Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features

• Published in: Journal of translational medicine Vol. 22; no. 1; pp. 984 - 14
• Main Authors: Yang, Meng, Yu, Huansha, Feng, Hongxiang, Duan, Jianghui, Wang, Kaige, Tong, Bing, Zhang, Yunzhi, Li, Wei, Wang, Ye, Liang, Chaoyang, Sun, Hongliang, Zhong, Dingrong, Wang, Bei, Chen, Huang, Gong, Chengxiang, He, Qiye, Su, Zhixi, Liu, Rui, Zhang, Peng
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 31.10.2024; BioMed Central; BMC
• Subjects: Adult; Aged; Analysis; Biomarkers, Tumor - blood; Care and treatment; Cell-free DNA methylation; CT imaging; Diagnosis; Diagnosis, Differential; DNA Methylation; Female; Health aspects; High-throughput screening (Biochemical assaying); Humans; Imaging; Integrated model; Lung cancer; Lung Neoplasms - blood; Lung Neoplasms - diagnosis; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - genetics; Lung Neoplasms - pathology; Male; Methylation; Middle Aged; Multiple Pulmonary Nodules - blood; Multiple Pulmonary Nodules - diagnostic imaging; Protein profiling; Pulmonary nodules classification; Risk factors; ROC Curve; Solitary Pulmonary Nodule - blood; Solitary Pulmonary Nodule - diagnosis; Solitary Pulmonary Nodule - diagnostic imaging; Sulfites; Tomography, X-Ray Computed; China; Pulmonary nodules classification; Integrated model; Protein profiling; Imaging; Cell-free DNA methylation
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-024-05723-5

Accurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5-10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules. Blood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules. Our results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5-10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones. Our study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making. This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic. gov/ct2/show/NCT05432128 .