Development and Validation of an Esophageal Squamous Cell Carcinoma Risk Prediction Model for Rural Chinese: Multicenter Cohort Study - Han, Junming; Wang, Lijie; Zhang, Huan; Ma, Siqi; Li, Yan; Wang, Zhongli; Zhu, Gaopei; Zhao, Deli; Wang, Jialin; Xue, Fuzhong (Frontiers in Oncology, 2021)
Background: There are rare prediction models for esophageal squamous cell carcinoma (ESCC) for rural Chinese population. We aimed to develop and validate a prediction model for ESCC based on cohort study for the population. Methods: Data of 115686 participants was collected from EC early diagnosis and treatment of cancer program as derivation cohort while data of 54750 participants was collected as validation cohort. Risk factors considered included age, sex, smoking status, alcohol drinking status, body mass index (BMI), tea drinking status, marital status, annual household income, source of drinking water, education level and diet habit. Cox proportional hazards model was used to develop ESCC prediction model at 5 years. Calibration ability, discrimination ability and decision curve analysis were analyzed in both derivation and validation cohort. A score model was developed based on prediction model. Results: 186 cases were diagnosed during 556949.40 person years follow-up in derivation cohort while 120 cases from 277302.70 in validation cohort. Prediction model included variables: age, sex, alcohol drinking status, BMI, tea drinking status and fresh fruit. The model had good discrimination and calibration performance: R^2, D statistic and Harrell’s C statistic of prediction model were 43.56%, 1.70 and 0.798 in derivation cohort and 45.19%, 1.62 and 0.787 in validation cohort. The calibration analysis showed good coherence between predicted probabilities and observed probabilities while decision curve analysis showed clinical usefulness. The score model was as follows: age (3 for 45-49 years old; 4 for 50-54 years old; 7 for 55-59 years old; 9 for 60-64 years; 10 for 65-69 years), sex (5 for men), BMI (1 for <=25), alcohol drinking status (2 for alcohol drinkers), tea drinking status (2 for tea drinkers) and fresh fruit (2 for never) and showed good discrimination ability with aera under the curve and its 95% confidence interval of 0.792(0.761,0.822) in deviation cohort and 0.773(0.736,0.811) in validation cohort. The calibration analysis showed great coherence between predicted probabilities and observed probabilities. Conclusions: We developed and validated an ESCC prediction model using cohort study with good discrimination and calibration capability which can be used for EC screening for rural Chinese population.