Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Beijing Chest Hospital, Capital Medical University, Beijing, China.
BMC Med Imaging. 2024 Jun 11;24(1):141. doi: 10.1186/s12880-024-01288-3.
To evaluate the consistency between doctors and artificial intelligence (AI) software in analysing and diagnosing pulmonary nodules, and assess whether the characteristics of pulmonary nodules derived from the two methods are consistent for the interpretation of carcinomatous nodules.
This retrospective study analysed participants aged 40-74 in the local area from 2011 to 2013. Pulmonary nodules were examined radiologically using a low-dose chest CT scan, evaluated by an expert panel of doctors in radiology, oncology, and thoracic departments, as well as a computer-aided diagnostic(CAD) system based on the three-dimensional(3D) convolutional neural network (CNN) with DenseNet architecture(InferRead CT Lung, IRCL). Consistency tests were employed to assess the uniformity of the radiological characteristics of the pulmonary nodules. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic accuracy. Logistic regression analysis is utilized to determine whether the two methods yield the same predictive factors for cancerous nodules.
A total of 570 subjects were included in this retrospective study. The AI software demonstrated high consistency with the panel's evaluation in determining the position and diameter of the pulmonary nodules (kappa = 0.883, concordance correlation coefficient (CCC) = 0.809, p = 0.000). The comparison of the solid nodules' attenuation characteristics also showed acceptable consistency (kappa = 0.503). In patients diagnosed with lung cancer, the area under the curve (AUC) for the panel and AI were 0.873 (95%CI: 0.829-0.909) and 0.921 (95%CI: 0.884-0.949), respectively. However, there was no significant difference (p = 0.0950). The maximum diameter, solid nodules, subsolid nodules were the crucial factors for interpreting carcinomatous nodules in the analysis of expert panel and IRCL pulmonary nodule characteristics.
AI software can assist doctors in diagnosing nodules and is consistent with doctors' evaluations and diagnosis of pulmonary nodules.
评估医生和人工智能（AI）软件在分析和诊断肺结节方面的一致性，并评估两种方法得出的肺结节特征是否一致，以便解读癌性结节。
本回顾性研究分析了 2011 年至 2013 年期间当地年龄在 40-74 岁的参与者。使用低剂量胸部 CT 扫描对肺结节进行影像学检查，由放射科、肿瘤科和胸科的专家小组以及基于三维（3D）卷积神经网络（CNN）和密集网络（DenseNet）结构的计算机辅助诊断（CAD）系统（InferRead CT Lung，IRCL）进行评估。采用一致性检验评估肺结节影像学特征的均匀性。采用受试者工作特征（ROC）曲线评估诊断准确性。采用逻辑回归分析确定两种方法是否产生相同的癌性结节预测因素。
本回顾性研究共纳入 570 例患者。人工智能软件在确定肺结节的位置和直径方面与专家组的评估具有高度一致性（kappa=0.883，一致性相关系数（CCC）=0.809，p=0.000）。实性结节衰减特征的比较也显示出较好的一致性（kappa=0.503）。在诊断为肺癌的患者中，专家组和 AI 的曲线下面积（AUC）分别为 0.873（95%CI：0.829-0.909）和 0.921（95%CI：0.884-0.949），但差异无统计学意义（p=0.0950）。最大直径、实性结节、亚实性结节是专家组和 IRCL 肺结节特征分析中解读癌性结节的关键因素。
人工智能软件可以辅助医生诊断结节，与医生对肺结节的评估和诊断一致。
