Mycobacterial cavity on chest computed tomography: clinical implications and deep learning-based automatic detection with quantification

Quantitative Imaging in Medicine and Surgery

article

Ieun Yoon¹  Jung Hee Hong²  Joseph Nathanael Witanto³  Jae-Joon Yim⁴  Nakwon Kwak⁴  Jin Mo Goo¹  Soon Ho Yoon¹ 

[1] Department of Radiology, Seoul National University Hospital , Seoul National University College of Medicine;Department of Radiology , Keimyung University Dongsan Medical Center;MEDICALIP Co., Ltd.;Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital , Seoul National University College of Medicine

关键词: Computed tomography (CT);    deep learning;    pulmonary tuberculosis;    non-tuberculous mycobacterial pulmonary disease (NTM-PD);    cavity;   

DOI  :  10.21037/qims-22-620

学科分类：外科医学

来源: AME Publications

【 摘 要 】

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 cm3, respectively, and were significantly greater in smear-positive TB (P<0.001) and NTM-PD necessitating treatment (P=0.020). The CT cavity volume provided areas under the curve of 0.701 [95% confidence interval (CI): 0.620–0.782] for TB sputum positivity and 0.834 (95% CI: 0.773–0.894) for necessity of NTM-PD treatment. The nnU-Net provided per-patient sensitivity of 100% (19/19) and per-lesion sensitivity of 83.7% (41/49) in the validation dataset, with an average of 0.47 false-positive small cavities per patient (median volume, 0.26 cm3). The mean Dice similarity coefficient between the manually segmented cavities and the U-Net-derived cavities was 78.9. The ICCs between the reference and U-Net-derived volumes were 0.991 (95% CI: 0.983–0.995) and 0.933 (95% CI: 0.897–0.957) on a per-patient and per-lesion basis, respectively. 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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