Development of a prediction model for pulmonary nodules using circulating tumor cells combined with the uAI platform
ObjectiveTo 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 qua...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1594499/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849712106316759040 |
|---|---|
| author | Dahu Ren Dahu Ren Shuangqing Chen Shicheng Liu Xiaopeng Zhang Wenfei Xue Qingtao Zhao Guochen Duan |
| author_facet | Dahu Ren Dahu Ren Shuangqing Chen Shicheng Liu Xiaopeng Zhang Wenfei Xue Qingtao Zhao Guochen Duan |
| author_sort | Dahu Ren |
| collection | DOAJ |
| description | ObjectiveTo 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.MethodsA 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.ResultsComparison 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).ConclusionThe 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. |
| format | Article |
| id | doaj-art-dcc07c5314b84d378feabcedf564c60e |
| institution | DOAJ |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-dcc07c5314b84d378feabcedf564c60e2025-08-20T03:14:23ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-07-011510.3389/fonc.2025.15944991594499Development of a prediction model for pulmonary nodules using circulating tumor cells combined with the uAI platformDahu Ren0Dahu Ren1Shuangqing Chen2Shicheng Liu3Xiaopeng Zhang4Wenfei Xue5Qingtao Zhao6Guochen Duan7Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, Hebei, ChinaGraduate School, Hebei Medical University, Shijiazhuang, Hebei, ChinaDepartment of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, Hebei, ChinaDepartment of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, Hebei, ChinaDepartment of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, Hebei, ChinaDepartment of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, Hebei, ChinaDepartment of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, Hebei, ChinaDepartment of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, Hebei, ChinaObjectiveTo 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.MethodsA 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.ResultsComparison 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).ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fonc.2025.1594499/fullpulmonary noduleartificial intelligencecirculating tumor cellsearly lung adenocarcinomaprediction mode |
| spellingShingle | Dahu Ren Dahu Ren Shuangqing Chen Shicheng Liu Xiaopeng Zhang Wenfei Xue Qingtao Zhao Guochen Duan Development of a prediction model for pulmonary nodules using circulating tumor cells combined with the uAI platform Frontiers in Oncology pulmonary nodule artificial intelligence circulating tumor cells early lung adenocarcinoma prediction mode |
| title | Development of a prediction model for pulmonary nodules using circulating tumor cells combined with the uAI platform |
| title_full | Development of a prediction model for pulmonary nodules using circulating tumor cells combined with the uAI platform |
| title_fullStr | Development of a prediction model for pulmonary nodules using circulating tumor cells combined with the uAI platform |
| title_full_unstemmed | Development of a prediction model for pulmonary nodules using circulating tumor cells combined with the uAI platform |
| title_short | Development of a prediction model for pulmonary nodules using circulating tumor cells combined with the uAI platform |
| title_sort | development of a prediction model for pulmonary nodules using circulating tumor cells combined with the uai platform |
| topic | pulmonary nodule artificial intelligence circulating tumor cells early lung adenocarcinoma prediction mode |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1594499/full |
| work_keys_str_mv | AT dahuren developmentofapredictionmodelforpulmonarynodulesusingcirculatingtumorcellscombinedwiththeuaiplatform AT dahuren developmentofapredictionmodelforpulmonarynodulesusingcirculatingtumorcellscombinedwiththeuaiplatform AT shuangqingchen developmentofapredictionmodelforpulmonarynodulesusingcirculatingtumorcellscombinedwiththeuaiplatform AT shichengliu developmentofapredictionmodelforpulmonarynodulesusingcirculatingtumorcellscombinedwiththeuaiplatform AT xiaopengzhang developmentofapredictionmodelforpulmonarynodulesusingcirculatingtumorcellscombinedwiththeuaiplatform AT wenfeixue developmentofapredictionmodelforpulmonarynodulesusingcirculatingtumorcellscombinedwiththeuaiplatform AT qingtaozhao developmentofapredictionmodelforpulmonarynodulesusingcirculatingtumorcellscombinedwiththeuaiplatform AT guochenduan developmentofapredictionmodelforpulmonarynodulesusingcirculatingtumorcellscombinedwiththeuaiplatform |