1. Introduction
A risk evaluation model is crucial for efficient cancer screening among high-risk populations.1 Environmental factors, such as education level, age at sexual debut, parity, and body mass index (BMI), are related to the risk of developing cervical cancer. Some genetic factors in which specific polymorphisms correlate with cervical cancer have been reported Several other genes, such as codon 72 of p53, codon 31 of p21, and fragile histidine triad (FHIT), have been examined for their association with cervical cancer. Cervical carcinogenesis is a multifactorial disease that may result from environmental and genetic factors. To improve the predictive accuracy for the determing the cervical cancer risk, we developed the risk evaluation model comprising both genetic and environmental factors. In previous studies, we proposed a risk evaluation model for cervical cancer based on several well-known environmental contributors, such as infection with high-risk genotype(s) of human papillomavirus (HPV),3–5 young age at first parturition,6 and low educationlevel ofthe subject andtheir spouse.7,8 Emerging evidence suggests that human leukocyte antigen (HLA) class II alleles are associated with cervical cancer.9 In this study, we combined HLA class II alleles with several risk factors to establish a risk evaluation model using multiple logistic regression analysis and an artificial neural network. This new evaluation model could significantly improve the accuracy of risk evaluation for cervical cancer.
1. Introduction
A risk evaluation model is crucial for efficient cancer screening among high-risk populations.1 Environmental factors, such as education level, age at sexual debut, parity, and body mass index (BMI), are related to the risk of developing cervical cancer. Some genetic factors in which specific polymorphisms correlate with cervical cancer have been reported Several other genes, such as codon 72 of p53, codon 31 of p21, and fragile histidine triad (FHIT), have been examined for their association with cervical cancer. Cervical carcinogenesis is a multifactorial disease that may result from environmental and genetic factors. To improve the predictive accuracy for the determing the cervical cancer risk, we developed the risk evaluation model comprising both genetic and environmental factors. In previous studies, we proposed a risk evaluation model for cervical cancer based on several well-known environmental contributors, such as infection with high-risk genotype(s) of human papillomavirus (HPV),3–5 young age at first parturition,6 and low educationlevel ofthe subject andtheir spouse.7,8 Emerging evidence suggests that human leukocyte antigen (HLA) class II alleles are associated with cervical cancer.9 In this study, we combined HLA class II alleles with several risk factors to establish a risk evaluation model using multiple logistic regression analysis and an artificial neural network. This new evaluation model could significantly improve the accuracy of risk evaluation for cervical cancer.
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