NDLI: Development and validation of reassigned CEA, CYFRA21-1 and NSE-based models for lung cancer diagnosis and prognosis prediction

Content Provider	Springer Nature : BioMed Central
Author	Yuan, Jingmin Sun, Yan Wang, Ke Wang, Zhiyi Li, Duo Fan, Meng Bu, Xiang Chen, Jun Wu, Zhiquan Geng, Hui Wu, Jiamei Xu, Ying Chen, Mingwei Ren, Hui
Abstract	Background The majority of lung cancer(LC) patients are diagnosed at advanced stage with a poor prognosis. However, there is still no ideal diagnostic and prognostic prediction model for lung cancer. Methods Data of CEA, CYFRA21-1 and NSE test of patients with LC and benign lung diseases (BLDs) or healthy people from Physical Examination Center was collected. Samples were divided into three data sets as needed. Reassign three kinds of tumor markers (TMs) according to their distribution characteristics in different populations. Diagnostic and prognostic models were thus established, and independent validation was conducted with other data sets. Results The diagnostic prediction model showed good discrimination ability: the area under the receiver operating characteristic curve (AUC) differentiated LC from healthy people and BLDs (diagnosed within 2 months), being 0.88 and 0.84 respectively. Meanwhile, the prognostic prediction model did great in prediction: AUC in training data set and test data set were 0.85 and 0.8 respectively. Conclusion Reassigned CEA, CYFRA21-1 and NSE can effectively predict the diagnosis and prognosis of LC. Compared with the same TMs that were considered individually, this diagnostic prediction model can identify high-risk population for LC screening more accurately. The prognostic prediction model could be helpful in making more scientific treatment and follow-up plans for patients.
Related Links	https://bmccancer.biomedcentral.com/counter/pdf/10.1186/s12885-022-09728-5.pdf
Ending Page	7
Page Count	7
Starting Page	1
File Format	HTM / HTML
ISSN	14712407
DOI	10.1186/s12885-022-09728-5
Journal	BMC Cancer
Issue Number	1
Volume Number	22
Language	English
Publisher	BioMed Central
Publisher Date	2022-06-22
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Surgical Oncology Health Promotion and Disease Prevention Biomedicine Medicine Public Health Lung cancer CEA CYFRA21-1 NSE Screening Prognosis Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Cancer Research Oncology Genetics
Journal Impact Factor	3.4/2023
5-Year Journal Impact Factor	3.8/2023

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