Performance of Prediction Models for Esophageal Squamous Cell Carcinoma in General Population: A Systematic Review and External Validation Study.

INTRODUCTION

Prediction models for esophageal squamous cell carcinoma (ESCC) need to be proven effective in the target population before they can be applied to population-based endoscopic screening to improve cost-effectiveness. We have systematically reviewed ESCC prediction models applicable to the general population and performed external validation and head-to-head comparisons in a large multicenter prospective cohort including 5 high-risk areas of China (Fei Cheng, Lin Zhou, Ci Xian, Yang Zhong, and Yan Ting).

METHODS

Models were identified through a systematic review and validated in a large population-based multicenter prospective cohort that included 89,753 participants aged 40-69 years who underwent their first endoscopic examination between April 2017 and March 2021 and were followed up until December 31, 2022. Model performance in external validation was estimated based on discrimination and calibration. Discrimination was assessed by C-statistic (concordance statistic), and calibration was assessed by calibration plot and Hosmer-Lemeshow test.

RESULTS

The systematic review identified 15 prediction models that predicted severe dysplasia and above lesion (SDA) or ESCC in the general population, of which 11 models (4 SDA and 7 ESCC) were externally validated. The C-statistics ranged from 0.67 (95% confidence interval 0.66-0.69) to 0.70 (0.68-0.71) of the SDA models, and the highest was achieved by Liu et al (2020) and Liu et al (2022). The C-statistics ranged from 0.51 (0.48-0.54) to 0.74 (0.71-0.77), and Han et al (2023) had the best discrimination of the ESCC models. Most models were well calibrated after recalibration because the calibration plots coincided with the x = y line.

DISCUSSION: Several prediction models showed moderate performance in external validation, and the prediction models may be useful in screening for ESCC. Further research is needed on model optimization, generalization, implementation, and health economic evaluation.