Office of National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China.
Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Shaanxi, P.R. China.
Cancer Prev Res (Phila). 2022 May 3;15(5):309-318. doi: 10.1158/1940-6207.CAPR-21-0426.
Risk prediction models for gastric cancer could identify high-risk individuals in the general population. The objective of this study was to systematically review the available evidence about the construction and verification of gastric cancer predictive models. We searched PubMed, Embase, and Cochrane Library databases for articles that developed or validated gastric cancer risk prediction models up to November 2021. Data extracted included study characteristics, predictor selection, missing data, and evaluation metrics. Risk of bias (ROB) was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). We identified a total of 12 original risk prediction models that fulfilled the criteria for analysis. The area under the receiver operating characteristic curve (AUC) ranged from 0.73 to 0.93 in derivation sets (n = 6), 0.68 to 0.90 in internal validation sets (n = 5), 0.71 to 0.92 in external validation sets (n = 7). The higher-performing models usually include age, salt preference, Helicobacter pylori, smoking, body mass index, family history, pepsinogen, and sex. According to PROBAST, at least one domain with a high ROB was present in all studies mainly due to methodologic limitations in the analysis domain. In conclusion, although some risk prediction models including similar predictors have displayed sufficient discriminative abilities, many have a high ROB due to methodologic limitations and are not externally validated efficiently. Future prediction models should adherence to well-established standards and guidelines to benefit gastric cancer screening.
Through systematical reviewing available evidence about the construction and verification of gastric cancer predictive models, we found that most models have a high ROB due to methodologic limitations and are not externally validated efficiently. Future prediction models are supposed to adherence to well-established standards and guidelines to benefit gastric cancer screening.
目的：系统评价胃癌风险预测模型的构建和验证。
检索策略：检索 PubMed、Embase 和 Cochrane Library 数据库，纳入截至 2021 年 11 月，构建或验证胃癌风险预测模型的研究。提取研究特征、预测因子选择、缺失数据和评估指标等信息。使用预测模型风险偏倚评估工具（PROBAST）评价偏倚风险。
结果：共纳入 12 个原始风险预测模型。在推导集（n=6）、内部验证集（n=5）和外部验证集（n=7）中，受试者工作特征曲线下面积（AUC）范围分别为 0.730.93、0.680.90 和 0.71~0.92。表现较好的模型通常包括年龄、盐偏好、幽门螺杆菌、吸烟、体质量指数、家族史、胃蛋白酶原和性别。根据 PROBAST，所有研究均至少有一个领域存在高偏倚风险，主要是由于分析领域的方法学限制。
结论：尽管一些包含相似预测因子的风险预测模型具有足够的判别能力，但由于方法学限制和未能有效进行外部验证，大多数模型的偏倚风险高。未来的预测模型应遵循既定的标准和指南，以利于胃癌筛查。
