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

Cheng, Nianlan; Wu, Shuo; Luo, Xianli; Xu, Chunyan; Lou, Qin; Zhu, Jin; You, Lu; Li, Bangguo

doi:10.2147/idr.s300754

Cited by 15 publications

(25 citation statements)

References 28 publications

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“…This finding can be supported by some previous study results. 23,[32][33][34] Additionally, our results showed that destroyed lung, bronchial stenosis, pleural and pericardial thickening were frequently observed in MDR-PTB, which were less commonly mentioned in the previous studies. The aforementioned CT features are closely associated with extensive fibrous tissue hyperplasia and bronchopulmonary structure destruction and are highly suggestive of a chronic TB course, which are consistent with some clinical features (eg, retreated history or longer previous anti-TB treatment) of patients with MDR-PTB presented herein.…”

Section: Discussionsupporting

confidence: 45%

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

Li¹,

Fan²,

Lv³

et al. 2023

IDR

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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.

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Section: Discussionsupporting

confidence: 45%

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

Li¹,

Fan²,

Lv³

et al. 2023

IDR

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“…Another, small study [ 7 ], utilized data from 144 patients and found that the presence of multiple cavities is a good predictor for DR-TB. A much larger study [ 8 ], compared 516 DR-TB and 1030 DS-TB cases, obtaining an AUC of 0.83 using a regression model. This study observed that the co-existence of multiple findings (multiple cavities, thick-walled cavities, disseminated lesions along the bronchi, whole-lung involvement) was indicative of DR-TB.…”

Section: Previous Workmentioning

confidence: 99%

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

et al. 2022

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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.

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“…A logistic regression analysis has been carried out based on the differential representations on chest CT scans between DR‐TB and DS‐TB. Various characteristics, including wider lung involvement range, emphysema, bronchial dissemination, multiple cavities, exudative lesions, and so on, may participate in DR‐TB prediction 13 . Additionally, in specific DR‐TB subtypes, features of lesion range and cavities manifest discriminatively as well.…”

Section: Discussionmentioning

confidence: 99%

“…Besides laboratory tests, radiology plays a pivotal role in TB screening and diagnosis owing to its accessibility and efficiency 10 . Previous evidence has revealed that certain manifestations on chest computed tomography (CT) volumes, such as pulmonary nodules, thick‐wall cavities, consolidations, bronchiectasis, whole‐lung involvement, disseminated lesions, and so on, are more frequently observed in DR‐TB 11–13 . Additionally, cavities and lesion extent are characterized differently among patients with RR‐TB, multidrug‐resistant tuberculosis (MDR‐TB), and XDR‐TB, implying the diagnostic potential of chest CT imaging 14 …”

Section: Introductionmentioning

confidence: 99%

Deep learning for precise diagnosis and subtype triage of drug‐resistant tuberculosis on chest computed tomography

Liang,

Xu,

Yang

et al. 2024

MedComm

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Deep learning, transforming input data into target prediction through intricate network structures, has inspired novel exploration in automated diagnosis based on medical images. The distinct morphological characteristics of chest abnormalities between drug‐resistant tuberculosis (DR‐TB) and drug‐sensitive tuberculosis (DS‐TB) on chest computed tomography (CT) are of potential value in differential diagnosis, which is challenging in the clinic. Hence, based on 1176 chest CT volumes from the equal number of patients with tuberculosis (TB), we presented a Deep learning‐based system for TB drug resistance identification and subtype classification (DeepTB), which could automatically diagnose DR‐TB and classify crucial subtypes, including rifampicin‐resistant tuberculosis, multidrug‐resistant tuberculosis, and extensively drug‐resistant tuberculosis. Moreover, chest lesions were manually annotated to endow the model with robust power to assist radiologists in image interpretation and the Circos revealed the relationship between chest abnormalities and specific types of DR‐TB. Finally, DeepTB achieved an area under the curve (AUC) up to 0.930 for thoracic abnormality detection and 0.943 for DR‐TB diagnosis. Notably, the system demonstrated instructive value in DR‐TB subtype classification with AUCs ranging from 0.880 to 0.928. Meanwhile, class activation maps were generated to express a human‐understandable visual concept. Together, showing a prominent performance, DeepTB would be impactful in clinical decision‐making for DR‐TB.

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

Cited by 15 publications

References 28 publications

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

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

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

Deep learning for precise diagnosis and subtype triage of drug‐resistant tuberculosis on chest computed tomography

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