Role of digital tomosynthesis and dual energy subtraction digital radiography in detection of parenchymal lesions in active pulmonary tuberculosis

Development and validation of a preoperative CT‑based 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.

Tuberculosis conundrum - current and future scenarios: A proposed comprehensive approach combining laboratory, imaging, and computing advances

Tuberculosis (TB) remains a global threat, with the rise of multiple and extensively drug resistant TB posing additional challenges. The International health community has set various 5-yearly targets for TB elimination: mathematical modelling suggests that a 2050 target is feasible with a strategy combining better diagnostics, drugs, and vaccines to detect and treat both latent and active infection. The availability of rapid and highly sensitive diagnostic tools (Gene-Xpert, TB-Quick) will vastly facilitate population-level identification of TB (including rifampicin resistance and through it, multi-drug-resistant TB). Basic-research advances have illuminated molecular mechanisms in TB, including the protective role of Vitamin D. Also, Mycobacterium tuberculosis impairs the host immune response through epigenetic mechanisms (histone-binding modulation). Imaging will continue to be key, both for initial diagnosis and follow-up. We discuss advances in multiple imaging modalities to evaluate TB tissue changes, such as molecular imaging techniques (including pathogen-specific positron emission tomography imaging agents), non-invasive temporal monitoring, and computing enhancements to improve data acquisition and reduce scan times. Big data analysis and Artificial Intelligence (AI) algorithms, notably in the AI sub-field called “Deep Learning”, can potentially increase the speed and accuracy of diagnosis. Additionally, Federated learning makes multi-institutional/multi-city AI-based collaborations possible without sharing identifiable patient data. More powerful hardware designs - e.g., Edge and Quantum Computing- will facilitate the role of computing applications in TB. However, “Artificial Intelligence needs real Intelligence to guide it!” To have maximal impact, AI must use a holistic approach that incorporates time tested human wisdom gained over decades from the full gamut of TB, i.e., key imaging and clinical parameters, including prognostic indicators, plus bacterial and epidemiologic data. We propose a similar holistic approach at the level of national/international policy formulation and implementation, to enable effective culmination of TB’s endgame, summarizing it with the acronym “TB - REVISITED”.

Improved detection of solitary pulmonary nodules on radiographs compared with deep bone suppression imaging

Background

The present study aimed to investigate whether deep bone suppression imaging (BSI) could increase the diagnostic performance for solitary pulmonary nodule detection compared with digital tomosynthesis (DTS), dual-energy subtraction (DES) radiography, and conventional chest radiography (CCR).

Methods

A total of 256 patients (123 with a solitary pulmonary nodule, 133 with normal findings) were included in the study. The confidence score of 6 observers determined the presence or absence of pulmonary nodules in each patient. These were first analyzed using a CCR image, then with CCR plus deep BSI, then with CCR plus DES radiography, and finally with DTS images. Receiver-operating characteristic curves were used to evaluate the performance of the 6 observers in the detection of pulmonary nodules.

Results

For the 6 observers, the average area under the curve improved significantly from 0.717 with CCR to 0.848 with CCR plus deep BSI (P<0.01), 0.834 with CCR plus DES radiography (P<0.01), and 0.939 with DTS (P<0.01). Comparisons between CCR and CCR plus deep BSI found that the sensitivities of the assessments by the 3 residents increased from 53.2% to 69.5% (P=0.014) for nodules located in the upper lung field, from 30.6% to 44.6% (P=0.015) for nodules that were partially/completely obscured by the bone, and from 33.2% to 45.8% (P=0.006) for nodules <10 mm.

Conclusions

The deep BSI technique can significantly increase the sensitivity of radiology residents for solitary pulmonary nodules compared with CCR. Increased detection was seen mainly for smaller nodules, nodules with partial/complete obscuration, and nodules located in the upper lung field.

Pulmonary Tuberculosis: Role of Radiology in Diagnosis and Management

Tuberculosis is a public health problem worldwide, including in the United States-particularly among immunocompromised patients and other high-risk groups. Tuberculosis manifests in active and latent forms. Active disease can occur as primary tuberculosis, developing shortly after infection, or postprimary tuberculosis, developing after a long period of latent infection. Primary tuberculosis occurs most commonly in children and immunocompromised patients, who present with lymphadenopathy, pulmonary consolidation, and pleural effusion. Postprimary tuberculosis may manifest with cavities, consolidations, and centrilobular nodules. Miliary tuberculosis refers to hematogenously disseminated disease that is more commonly seen in immunocompromised patients, who present with miliary lung nodules and multiorgan involvement. The principal means of testing for active tuberculosis is sputum analysis, including smear, culture, and nucleic acid amplification testing. Imaging findings, particularly the presence of cavitation, can affect treatment decisions, such as the duration of therapy. Latent tuberculosis is an asymptomatic infection that can lead to postprimary tuberculosis in the future. Patients who are suspected of having latent tuberculosis may undergo targeted testing with a tuberculin skin test or interferon-γ release assay. Chest radiographs are used to stratify for risk and to assess for asymptomatic active disease. Sequelae of previous tuberculosis that is now inactive manifest characteristically as fibronodular opacities in the apical and upper lung zones. Stability of radiographic findings for 6 months distinguishes inactive from active disease. Nontuberculous mycobacterial disease can sometimes mimic the findings of active tuberculosis, and laboratory confirmation is required to make the distinction. Familiarity with the imaging, clinical, and laboratory features of tuberculosis is important for diagnosis and management. ^©RSNA, 2017.

MRI in Thoracic Tuberculosis of Children

OBJECTIVE

To explore the utility of lung magnetic resonance imaging (MRI) in children with thoracic tuberculosis (TB).

METHODS

This prospective study of forty children (age range of 5 to 15 y) with thoracic TB was approved by the institutional ethics committee. Chest radiograph, lung MRI and computed tomography (CT) scan were performed in all children. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of MRI were calculated and kappa test of agreement was used to determine the strength of agreement between the MRI and CT findings.

RESULTS

MRI performed equivalent to CT scan in detection of mediastinal and hilar lymphadenopathy, pleural effusion and lung cavitation (considered typical for TB) with sensitivity and specificity of 100%. MRI also yielded a sensitivity of 88.2% and specificity of 95.7% for nodules, with sensitivity of 100% and specificity of 92.9% for consolidation. Kappa agreement between CT and MRI in detection of each finding was almost perfect (k: 0.8-1).

CONCLUSIONS

Although CT scan is still superior to MRI in detection of smaller nodules, MRI demonstrates a very high degree of correlation and agreement with CT scan for detecting lung and mediastinal pathology related to TB, in children with thoracic TB and can be particularly useful in select population and follow-up of these children to avoid repeated radiation exposures.

Comparison of digital tomosynthesis and chest radiography for the detection of pulmonary nodules: systematic review and meta-analysis

OBJECTIVE

To compare the diagnostic accuracy of digital tomosynthesis (DTS) with that of chest radiography for the detection of pulmonary nodules by meta-analysis.

METHODS

A systematic literature search was performed to identify relevant original studies from 1 January 1 1976 to 31 August 31 2016. The quality of included studies was assessed by quality assessment of diagnostic accuracy studies-2. Per-patient data were used to calculate the sensitivity and specificity and per-lesion data were used to calculate the detection rate. Summary receiver-operating characteristic curves were drawn for pulmonary nodule detection.

RESULTS

16 studies met the inclusion criteria. 1017 patients on a per-patient basis and 2159 lesions on a per-lesion basis from 16 eligible studies were evaluated. The pooled patient-based sensitivity of DTS was 0.85 [95% confidence interval (CI) 0.83-0.88] and the specificity was 0.95 (0.93-0.96). The pooled sensitivity and specificity of chest radiography were 0.47 (0.44-0.51) and 0.37 (0.34-0.40), respectively. The per-lesion detection rate was 2.90 (95% CI 2.63-3.19).

CONCLUSION

DTS has higher diagnostic accuracy than chest radiography for detection of pulmonary nodules. Chest radiography has low sensitivity but similar specificity, comparable with that of DTS. Advances in knowledge: DTS has higher diagnostic accuracy than chest radiography for the detection of pulmonary nodules.