AUTHOR=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 

TITLE=Development of Diagnosis Model for Early Lung Nodules Based on a Seven Autoantibodies Panel and Imaging Features

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.883543

DOI=10.3389/fonc.2022.883543

ISSN=2234-943X

ABSTRACT=Abstract 
Background: There is an 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, high rate of false-positive and concern of radiation-related cancer risk of repeated CT scanning remains major obstacle to its wide application. Here, we aimed to investigate the clinical value of a non-invasive and simple test, named 7 autoantibodies (AABs) assay (P53, PGP9.5, SOX2, GAGE7, GUB4-5, MAGEA1 and CAGE), in distinguishing malignant pulmonary disease from benign ones in routine clinical practice, and construct a neural network diagnostic model with the development of machine learning methods.
Method: 933 patients with lung diseases and 744 of which with lung nodules were identified. The serum levels of the 7-AABs were tested by Enzyme-linked Immunosorbent Assay (ELISA). The primary goal was to assess the sensitivity and specificity of the 7-AABs panel in detection of lung cancer. ROC curves were to estimate the diagnosis potential of 7-AABs in different groups. Next, constructing a machine learning model based on 7-AABs and imaging features evaluated diagnostic efficacy in lung nodules.
Results: The serum levels of all 7 AABs in malignant lung diseases group were significantly higher than that in benign group. The sensitivity and specificity of the 7-AABs panel test were 60.7% and 81.5% in whole group, and 59.7% and 81.1% in cases with early lung nodules. Comparing to 7-AABs panel test alone, the neural network model improved the AUC from 0.748 to 0.96 in patients with pulmonary nodules.
Conclusion: 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 which could be used to clinical practice.