Duan Lizhen, Shan Wenli, Bo Genji, Lu Guangming, Guo Lili
Department of Medical Imaging, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an 223300, China.
Department of Medical Imaging, The Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China.
Diagnostics (Basel). 2022 Nov 4;12(11):2699. doi: 10.3390/diagnostics12112699.
Background: Lung-RADS classification and CT signs can both help in the differential diagnosis of SPNs. The purpose of this study was to investigate the diagnostic value of these two methods and the combination of the two methods for solitary pulmonary nodules (SPNs). Methods: A total of 296 cases of SPNs were retrospectively analyzed. All the SPNs were classified according to the Lung-RADS grading version 1.1. The scores of each lesion were calculated according to their CT signs. Imaging features, such as the size and margin of the lesions, pleural traction, spiculation, lobulation, bronchial cutoff, air bronchogram, vacuoles, tumor vasculature, and cavity signs, were analyzed. The imaging results were compared with the pathology examination findings. Receiver operating characteristic (ROC) curves were applied to compare the values of the different methods in differentially diagnosing benign and malignant SPNs. Results: The sensitivity, specificity, and accuracy of Lung-RADS grading for diagnosing SPNs were 34.0%, 94.4%, and 47.6%, respectively. The area under the ROC curve (AUC) was 0.600 (p < 0.001). The sensitivity, specificity, and accuracy of the CT sign scores were 56.3%, 70.0%, and 60.5%, respectively, and the AUC was 0.657 (p < 0.001). The sensitivity, specificity, and accuracy of the combination of the two methods for diagnosing SPNs were 93.2%, 61.1%, and 83.5%, and the AUC was 0.777 (p < 0.001). Conclusion: The combination of Lung-RADS classification and CT signs significantly improved the differential diagnosis of SPNs.
Lung-RADS分类和CT征象均可有助于孤立性肺结节(SPN)的鉴别诊断。本研究的目的是探讨这两种方法及其联合应用对SPN的诊断价值。方法:回顾性分析296例SPN患者。所有SPN均根据Lung-RADS 1.1版进行分级。根据各病变的CT征象计算其得分。分析病变的大小、边缘、胸膜牵拉、毛刺、分叶、支气管截断、空气支气管征、空泡、肿瘤血管和空洞征等影像特征。将影像结果与病理检查结果进行比较。应用受试者操作特征(ROC)曲线比较不同方法鉴别良恶性SPN的价值。结果:Lung-RADS分级诊断SPN的敏感性、特异性和准确性分别为34.0%、94.4%和47.6%。ROC曲线下面积(AUC)为0.600(p<0.001)。CT征象评分的敏感性、特异性和准确性分别为56.3%、70.0%和60.5%,AUC为0.657(p<0.001)。两种方法联合诊断SPN的敏感性、特异性和准确性分别为93.2%、61.1%和83.5%,AUC为0.777(p<0.001)。结论:Lung-RADS分类与CT征象联合应用可显著提高SPN的鉴别诊断能力。