ORIGINAL RESEARCH article
Front. Oncol.
Sec. Thoracic Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1594499
This article is part of the Research TopicAdvancing Precision Medicine in Lung Cancer: Integrating Genomics, Liquid Biopsy and Novel Diagnostic ToolsView all 5 articles
Development of a Prediction Model for Pulmonary Nodules Using Circulating Tumor Cells Combined with the uAI Platform
Provisionally accepted
- 1Hebei Medical University, Shijiazhuang, China
- 2Hebei General Hospital, Shijiazhuang, Hebei Province, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Objective: To explore the clinical application value of combining circulating tumor cell (CTC) detection with the artificial intelligence imaging software "uAI platform" in predicting the pathological nature of pulmonary nodules (PN). Develop a joint diagnostic system based on the uAI platform and quantitative detection of CTCs, enable simultaneous classification of pulmonary nodules as benign or malignant and assess the degree of infiltration. Methods: A total of 76 patients with pulmonary nodules undergoing surgical treatment were enrolled. Preoperatively, three-dimensional nodule risk stratification (low、 medium、 high risk) was performed using the uAI platform, and CTC high-throughput detection was conducted. Key indicators were selected through multi-group comparisons (Benign、Malignant、Invasive subgroups) and logistic regression analysis. A multi-dimensional nomogram model was constructed, and its clinical utility was evaluated using ROC curves and clinical decision curves. Results: Comparison between benign and malignant pulmonary nodule groups revealed significant differences in the risk stratification of the uAI platform (proportion of high-risk: 75.61% vs 34.29%) and in the median value of CTC quantitative detection (P<0.001). Multivariate logistic regression analysis demonstrated that high-risk classification by uAI and CTC quantitative detection were independent predictors of malignancy in pulmonary nodules (P<0.05). The nomogram model constructed based on these factors exhibited excellent discrimination, and its combined diagnostic performance was significantly better than that of single indicators (AUC=0.805 vs uAI 0.730/CTC 0.743). Conclusion: The combined uAI-CTC model breaks through the limitations of single-dimension diagnosis, enabling risk stratification of malignant pulmonary nodules and quantitative assessment of infiltration, providing evidence-based support for clinical treatment strategies.
Keywords: lung cancer, Prognostic model, circulating tumor cells, artificial intelligence Pulmonary nodule, artificial intelligence, Early lung adenocarcinoma, Prediction mode
Received: 16 Mar 2025; Accepted: 07 Jul 2025.
Copyright: © 2025 Ren, Chen, Liu, Zhang, Xue, Zhao and Duan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Qingtao Zhao, Hebei General Hospital, Shijiazhuang, 050051, Hebei Province, China
Guochen Duan, Hebei General Hospital, Shijiazhuang, 050051, Hebei Province, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.