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

Ping Chen, Central Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang University Hangzhou, Zhejiang Province, China.
Wenting Zhao, Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Sicong Wang, Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Zilong Bian, Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Shu Li, Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Wenyuan Li, Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Huakang Tu, Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China; Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China.
Chi Pang Wen, National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China. Electronic address: cwengood@nhri.edu.tw.
Xifeng Wu, Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China; Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China; School of Medicine and Health Science, George Washington University, Washington DC, USA. Electronic address: xifengw@zju.edu.cn.

Document Type

Journal Article

Publication Date

12-1-2024

Journal

Preventive medicine

Volume

189

DOI

10.1016/j.ypmed.2024.108119

Keywords

Cancer; Esophageal cancer; Oral cancer; Prediction model; Risk factors

Abstract

OBJECTIVE: Oral 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. METHODS: From 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. RESULTS: Finally, 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. CONCLUSIONS: This 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.

Department

Biostatistics and Bioinformatics

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