• Preventive medicine · Aug 2024

    Predicting oral and esophageal cancers by one model in a Chinese prospective cohort study.

    • Ping Chen, Wenting Zhao, Sicong Wang, Zilong Bian, Shu Li, Wenyuan Li, Huakang Tu, Chi Pang Wen, and Xifeng Wu.
    • Central Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang University Hangzhou, Zhejiang Province, China.
    • Prev Med. 2024 Aug 28; 189: 108119108119.

    ObjectiveOral and esophageal cancers are both upper gastrointestinal cancers that share a number of risk factors. However, most previous risk prediction models only focused on one of these two types of cancer. There is no single model that could predict both cancers simultaneously. Our objective was to develop a model specifically tailored for oral and esophageal cancers.MethodsFrom 1996 to 2007, a total of 431,460 subjects aged 20 and older without a history of cancer at baseline were included and were monitored for an average duration of 7.3 years in Taiwan, China. A total of 704 cases of oral and esophageal cancers were detected. We utilized both univariate and multivariate COX regression for screening predictors and constructing the model. We evaluated the goodness of fit of the model based on discriminatory accuracy, Harrell's C-index, and calibration.ResultsFinally, we developed a Cox regression model using the twelve most significant variables: age, gender, alcohol consumption, betel chewing, smoking status, history of oral ulceration, educational level, marital status, oropharynx status, family history of nasopharyngeal carcinoma, volume ratio of blood cell, and gamma-glutamyl transferase. The AUC (area under the curve) for the complete model was 0.82. Additionally, the C-index was 0.807 (with a 95 % confidence interval ranging from 0.789 to 0.824) and internal calibration results demonstrated that the model performed well.ConclusionsThis study identified the twelve most significant common risk factors for oral and esophageal cancers and developed a single prediction model that performs well for both types of cancer.Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.

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