Cui E-Nuo, Yu Tao, Shang Sheng-Jie, Wang Xiao-Yu, Jin Yi-Lin, Dong Yue, Zhao Hai, Luo Ya-Hong, Jiang Xi-Ran
School of Computer Science and Engineering, Northeastern University, Shenyang 110619, Liaoning Province, China.
Medical Imaging Department, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, Liaoning Province, China.
World J Clin Cases. 2020 Nov 6;8(21):5203-5212. doi: 10.12998/wjcc.v8.i21.5203.
Pulmonary tuberculosis (TB) and lung cancer (LC) are common diseases with a high incidence and similar symptoms, which may be misdiagnosed by radiologists, thus delaying the best treatment opportunity for patients.
To develop and validate radiomics methods for distinguishing pulmonary TB from LC based on computed tomography (CT) images.
We enrolled 478 patients (January 2012 to October 2018), who underwent preoperative CT screening. Radiomics features were extracted and selected from the CT data to establish a logistic regression model. A radiomics nomogram model was constructed, with the receiver operating characteristic, decision and calibration curves plotted to evaluate the discriminative performance.
Radiomics features extracted from lesions with 4 mm radial dilation distances outside the lesion showed the best discriminative performance. The radiomics nomogram model exhibited good discrimination, with an area under the curve of 0.914 (sensitivity = 0.890, specificity = 0.796) in the training cohort, and 0.900 (sensitivity = 0.788, specificity = 0.907) in the validation cohort. The decision curve analysis revealed that the constructed nomogram had clinical usefulness.
These proposed radiomic methods can be used as a noninvasive tool for differentiation of TB and LC based on preoperative CT data.
肺结核（TB）和肺癌（LC）是常见疾病，发病率高且症状相似，放射科医生可能会误诊，从而延误患者的最佳治疗时机。
基于计算机断层扫描（CT）图像开发并验证用于区分肺结核和肺癌的放射组学方法。
我们纳入了478例患者（2012年1月至2018年10月），这些患者均接受了术前CT筛查。从CT数据中提取并选择放射组学特征，以建立逻辑回归模型。构建放射组学列线图模型，并绘制受试者工作特征曲线、决策曲线和校准曲线以评估其鉴别性能。
从病变外4毫米径向扩张距离的病变中提取的放射组学特征显示出最佳的鉴别性能。放射组学列线图模型表现出良好的鉴别能力，训练队列中的曲线下面积为0.914（敏感性 = 0.890，特异性 = 0.796），验证队列中的曲线下面积为0.900（敏感性 = 0.788，特异性 = 0.907）。决策曲线分析表明构建的列线图具有临床实用性。
这些提出的放射组学方法可作为一种基于术前CT数据区分肺结核和肺癌的非侵入性工具。
