Suppr超能文献

基于评分的模型在机会性筛查人群中评估食管鳞状细胞癌及癌前病变风险的开发与验证

Development and Validation of a Score-Based Model for Estimating Esophageal Squamous Cell Carcinoma and Precancerous Lesions Risk in an Opportunistic Screening Population.

作者信息

Bian Yan, Gao Ye, Jiang Huishan, Li Qiuxin, Wang Yuling, Zhang Yanrong, Li Zhaoshen, Xu Jinfang, Wang Luowei

机构信息

Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai 200433, China.

Changhai Clinical Research Unit, Changhai Hospital, Naval Medical University, Shanghai 200433, China.

出版信息

Cancers (Basel). 2025 Jun 25;17(13):2138. doi: 10.3390/cancers17132138.

Abstract

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. 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). 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. 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.

摘要

机会性筛查是食管鳞状细胞癌(ESCC)的一种主要筛查方法。我们旨在开发一种基于评分的风险分层模型,以评估机会性筛查中ESCC和癌前病变的风险,并在外部人群中进行验证。该研究是对一项已发表的食管癌筛查试验的二次分析。该试验在中国的39家二级或三级医院进行,14597人参加了机会性筛查,其中包括71例高级别上皮内瘤变(HGIN)和182例ESCC。此外,还进行了问卷调查和内镜检查。主要结局是组织学确诊的高级别食管病变,包括HGIN和ESCC。通过单变量和多变量逻辑回归选择预测因素。模型性能主要用受试者操作特征曲线下面积(AUROC)来衡量。基于评分的预测模型包含8个变量,范围为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)。使用在训练队列中确定的截断分数(≥9),在验证队列中检测HGIN、早期ESCC和晚期ESCC的敏感性分别达到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%)。基于评分的模型在校准图中表现出令人满意的校准。该模型可使接受内镜检查的人数减少75.6%。这种基于评分的模型对食管高级别病变具有优异的区分能力。它有可能为机会性筛查中的转诊决策提供依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c16d/12249110/90f304866bfc/cancers-17-02138-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验