Faculty, Staff and Student Publications
Language
English
Publication Date
12-8-2025
Journal
Nature Communications
DOI
10.1038/s41467-025-65930-6
PMID
41360981
PMCID
PMC12686440
PubMedCentral® Posted Date
12-8-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Early detection of lung cancer is crucial for improving patient outcomes. However, accurately diagnosing invasive pulmonary nodules and predicting tumor invasiveness remain major clinical challenges. Given the established role of immune dysfunction in cancer development, we hypothesize that peripheral immune profiling could provide a strategy for managing pulmonary nodules. In this multi-center, prospective study, we combine peripheral immune profiling via mass cytometry with machine learning algorithms to develop an integrated pulmonary nodule management platform. This platform accurately distinguishes invasive from non-invasive pulmonary nodules (AUC = 0.952), outperforming established clinical and radiomics-based models. Furthermore, it effectively predicts tumor invasiveness, differentiating minimally invasive from invasive adenocarcinoma (AUC = 0.949), thereby offering valuable guidance for surgical decision-making. In conclusion, the platform demonstrates substantial clinical utility and holds significant promise as a precision tool for future management of pulmonary nodules.
Keywords
Humans, Lung Neoplasms, Prospective Studies, Male, Female, Single-Cell Analysis, Multiple Pulmonary Nodules, Middle Aged, Aged, Solitary Pulmonary Nodule, Machine Learning, Neoplasm Invasiveness, Lung cancer, Cancer screening, Predictive markers
Published Open-Access
yes
Recommended Citation
Xia, Yang; Zhu, Yin; Zhang, Sai; et al., "Precise Diagnosis of Small Invasive Pulmonary Nodules Driven by Single-Cell Immune Signatures in Peripheral Blood" (2025). Faculty, Staff and Student Publications. 6125.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6125
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Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons