Yoon Ieun, Hong Jung Hee, Witanto Joseph Nathanael, Yim Jae-Joon, Kwak Nakwon, Goo Jin Mo, Yoon Soon Ho
Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
Department of Radiology, Keimyung University Dongsan Medical Center, Daegu, Korea.
Quant Imaging Med Surg. 2023 Feb 1;13(2):747-762. doi: 10.21037/qims-22-620. Epub 2023 Jan 3.
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.
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.
The mean CT cavity volumes in TB and NTM-PD patients were 11.3 and 16.4 cm, 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 cm). 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.
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.
本研究旨在（I）探讨计算机断层扫描（CT）空洞体积在肺结核（TB）和非结核分枝杆菌肺病（NTM-PD）中的临床意义，以及（II）开发一种三维（3D）nnU-Net模型，以自动检测和量化CT图像上的空洞体积。
我们回顾性纳入了一家三级转诊医院中206例TB患者和186例NTM-PD患者，这些患者在2012年至2019年间接受了胸部薄层CT扫描。TB通过微生物学确诊，NTM-PD根据2007年美国传染病学会/美国胸科学会指南进行诊断。在CT图像上对参考空洞进行半自动分割，并使用298例病例（240例用于训练，20例用于调整，38例用于内部验证）构建3D nnU-Net模型。采用受试者操作特征曲线评估CT空洞体积对两个临床相关参数的准确性：TB中的痰涂片阳性和NTM-PD中的治疗。以放射科医生检测到的空洞为参考，计算nnU-Net的敏感性和假阳性率，分析参考空洞与U-Net得出的空洞体积之间的组内相关系数（ICC）。
TB和NTM-PD患者的平均CT空洞体积分别为11.3 cm和16.4 cm，痰涂片阳性的TB患者（P<0.001）和需要治疗的NTM-PD患者（P=0.020）的空洞体积显著更大。CT空洞体积对TB痰阳性的曲线下面积为0.701[95%置信区间（CI）：0.620-0.782]，对NTM-PD治疗必要性的曲线下面积为0.834（95%CI：0.773-0.
