ArticleFrontiers in oncology2022
Development of Diagnosis Model for Early Lung Nodules Based on a Seven Autoantibodies Panel and Imaging Features.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- The early diagnostic value of lung cancer autoantibodies and tumor markers in lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Preoperative evaluation of solitary pulmonary nodules and adenocarcinoma invasiveness using ultra-high-resolution computed tomography and multidimensional liquid biopsy: a prospective exploratory study.Frontiers in oncology · 2026Article
- Application of liquid biopsy in differentiating lung cancer from benign pulmonary nodules (Review).International journal of molecular medicine · 2025Review
- A novel composite model for distinguishing benign and malignant pulmonary nodules.Clinical and experimental medicine · 2025Article
- Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer.Frontiers in immunology · 2025Article
- Advances in modelling the risk of benign and malignant lung nodules.Frontiers in oncology · 2025Review
- Clinical application of serum seven tumour-associated autoantibodies in patients with pulmonary nodules.Heliyon · 2024Article
- Application value of the automated machine learning model based on modified CT index combined with serological indices in the early prediction of lung cancer.Frontiers in public health · 2024Article
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14 authors.
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Abstract
Background: 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. Method: 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. Results: 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. 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 nodules which could be used in clinical practice.
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