دورية أكاديمية

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

التفاصيل البيبلوغرافية
العنوان: Development and validation of reassigned CEA, CYFRA21-1 and NSE-based models for lung cancer diagnosis and prognosis prediction
المؤلفون: 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
المساهمون: General project in the field of social development in Shaanxi Province, The Key research and development projects of Shaanxi Province
المصدر: BMC Cancer ; volume 22, issue 1 ; ISSN 1471-2407
بيانات النشر: Springer Science and Business Media LLC
سنة النشر: 2022
مصطلحات موضوعية: Cancer Research, Genetics, Oncology
الوصف: 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.
نوع الوثيقة: article in journal/newspaper
اللغة: English
DOI: 10.1186/s12885-022-09728-5
DOI: 10.1186/s12885-022-09728-5.pdf
DOI: 10.1186/s12885-022-09728-5/fulltext.html
الإتاحة: https://doi.org/10.1186/s12885-022-09728-5Test
حقوق: https://creativecommons.org/licenses/by/4.0Test ; https://creativecommons.org/licenses/by/4.0Test
رقم الانضمام: edsbas.BBC02395
قاعدة البيانات: BASE