Background:Opportunistic screening is one major screening approach for esophageal squamous cell carcinoma (ESCC). We aimed to develop a score-based risk stratification model to assess the risk of ESCC and precancerous lesions in opportunistic screening and to validate it in an external population.Methods:The study was a secondary analysis of a published esophageal cancer screening trial. The trial was conducted in 39 secondary or tertiary hospitals in China, with 14,597 individuals including 71 high-grade intraepithelial neoplasia (HGIN) and 182 ESCC, enrolled for opportunistic screening. Additionally, questionnaires and endoscopy were performed. The primary outcome was histology-confirmed high-grade esophageal lesions, including HGIN and ESCC. The predictors were selected using univariable and multivariable logistic regression. Model performance was primarily measured with the area under the receiver operating characteristic curve (AUROC).Results:The score-based prediction model contained 8 variables on a 21-point scale. The model demonstrated an AUROC of 0.833 (95% CI, 0.803–0.862) and 0.828 (95% CI, 0.793–0.864) for detecting high-grade lesions in the training and validation cohorts, respectively. Using the cut-off score determined in the training cohort (≥9), the sensitivity reached 70.0% (95% CI, 50.6–85.3%), 81.3% (95% CI, 63.6–92.8%), and 81.1% (95% CI, 64.9–92.0%) in the validation cohort for detecting HGIN, early ESCC, and advanced ESCC, respectively, at a specificity of 76.4% (95%CI, 75.4–77.4%). The score-based model exhibited satisfactory calibration in the calibration plots. The model could result in 75.6% fewer individuals subjected to endoscopy.Conclusions:This score-based model demonstrated superior discrimination for esophageal high-grade lesions. It has the potential to inform referral decisions in an opportunistic screening setting.
背景：机会性筛查是食管鳞状细胞癌（ESCC）的主要筛查方式之一。本研究旨在开发一种基于评分的风险分层模型，用于评估机会性筛查中ESCC及癌前病变的风险，并在外部人群中验证其效能。 方法：本研究对一项已发表的食管癌筛查试验进行二次分析。该试验在中国39家二级或三级医院开展，共纳入14,597名参与者，其中包括71例高级别上皮内瘤变（HGIN）和182例ESCC病例，均接受机会性筛查。研究同时收集了问卷调查数据并进行了内镜检查。主要结局指标为经组织学证实的高级别食管病变，包括HGIN和ESCC。通过单变量和多变量逻辑回归筛选预测因子。模型性能主要通过受试者工作特征曲线下面积（AUROC）进行评估。 结果：基于评分的预测模型包含8个变量，总分21分。该模型在训练队列和验证队列中检测高级别病变的AUROC分别为0.833（95% CI：0.803–0.862）和0.828（95% CI：0.793–0.864）。采用训练队列确定的截断分值（≥9分）时，模型在验证队列中检测HGIN、早期ESCC和进展期ESCC的灵敏度分别达到70.0%（95% CI：50.6–85.3%）、81.3%（95% CI：63.6–92.8%）和81.1%（95% CI：64.9–92.0%），特异性为76.4%（95% CI：75.4–77.4%）。校准曲线显示该评分模型具有良好校准度。应用该模型可减少75.6%的内镜检查人数。 结论：该评分模型对食管高级别病变具有优异的鉴别能力，在机会性筛查场景中有潜力为临床转诊决策提供依据。
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