A multi-variable predictive warning model for cervical cancer using clinical and SNPs data
Cervical cancer is the fourth most common cancer among female worldwide. Early detection and intervention are essential. This study aims to construct an early predictive warning model for cervical cancer and precancerous lesions utilizing clinical data and simple nucleotide polymorphisms (SNPs). Cli...
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Published in | Frontiers in medicine Vol. 11; p. 1294230 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
22.02.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Cervical cancer is the fourth most common cancer among female worldwide. Early detection and intervention are essential. This study aims to construct an early predictive warning model for cervical cancer and precancerous lesions utilizing clinical data and simple nucleotide polymorphisms (SNPs).
Clinical data and germline SNPs were collected from 472 participants. Univariate logistic regression, least absolute shrinkage selection operator (LASSO), and stepwise regression were performed to screen variables. Logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), extreme gradient boosting(XGBoost) and neural network(NN) were applied to establish models. The receiver operating characteristic (ROC) curve was used to compare the models' efficiencies. The performance of models was validated using decision curve analysis (DCA).
The LR model, which included 6 SNPs and 2 clinical variables as independent risk factors for cervical carcinogenesis, was ultimately chosen as the most optimal model. The DCA showed that the LR model had a good clinical application.
The predictive model effectively foresees cervical cancer risk using clinical and SNP data, aiding in planning timely interventions. It provides a transparent tool for refining clinical decisions in cervical cancer management. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Shujun Gao, Fudan University, China Xiaomo Xiong, University of Cincinnati, United States These authors have contributed equally to this work Edited by: Jing He, Guangzhou Medical University, China |
ISSN: | 2296-858X 2296-858X |
DOI: | 10.3389/fmed.2024.1294230 |