ArticleQuantitative imaging in medicine and surgery2023
Mycobacterial cavity on chest computed tomography: clinical implications and deep learning-based automatic detection with quantification.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Revolutionizing diagnosis of pulmonaryFrontiers in microbiology · 2024Pooled it
- An Integrated Mycobacterial CT Imaging Dataset with Multispecies Information.Scientific data · 2025Article
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed (I) to investigate the clinical implication of computed tomography (CT) cavity volume in tuberculosis (TB) and non-tuberculous mycobacterial pulmonary disease (NTM-PD), and (II) to develop a three-dimensional (3D) nnU-Net model to automatically detect and quantify cavity volume on CT images. Methods: We retrospectively included conveniently sampled 206 TB and 186 NTM-PD patients in a tertiary referral hospital, who underwent thin-section chest CT scans from 2012 through 2019. TB was microbiologically confirmed, and NTM-PD was diagnosed by 2007 Infectious Diseases Society of America/American Thoracic Society guideline. The reference cavities were semi-automatically segmented on CT images and a 3D nnU-Net model was built with 298 cases (240 cases for training, 20 for tuning, and 38 for internal validation). Receiver operating characteristic curves were used to evaluate the accuracy of the CT cavity volume for two clinically relevant parameters: sputum smear positivity in TB and treatment in NTM-PD. The sensitivity and false-positive rate were calculated to assess the cavity detection of nnU-Net using radiologist-detected cavities as references, and the intraclass correlation coefficient (ICC) between the reference and the U-Net-derived cavity volumes was analyzed. Results: The mean CT cavity volumes in TB and NTM-PD patients were 11.3 and 16.4 cm Conclusions: CT cavity volume was associated with sputum positivity in TB and necessity of treatment in NTM-PD. The 3D nnU-Net model could automatically detect and quantify mycobacterial cavities on chest CT, helping assess TB infectivity and initiate NTM-TB treatment.
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Registered trials
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