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Journal Of Modern Oncology[ISSN:1672-4992/CN:61-1415/R]

Issue:

2023 04

Page:

663-669

Research Field:

Publishing date:

Info

Title:

Establishment and evaluation of EGFR mutation prediction model based on serum tumor markers and medical imaging in patients with lung adenocarcinoma

Author(s):

WANG Qiutong¹; WU Shuang¹; WANG Ming²; CHEN Ling¹; AN Yuezhen²; JIANG Junjie²; WANG Hansen³; ZHAO Rui⁴

1.Cangzhou Medical College,Hebei Cangzhou 061001,China;2.Cangzhou Central Hospital,Hebei Cangzhou 061001,China;3.Cangzhou Maternal and Child Health Care Hospital,Hebei Cangzhou 061001,China;4.Beijing Chao-Yang Hospital,Capital Medical University,Beijing 100020,China.

Keywords:

adenocarcinoma; tumor marker; coagulation function; EGFR mutations; medical imaging; prediction model

PACS:

R734.2

DOI:

10.3969/j.issn.1672-4992.2023.04.013

Abstract:

Objective:To establish a clinical model for predicting EGFR mutation status in patients with lung adenocarcinoma using tumor markers,coagulation function indicators and imaging related indicators,and to verify the predictive ability.Methods:A total of 225 non-small cell lung cancer (NSCLC) patients admitted to the oncology department of Cangzhou Central Hospital from 2018 to 2019 were randomly selected as the research object of the modeling group.The independent factors that affected EGFR mutation in NSCLC patients were screened by multivariate Logistic regression analysis,and a regression model was established to draw the receiver operating characteristic (ROC) curve for predicting EGFR mutation probability in NSCLC patients.A total of 75 patients with lung adenocarcinoma admitted to the oncology department of Cangzhou Central Hospital from 2020 to 2021 were randomly selected as the validation group to conduct internal verification of the predictive model.Results:Multivariate Logistic regression analysis showed that density shadow of ground glass,aerated bronchial sign,vascular cluster sign and CEA were independent influencing factors for predicting EGFR mutation in NSCLC patients (P<0.05).The multi-factor Logistic regression modelexpression was P=eX/(1+eX),X=-2.196+（0.036×CEA）+（0.984×ground glass opacity）+（1.923×air bronchogram）+（1.206×vessel convergence）.The prediction model ROC curve was 0.824［95%CI（0.767，0.881）］.The sensitivity and the specificity were 78.6% and 77.5%.The 75 patients factors in the validation group were brought into the prediction model,and the results showed that the area under the ROC curve was 0.762.The H-L goodness-of-fit test showed that χ2 =3.140，P=0.872．Conclusion:There are many risk factors affecting EGFR gene mutations in lung adenocarcinoma patients and clinical evaluation should be carried out in a timely and effective manner．A clinic prediction model constructedin this study has a good evaluation performance,which is worthy of clinical promotion.

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