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

FDG-PET-CT as an early detection method for tuberculosis: a systematic review and meta-analysis

Tuberculosis (TB) causes major public health problems worldwide. Fighting TB requires sustained efforts in health prevention, diagnosis and treatment. Previous literature has shown that conventional diagnostic methods like X-ray and sputum microscopy often miss early or extrapulmonary TB due to their limited sensitivity. Blood tests, while useful, lack the anatomical detail needed for precise localization of TB lesions. A possible step forward in the fight against TB could be the use of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) and Computed Tomography (CT). This meta-analysis discusses the current literature, including the methods, results and implications of using FDG-PET-CT in the early diagnosis of TB. Analysis of the studies showed that the sensitivity of FDG-PET-CT as a potential method for early detection of TB was 82.6%.

Characteristics of atypical Pulmonary Tuberculosis without typical clinical features diagnosed by pathology

PURPOSE

Some patients with pulmonary tuberculosis (PTB) do not display typical clinical features, leading to delays in diagnosis and treatment.

METHODS

We retrospectively analyzed PTB patients admitted to the Second Affiliated Hospital of Chongqing Medical University between 2017 and 2020. They are divided into pathological group (diagnosed through pathological biopsy) and control group (diagnosed via sputum or lavage fluid). Clinical data of both groups were compared. Based on radiographic features, the pathological group was further divided into the inflammation group, peripheral nodule group, and central occupancy group. We then statistically analyzed the computed tomography (CT) signs, bronchoscopic manifestations and results of pathological biopsy for each subgroup.

RESULTS

The pathological group consisted of 75 patients, while the control group had 338 patients. Multivariate logistic regression analysis showed that the pathological group had more diabetes (OR = 3.266, 95% CI = 1.609-6.630, P = 0.001), lower ESR (OR = 0.984, 95% CI = 0.971-0.998, P = 0.022), and lower CRP (OR = 0.990, 95% CI = 0.980-0.999, P = 0.036). In the three subgroups, the exudative lesions in the inflammation group were mostly located in atypical areas of PTB. The lobulation sign and spiculation sign were frequently observed in the peripheral nodule group. All presented with significant hilar mediastinal lymphadenopathy in the central occupancy group. In the pathological group, bronchoscopic manifestations typically included mucosal edema and bronchial stenosis.

CONCLUSION

Diabetes is an independent risk factor for atypical PTB. Expression of erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) in atypical PTB is low. Radiologically, it is most easily misdiagnosed when presented as peripheral solid nodules or masses, so a biopsy is recommended.

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.

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.

Establishment and evaluation of a CT-based radiomic model for AIDS-associated pulmonary cryptococcosis

Background

Establish a CT-based diagnostic radiomic model for AIDS complicated with pulmonary cryptococcosis and evaluate the diagnostic efficacy of this model.

Methods

This retrospective study enrolled 98 AIDS patients with pulmonary cryptococcosis and 103 AIDS patients with other infections or neoplastic lesions, comprising a total of 699 lesions. Patients were randomly divided into a training group and test group at a ratio of 2.75:1. Features from all lesions, cavity lesions and solid nodule lesions were extracted, and two kinds of radiomic models (6 types) were established. ROC curves were drawn, and the sensitivity and specificity were calculated to compare the SVM model and LR model, radiologists’ empirical diagnoses and the combination of these empirical diagnoses with the radiomic model.

Results

The AUCs of senior radiologist for all lesions and cavity lesions were lower than those of the SVM and LR models. The diagnostic efficacy of primary radiologist was lower than that of both of the other model types. The diagnostic efficacy of the LR model was relatively stable, with the highest diagnostic efficiency of the 3 model/radiologist groups. The AUCs of intermediate radiologist in combination with the LR radiomic model for all lesions, nodular lesions and cavity lesions were 0.88, 0.84, and 0.9, respectively, which were the highest among all models and radiologists.

Conclusions

The CT-based radiomic LR model of AIDS-associated pulmonary cryptococcosis exhibits good diagnostic performance, which was similar to that of senior radiologists and higher than that of the primary radiologist. With the help of a radiomic model, radiologists can achieve improved diagnostic accuracy compared to that when only an empirical diagnosis is used.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00910-6.

The relationship between computed tomography appearance of pulmonary tuberculosis and blood glucose levels in 763 diabetes mellitus patients with pulmonary tuberculosis: a comparative study

INTRODUCTION

Glucose tolerance is often impaired in pulmonary tuberculosis (TB) patients. We aimed to explore the relationship between computed tomography (CT) findings of TB and blood glucose levels in diabetes mellitus (DM) patients.

METHODS

763 diabetes mellitus patients with pulmonary tuberculosis (DMTB) from March 2015 to March 2018 were selected and their clinical data were retrospectively evaluated. CT appearance of DMTB was reviewed and compared according to blood glucose levels and CT scores. TB scores were calculated according to the combination of typical and atypical CT appearance. The relationship between blood glucose levels and CT scores was analyzed via Pearson correlation coefficient.

RESULTS

TB lesions mainly occurred in the left lung and the lower lobes in the DMTB patients. Nodules and cavities are the main typical complications in these DMTB patients, and especially the number and size of cavities predominantly contribute to CT scan scores. The size of cavities (diameters (cm), median (95% CI of median)) was 0.72 (0.66-0.77), 1.20 (1.09-1.28), and 3.45 (2.92-3.94) from the low-, middle- and high-CT-score groups, respectively. The patients with high CT scores had a higher level of fasting plasma glucose (mean 13.48 mM, 95% CI of median 12.56-14.44 mM) than the patients in the low-CT-score (mean 8.73 mM, 95% CI of median 8.49-9.36 mM) and middle-CT-score groups (mean 10.16 mM, 95% CI of median 9.89-10.49 mM) (P < 0.0001). CT scores have a consistent relationship with the levels of blood glucose (rho = 0.60, P < 0.0001).

CONCLUSIONS

CT appearance stands for the severity of tuberculosis and is closely associated with blood glucose levels in diabetic TB patients.

Content-based image retrieval with a Convolutional Siamese Neural Network: Distinguishing lung cancer and tuberculosis in CT images

BACKGROUND

CT findings of lung cancer and tuberculosis are sometimes similar, potentially leading to misdiagnosis. This study aims to combine deep learning and content-based image retrieval (CBIR) to distinguish lung cancer (LC) from nodular/mass atypical tuberculosis (NMTB) in CT images.

METHODS

This study proposes CBIR with a convolutional Siamese neural network (CBIR-CSNN). First, the lesion patches are cropped out to compose LC and NMTB datasets and the pairs of two arbitrary patches form a patch-pair dataset. Second, this patch-pair dataset is utilized to train a CSNN. Third, a test patch is treated as a query. The distance between this query and 20 patches in both datasets is calculated using the trained CSNN. The patches closest to the query are used to give the final prediction by majority voting. One dataset of 719 patients is used to train and test the CBIR-CSNN. Another external dataset with 30 patients is employed to verify CBIR-CSNN.

RESULTS

The CBIR-CSNN achieves excellent performance at the patch level with an mAP (Mean Average Precision) of 0.953, an accuracy of 0.947, and an area under the curve (AUC) of 0.970. At the patient level, the CBIR-CSNN correctly predicted all labels. In the external dataset, the CBIR-CSNN has an accuracy of 0.802 and AUC of 0.858 at the patch level, and 0.833 and 0.902 at the patient level.

CONCLUSIONS

This CBIR-CSNN can accurately and automatically distinguish LC from NMTB using CT images. CBIR-CSNN has excellent representation capability, compatibility with few-shot learning, and visual explainability.