; software B, 0.92 [0.91-0.93]). Compared with threshold scores, multivariable models increased specificity (e.g., at 90% sensitivity, threshold vs model specificity [95% confidence interval]: software A, 71% [68-74%] vs 75% [74-77%]; software B, 69% [63-75%] vs 75% [74-77%]). CONCLUSION: Using CAD scores in multivariable models outperformed the current practice of CAD-threshold-based CXR classification for TB diagnosis.

Application value of volumetric CT value in quantifying the activity of a pulmonary tuberculoma.

OBJECTIVE: The purpose of this study was to explore the auxiliary diagnostic value of volumetric CT value in quantifying the activity of a pulmonary tuberculoma. METHODS: Chest CT image data of 112 patients with pulmonary tuberculomas who were diagnosed clinically between October 16, 2013 and March 21, 2023 were selected. With the shortest diameter axis>5 mm on the mediastinal window serving as the inclusion criterion, 108 active tuberculomas and 64 non-active tuberculomas were selected. The focused image was manually segmented using ITK-SNAP software, the volumetric CT value of the focus was calculated, and the ROC curve was analyzed. Using the final clinical diagnosis as the reference standard, the auxiliary diagnostic efficacy and consistency of the conventional CT film reading method and volumetric CT value in determining the activity of a pulmonary tuberculoma were compared. RESULTS: The volumetric CT value of 108 active pulmonary tuberculoma lesions (33.39 [28.17,36.23] HU) was significantly less than 64 inactive pulmonary tuberculoma lesions (78.91 [57.81,120.31] HU); the difference was statistically significant (Z = -10.888. P < 0.001). ROC curve analysis showed that at a maximum Yoden index value of 0.963, the optimal volumetric CT threshold value was 45.32 HU, the sensitivity and specificity of the volumetric CT value in determining the activity of a pulmonary tuberculoma were 97.2% and 100.0%, respectively, and the maximum area under the ROC curve was 0.998. Taking the final clinical diagnosis as the reference standard, the sensitivity, specificity, consistency, and kappa value of the conventional CT film reading method for determining the activity of a pulmonary tuberculoma were 72.2% (78/108), 70.3% (45/64), 71.5% (123/172), and 0.413, respectively, while the corresponding volumetric CT values were 97.2% (105/108), 100.0% (64/64), 98.3% (168/172), and 0.951, respectively. CONCLUSION: Accurately quantifying the volumetric CT value of a pulmonary tuberculoma focus determines the activity of a pulmonary tuberculoma, which has very important auxiliary diagnostic value.

Prior tuberculosis, radiographic lung abnormalities and prevalent diabetes in rural South Africa.

BACKGROUND: Growing evidence suggests that chronic inflammation caused by tuberculosis (TB) may increase the incidence of diabetes. However, the relationship between post-TB pulmonary abnormalities and diabetes has not been well characterized. METHODS: We analyzed data from a cross-sectional study in KwaZulu-Natal, South Africa, of people 15 years and older who underwent chest X-ray and diabetes screening with hemoglobin A1c testing. The analytic sample was restricted to persons with prior TB, defined by either (1) a self-reported history of TB treatment, (2) radiologist-confirmed prior TB on chest radiography, and (3) a negative sputum culture and GeneXpert. Chest X-rays of all participants were evaluated by the study radiologist to determine the presence of TB lung abnormalities. To assess the relationships between our outcome of interest, prevalent diabetes (HBA1c ≥6.5%), and our exposure of interest, chest X-ray abnormalities, we fitted logistic regression models adjusted for potential clinical and demographic confounders. In secondary analyses, we used the computer-aided detection system CAD4TB, which scores X-rays from 10 to 100 for detection of TB disease, as our exposure interest, and repeated analyses with a comparator group that had no history of TB disease. RESULTS: In the analytic cohort of people with prior TB (n = 3,276), approximately two-thirds (64.9%) were women, and the average age was 50.8 years (SD 17.4). The prevalence of diabetes was 10.9%, and 53.0% of people were living with HIV. In univariate analyses, there was no association between diabetes prevalence and radiologist chest X-ray abnormalities (OR 1.23, 95%CI 0.95-1.58). In multivariate analyses, the presence of pulmonary abnormalities was associated with an 29% reduction in the odds of prevalent diabetes (aOR 0.71, 95%CI 0.53-0.97, p = 0.030). A similar inverse relationship was observed for diabetes with each 10-unit increase in the CAD4TB chest X-ray scores among people with prior TB (aOR 0.92, 95%CI 0.87-0.97; p = 0.002), but this relationship was less pronounced in the no TB comparator group (aOR 0.96, 95%CI 0.94-0.99). CONCLUSIONS: Among people with prior TB, pulmonary abnormalities on digital chest X-ray are inversely associated with prevalent diabetes. The severity of radiographic post-TB lung disease does not appear to be a determinant of diabetes in this South African population.

An exploratory deep learning approach to investigate tuberculosis pathogenesis in nonhuman primate model: Combining automated radiological analysis with clinical and biomarkers data.

BACKGROUND: Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti-TB drugs are generally curative. Therefore, TB-case detection and monitoring of therapy, need a comprehensive approach. Automated radiological analysis, combined with clinical, microbiological, and immunological data, by machine learning (ML), can help achieve it. METHODS: Six rhesus macaques were experimentally inoculated with pathogenic Mycobacterium tuberculosis in the lung. Data, including Computed Tomography (CT), were collected at 0, 2, 4, 8, 12, 16, and 20 weeks. RESULTS: Our ML-based CT analysis (TB-Net) efficiently and accurately analyzed disease progression, performing better than standard deep learning model (LLM OpenAI's CLIP Vi4). TB-Net based results were more consistent than, and confirmed independently by, blinded manual disease scoring by two radiologists and exhibited strong correlations with blood biomarkers, TB-lesion volumes, and disease-signs during disease pathogenesis. CONCLUSION: The proposed approach is valuable in early disease detection, monitoring efficacy of therapy, and clinical decision making.

Computer-aided detection of tuberculosis from chest radiographs in a tuberculosis prevalence survey in South Africa: external validation and modelled impacts of commercially available artificial intelligence software.

BACKGROUND: Computer-aided detection (CAD) can help identify people with active tuberculosis left undetected. However, few studies have compared the performance of commercially available CAD products for screening in high tuberculosis and high HIV settings, and there is poor understanding of threshold selection across products in different populations. We aimed to compare CAD products' performance, with further analyses on subgroup performance and threshold selection. METHODS: We evaluated 12 CAD products on a case-control sample of participants from a South African tuberculosis prevalence survey. Only those with microbiological test results were eligible. The primary outcome was comparing products' accuracy using the area under the receiver operating characteristic curve (AUC) against microbiological evidence. Threshold analyses were performed based on pre-defined criteria and across all thresholds. We conducted subgroup analyses including age, gender, HIV status, previous tuberculosis history, symptoms presence, and current smoking status. FINDINGS: Of the 774 people included, 516 were bacteriologically negative and 258 were bacteriologically positive. Diverse accuracy was noted: Lunit and Nexus had AUCs near 0·9, followed by qXR, JF CXR-2, InferRead, Xvision, and ChestEye (AUCs 0·8-0·9). XrayAME, RADIFY, and TiSepX-TB had AUC under 0·8. Thresholds varied notably across these products and different versions of the same products. Certain products (Lunit, Nexus, JF CXR-2, and qXR) maintained high sensitivity (>90%) across a wide threshold range while reducing the number of individuals requiring confirmatory diagnostic testing. All products generally performed worst in older individuals, people with previous tuberculosis, and people with HIV. Variations in thresholds, sensitivity, and specificity existed across groups and settings. INTERPRETATION: Several previously unevaluated products performed similarly to those evaluated by WHO. Thresholds differed across products and demographic subgroups. The rapid emergence of products and versions necessitates a global strategy to validate new versions and software to support CAD product and threshold selections. FUNDING: Government of Canada.

The utility of artificial intelligence in identifying radiological evidence of lung cancer and pulmonary tuberculosis in a high-burden tuberculosis setting.

BACKGROUND: Artificial intelligence (AI), using deep learning (DL) systems, can be utilised to detect radiological changes of various pulmonary diseases. Settings with a high burden of tuberculosis (TB) and people living with HIV can potentially benefit from the use of AI to augment resource-constrained healthcare systems. OBJECTIVE: To assess the utility of qXR software (AI) in detecting radiological changes compatible with lung cancer or pulmonary TB (PTB). METHODS: We performed an observational study in a tertiary institution that serves a population with a high burden of lung cancer and PTB. In total, 382 chest radiographs that had a confirmed diagnosis were assessed: 127 with lung cancer, 144 with PTB and 111 normal. These chest radiographs were de-identified and randomly uploaded by a blinded investigator into qXR software. The output was generated as probability scores from predefined threshold values. RESULTS: The overall sensitivity of the qXR in detecting lung cancer was 84% (95% confidence interval (CI) 80 - 87%), specificity 91% (95% CI 84 - 96%) and positive predictive value of 97% (95% CI 95 - 99%). For PTB, it had a sensitivity of 90% (95% CI 87 - 93%) and specificity of 79% (95% CI 73 - 84%) and negative predictive value of 85% (95% CI 79 - 91%). CONCLUSION: The qXR software was sensitive and specific in categorising chest radiographs as consistent with lung cancer or TB, and can potentially aid in the earlier detection and management of these diseases.

Development and validation of a preoperative CT­based radiomics nomogram to differentiate tuberculosis granulomas from lung adenocarcinomas: an external validation study.

BACKGROUND: An accurate and non-invasive approach is urgently needed to distinguish tuberculosis granulomas from lung adenocarcinomas. This study aimed to develop and validate a nomogram based on contrast enhanced-compute tomography (CE-CT) to preoperatively differentiate tuberculosis granuloma from lung adenocarcinoma appearing as solitary pulmonary solid nodules (SPSN). METHODS: This retrospective study analyzed 143 patients with lung adenocarcinoma (mean age: 62.4 ± 6.5 years; 54.5% female) and 137 patients with tuberculosis granulomas (mean age: 54.7 ± 8.2 years; 29.2% female) from two centers between March 2015 and June 2020. The training and internal validation cohorts included 161 and 69 patients (7:3 ratio) from center No.1, respectively. The external testing cohort included 50 patients from center No.2. Clinical factors and conventional radiological characteristics were analyzed to build independent predictors. Radiomics features were extracted from each CT-volume of interest (VOI). Feature selection was performed using univariate and multivariate logistic regression analysis, as well as the least absolute shrinkage and selection operator (LASSO) method. A clinical model was constructed with clinical factors and radiological findings. Individualized radiomics nomograms incorporating clinical data and radiomics signature were established to validate the clinical usefulness. The diagnostic performance was assessed using the receiver operating characteristic (ROC) curve analysis with the area under the receiver operating characteristic curve (AUC). RESULTS: One clinical factor (CA125), one radiological characteristic (enhanced-CT value) and nine radiomics features were found to be independent predictors, which were used to establish the radiomics nomogram. The nomogram demonstrated better diagnostic efficacy than any single model, with respective AUC, accuracy, sensitivity, and specificity of 0.903, 0.857, 0.901, and 0.807 in the training cohort; 0.933, 0.884, 0.893, and 0.892 in the internal validation cohort; 0.914, 0.800, 0.937, and 0.735 in the external test cohort. The calibration curve showed a good agreement between prediction probability and actual clinical findings. CONCLUSION: The nomogram incorporating clinical factors, radiological characteristics and radiomics signature provides additional value in distinguishing tuberculosis granuloma from lung adenocarcinoma in patients with a SPSN, potentially serving as a robust diagnostic strategy in clinical practice.

Lung Lesions: Differential Diagnoses Beyond Cancer.

BACKGROUND Lung cancer is the most common malignant neoplasm diagnosed worldwide. Early diagnosis and treatment are of great importance for patient's prognosis. A wide variety of pulmonary conditions display clinical and radiological presentation similar to that of lung cancer, and the awareness of their existence can help in making correct diagnoses. CASE REPORT This article presents a description of 4 patients with an insidious type of lesions mimicking pulmonary carcinomas. The first patient was referred to Department with a tumor-like lesion in the right lung. After CT of the chest and core-needle biopsy, the lesion turned out to be an ectopic thyroid tissue. The second patient reported a dry cough and weight loss. A lung nodule mass was revealed in chest CT and the patient was diagnosed with pulmonary tuberculoma. The remaining 2 patients, despite the suspicion of lung cancer, were subsequently diagnosed with a post-traumatic pleural hematoma and diffuse large B cell lymphoma. CONCLUSIONS Low-dose computed tomography of the chest plays a significant role in the diagnosis of newly detected lesions in the lungs. However, due to the similarity of the image of cancer to that of other diseases, the ultimate diagnosis should be based on the interpretation of full imaging diagnostic tests, clinical presentation, and histopathological examination of the material obtained from the lesion. Analysis of cases enables us to expand our understanding of the diseases that need to be considered in differential diagnosis of a patient with a detected tumor-like lesion in the lungs.

A deep learning-based algorithm for pulmonary tuberculosis detection in chest radiography.

In tuberculosis (TB), chest radiography (CXR) patterns are highly variable, mimicking pneumonia and many other diseases. This study aims to evaluate the efficacy of Google teachable machine, a deep neural network-based image classification tool, to develop algorithm for predicting TB probability of CXRs. The training dataset included 348 TB CXRs and 3806 normal CXRs for training TB detection. We also collected 1150 abnormal CXRs and 627 normal CXRs for training abnormality detection. For external validation, we collected 250 CXRs from our hospital. We also compared the accuracy of the algorithm to five pulmonologists and radiological reports. In external validation, the AI algorithm showed areas under the curve (AUC) of 0.951 and 0.975 in validation dataset 1 and 2. The accuracy of the pulmonologists on validation dataset 2 showed AUC range of 0.936-0.995. When abnormal CXRs other than TB were added, AUC decreased in both human readers (0.843-0.888) and AI algorithm (0.828). When combine human readers with AI algorithm, the AUC further increased to 0.862-0.885. The TB CXR AI algorithm developed by using Google teachable machine in this study is effective, with the accuracy close to experienced clinical physicians, and may be helpful for detecting tuberculosis by CXR.

Artificial intelligence-based radiographic extent analysis to predict tuberculosis treatment outcomes: a multicenter cohort study.

Predicting outcomes in pulmonary tuberculosis is challenging despite effective treatments. This study aimed to identify factors influencing treatment success and culture conversion, focusing on artificial intelligence (AI)-based chest X-ray analysis and Xpert MTB/RIF assay cycle threshold (Ct) values. In this retrospective study across six South Korean referral centers (January 1 to December 31, 2019), we included adults with rifampicin-susceptible pulmonary tuberculosis confirmed by Xpert assay from sputum samples. We analyzed patient characteristics, AI-based tuberculosis extent scores from chest X-rays, and Xpert Ct values. Of 230 patients, 206 (89.6%) achieved treatment success. The median age was 61 years, predominantly male (76.1%). AI-based radiographic tuberculosis extent scores (median 7.5) significantly correlated with treatment success (odds ratio [OR] 0.938, 95% confidence interval [CI] 0.895-0.983) and culture conversion at 8 weeks (liquid medium: OR 0.911, 95% CI 0.853-0.973; solid medium: OR 0.910, 95% CI 0.850-0.973). Sputum smear positivity was 49.6%, with a median Ct of 26.2. However, Ct values did not significantly correlate with major treatment outcomes. AI-based radiographic scoring at diagnosis is a significant predictor of treatment success and culture conversion in pulmonary tuberculosis, underscoring its potential in personalized patient management.

Comparison of Computed Tomography Findings in Lung Tuberculosis in Diabetic and Nondiabetic Patients.

BACKGROUND: Tuberculosis (TB) is one of the leading infectious causes of mortality globally. The purpose of this research is to examine the clinical and radiological characteristics of patients with TB and diabetes. METHODS: The research comprised 276 TB patients, 52 of whom were diabetic and 224 of whom were not. During the evaluation of the patients' clinical histories, age, gender, diagnostic indicator, and whether or not they had undergone prior treatment were questioned, as were the requirement of inpatient treatment and the existence of drug resistance. Radiographically, they were questioned in terms of bilateral-unilateral extent, percentage of parenchymal involvement, cavitation, tree-in-bud appearance, the presence of ground glass, consolidation, miliary involvement, sequela fibrotic changes, parenchymal calcification, mediastinal lymphadenopathy, pleural effusion, and pleural calcification. In addition, segmenting was used to assess involvement in the affected lobes. RESULTS: When we look at the results of 276 patients, 182 males and 94 females, the mean age is 46.01 ± 17.83. Diabetes and TB coexistence are more prevalent in male individuals (P = 0.029). Smear positivity and the need for inpatient treatment were found to be higher in the clinical features of diabetic patients (P = 0.05 and P = 0.01, respectively). Radiologically, diabetes individuals are more likely to have larger mediastinal lymph nodes (P = 0.032). CONCLUSION: In the coexistence of both TB and diabetes, there are variations in radiological findings, complexity in treatment response, and patient management.

Integrating molecular and radiological screening tools during community-based active case-finding for tuberculosis and COVID-19 in southern Africa.

OBJECTIVES: To evaluate diagnostic yield and feasibility of integrating testing for TB and COVID-19 using molecular and radiological screening tools during community-based active case-finding (ACF). METHODS: Community-based participants with presumed TB and/or COVID-19 were recruited using a mobile clinic. Participants underwent simultaneous point-of-care (POC) testing for TB (sputum; Xpert Ultra) and COVID-19 (nasopharyngeal swabs; Xpert SARS-CoV-2). Sputum culture and SARS-CoV-2 RT-PCR served as reference standards. Participants underwent ultra-portable POC chest radiography with computer-aided detection (CAD). TB infectiousness was evaluated using smear microscopy, cough aerosol sampling studies (CASS), and chest radiographic cavity detection. Feasibility of POC testing was evaluated via user-appraisals. RESULTS: Six hundred and one participants were enrolled, with 144/601 (24.0%) reporting symptoms suggestive of TB and/or COVID-19. 16/144 (11.1%) participants tested positive for TB, while 10/144 (6.9%) tested positive for COVID-19 (2/144 [1.4%] had concurrent TB/COVID-19). Seven (7/16 [43.8%]) individuals with TB were probably infectious. Test-specific sensitivity and specificity (95% CI) were: Xpert Ultra 75.0% (42.8-94.5) and 96.9% (92.4-99.2); Xpert SARS-CoV-2 66.7% (22.3-95.7) and 97.1% (92.7-99.2). Area under the curve (AUC) for CAD4TB was 0.90 (0.82-0.97). User appraisals indicated POC Xpert to have 'good' user-friendliness. CONCLUSIONS: Integrating TB/COVID-19 screening during community-based ACF using POC molecular and radiological tools is feasible, has a high diagnostic yield, and can identity probably infectious persons.

Chest Radiograph Screening for Detecting Subclinical Tuberculosis in Asymptomatic Household Contacts, Peru.

The World Health Organization's end TB strategy promotes the use of symptom and chest radiograph screening for tuberculosis (TB) disease. However, asymptomatic early states of TB beyond latent TB infection and active disease can go unrecognized using current screening criteria. We conducted a longitudinal cohort study enrolling household contacts initially free of TB disease and followed them for the occurrence of incident TB over 1 year. Among 1,747 screened contacts, 27 (52%) of the 52 persons in whom TB subsequently developed during follow-up had a baseline abnormal radiograph. Of contacts without TB symptoms, persons with an abnormal radiograph were at higher risk for subsequent TB than persons with an unremarkable radiograph (adjusted hazard ratio 15.62 [95% CI 7.74-31.54]). In young adults, we found a strong linear relationship between radiograph severity and time to TB diagnosis. Our findings suggest chest radiograph screening can extend to detecting early TB states, thereby enabling timely intervention.

Added value of spectral computed tomography quantitative parameters for differentiating tuberculosis-associated fibrosing mediastinitis from endobronchial lung cancer: initial results.

OBJECTIVE: The objective of this study was to explore the added value of spectral computed tomography (CT) parameters to conventional CT features for differentiating tuberculosis-associated fibrosing mediastinitis (TB-associated FM) from endobronchial lung cancer (EBLC). METHODS: Chest spectral CT enhancement images from 109 patients with atelectasis were analyzed retrospectively. These patients were divided into two distinct categories: the TB-associated FM group (n = 77) and the EBLC group (n = 32), based on bronchoscopy and/or pathological findings. The selection of spectrum parameters was optimized with the least absolute shrinkage and selection operator regression analysis. The relationship between the spectrum parameters and conventional parameters was explored using Pearson's correlation. Multivariate logistic regression analysis was used to build spectrum model. The spectrum parameters in the spectrum model were replaced with their corresponding conventional parameters to build the conventional model. Diagnostic performances were evaluated using receiver operating characteristic curve analyses. RESULTS: There was a moderate correlation between the parameters ㏒(L-AEFNIC) - ㏒(L-AEFC) (r= 0.419; p< 0.0001), ㏒(O-AEF40KeV) - ㏒(O-AEFC) (r= 0.475; p< 0.0001), [L-A-hydroxyapatite {HAP}(I)] - (L-U-CT) (r= 0.604; p< 0.0001), {arterial enhancement fraction (AEF) derived from normalized iodine concentration (NIC) of lymph node (L-AEFNIC), AEF derived from CT40KeV of bronchial obstruction (O-AEF40KeV), arterial-phase Hydroxyapatite (Iodine) concentration of lymph node [L-A-HAP(I)], AEF derived from conventional CT (AEFC), unenhanced CT value (U-CT)}. Spectrum model could improve diagnostic performances compared to conventional model (area under curve: 0.965 vs 0.916, p= 0.038). CONCLUSION: There was a moderate correlation between spectrum parameters and conventional parameters. Integrating conventional CT features with spectrum parameters could further improve the ability in differentiating TB-associated FM from EBLC.

Chest X-ray evaluation using machine learning to support the early diagnosis of pulmonary TB.

TB is a public health problem, and its diagnosis can be challenging. Among imaging methods, chest X-ray (CXR) is the leading choice for assessing pulmonary TB (PTB). Recent advancements in the field of artificial intelligence have stimulated studies evaluating the performance of machine learning (ML) for medical diagnosis. This study validated a new original Brazilian tool, the XmarTB, applied to CXR images to support the early diagnosis of PTB.An ML model was trained on 3,800 normal images, 3,800 abnormal CXRs without PTB and 1,376 with PTB manifestations from the publicly available TBX11K database.The binary classification can distinguish between normal and abnormal CXR with a sensitivity of 99.4% and specificity of 99.4%. The XmarTB tool had a sensitivity of 98.1% and a specificity of 99.7% in detecting TB cases among CXRs with abnormal CXRs; sensitivity was 96.7% and specificity 98.7% in detecting TB cases among all samples.This diagnostic tool can accurately and automatically detect abnormal CXRs and satisfactorily differentiate PTB from other pulmonary diseases. This tool holds significant promise in aiding the proactive detection of TB cases, providing rapid and accurate support for early diagnosis..