Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Haihe Hospital, Tianjin University, Tianjin Institute of Respiratory Diseases, Tianjin, China.
Biomed Res Int. 2020 Sep 29;2020:6287545. doi: 10.1155/2020/6287545. eCollection 2020.
An increasing number of patients infected with nontuberculous mycobacteria (NTM) are observed worldwide. However, it is challenging to identify NTM lung diseases from pulmonary tuberculosis (PTB) due to considerable overlap in classic manifestations and clinical and radiographic characteristics. This study quantifies both cavitary and bronchiectasis regions in CT images and explores a machine learning approach for the differentiation of NTM lung diseases and PTB. It involves 116 patients and 103 quantitative features. After the selection of informative features, a linear support vector machine performs disease classification, and simultaneously, discriminative features are recognized. Experimental results indicate that bronchiectasis is relatively more informative, and two features are figured out due to promising prediction performance (area under the curve, 0.84 ± 0.06; accuracy, 0.85 ± 0.06; sensitivity, 0.88 ± 0.07; and specificity, 0.80 ± 0.12). This study provides insight into machine learning-based identification of NTM lung diseases from PTB, and more importantly, it makes early and quick diagnosis of NTM lung diseases possible that can facilitate lung disease management and treatment planning.
全球范围内观察到感染非结核分枝杆菌（NTM）的患者数量不断增加。然而，由于经典表现和临床及影像学特征有很大的重叠，因此很难将 NTM 肺部疾病与肺结核（PTB）区分开来。本研究对 CT 图像中的空洞和支气管扩张区域进行定量，并探讨了一种基于机器学习的区分 NTM 肺部疾病和 PTB 的方法。该研究涉及 116 名患者和 103 个定量特征。在选择信息丰富的特征后，线性支持向量机执行疾病分类，同时识别出有区别的特征。实验结果表明，支气管扩张相对更具信息量，并且由于有希望的预测性能（曲线下面积为 0.84±0.06；准确率为 0.85±0.06；灵敏度为 0.88±0.07；特异性为 0.80±0.12），确定了两个特征。本研究为基于机器学习的 NTM 肺部疾病与 PTB 的识别提供了深入了解，更重要的是，它使得 NTM 肺部疾病的早期快速诊断成为可能，从而有助于肺病的管理和治疗计划。
