Xu Leidi, Chang Ning, Yang Tingyi, Lang Yuxiang, Zhang Yong, Che Yinggang, Xi Hangtian, Zhang Weiqi, Song Qingtao, Zhou Ying, Yang Xuemin, Yang Juanli, Qu Shuoyao, Zhang Jian
Department of Pulmonary Medicine, Xijing Hospital, Air Force Medical University, Xi'an, China.
National Science Library, Chinese Academy of Sciences, Beijing, China.
Front Oncol. 2022 Apr 21;12:883543. doi: 10.3389/fonc.2022.883543. eCollection 2022.
There is increasing incidence of pulmonary nodules due to the promotion and popularization of low-dose computed tomography (LDCT) screening for potential populations with suspected lung cancer. However, a high rate of false-positive and concern of radiation-related cancer risk of repeated CT scanning remains a major obstacle to its wide application. Here, we aimed to investigate the clinical value of a non-invasive and simple test, named the seven autoantibodies (7-AABs) assay (P53, PGP9.5, SOX2, GAGE7, GUB4-5, MAGEA1, and CAGE), in distinguishing malignant pulmonary diseases from benign ones in routine clinical practice, and construct a neural network diagnostic model with the development of machine learning methods.
A total of 933 patients with lung diseases and 744 with lung nodules were identified. The serum levels of the 7-AABs were tested by an enzyme-linked Immunosorbent assay (ELISA). The primary goal was to assess the sensitivity and specificity of the 7-AABs panel in the detection of lung cancer. ROC curves were used to estimate the diagnosis potential of the 7-AABs in different groups. Next, we constructed a machine learning model based on the 7-AABs and imaging features to evaluate the diagnostic efficacy in lung nodules.
The serum levels of all 7-AABs in the malignant lung diseases group were significantly higher than that in the benign group. The sensitivity and specificity of the 7-AABs panel test were 60.7% and 81.5% in the whole group, and 59.7% and 81.1% in cases with early lung nodules. Comparing to the 7-AABs panel test alone, the neural network model improved the AUC from 0.748 to 0.96 in patients with pulmonary nodules.
The 7-AABs panel may be a promising method for early detection of lung cancer, and we constructed a new diagnostic model with better efficiency to distinguish malignant lung nodules from benign nodules which could be used in clinical practice.
由于低剂量计算机断层扫描（LDCT）筛查在疑似肺癌潜在人群中的推广和普及，肺结节的发病率日益增加。然而，高假阳性率以及对重复CT扫描辐射相关癌症风险的担忧仍然是其广泛应用的主要障碍。在此，我们旨在研究一种名为七种自身抗体（7-AABs）检测（P53、PGP9.5、SOX2、GAGE7、GUB4-5、MAGEA1和CAGE）的非侵入性简单检测方法在常规临床实践中区分恶性肺部疾病和良性肺部疾病的临床价值，并利用机器学习方法构建神经网络诊断模型。
共纳入933例肺部疾病患者和744例肺结节患者。采用酶联免疫吸附测定（ELISA）检测血清中7-AABs水平。主要目的是评估7-AABs检测在肺癌检测中的敏感性和特异性。采用ROC曲线评估7-AABs在不同组中的诊断潜力。接下来，我们基于7-AABs和影像学特征构建机器学习模型，以评估其在肺结节中的诊断效能。
恶性肺部疾病组所有7种自身抗体的血清水平均显著高于良性组。7-AABs检测在全组中的敏感性和特异性分别为60.7%和81.5%，在早期肺结节患者中分别为59.7%和81.1%。与单独的7-AABs检测相比，神经网络模型将肺结节患者的AUC从0.748提高到了0.96。
7-AABs检测可能是早期检测肺癌的一种有前景的方法，我们构建了一种效率更高的新诊断模型，用于区分恶性肺结节和良性结节，并可应用于临床实践。
