基于胃癌风险分层模型鉴别癌前病变与胃炎
Discrimination between Precancerous Gastric Lesions and Gastritis Using a Gastric Cancer Risk Stratification Model.
机构信息
University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Department of Epidemiology, Chapel Hill, NC, USA.
Duke University, Department of Population Health Sciences, and Duke Cancer Institute, Cancer Risk, Detection, and Interception Program, Durham, NC, USA.
出版信息
Asian Pac J Cancer Prev. 2023 Mar 1;24(3):935-943. doi: 10.31557/APJCP.2023.24.3.935.
BACKGROUND
Seropositivity to certain Helicobacter pylori proteins may affect development of gastric lesions that could become cancerous. Previously, we developed a model of gastric cancer risk including gender, age, HP0305 sero-positivity, HP1564 sero-positivity, UreA antibody titer and serologically defined chronic atrophic gastritis (termed: "Lasso model").
METHODS
We evaluated the Lasso model's ability to discriminate individuals with precancerous gastric lesions (n=320) from individuals with superficial or mild atrophic gastritis (n=226) in Linqu County, China, a population at high risk for gastric cancer. We also compared its performance to the ABC Method, a gastric cancer risk stratification tool currently used in East Asia.
RESULTS
For distinguishing precancerous lesions from those with gastritis, the receiver operating characteristic curve had an area under the curve (AUC) of 73.41% (95% CI: 69.10%, 77.71%) and, at Youden's Index, a sensitivity of 78.44% (59.38%, 82.50%) and specificity of 64.72% (95% CI: 58.85%, 81.42%). Positive predictive value (PPV) was 75.38% (72.78%, 82.51%). Specificity, AUC and PPV were significantly greater (p < 0.05) than those of the ABC Method. When specificity was held constant, the Lasso model had greater sensitivity, PPV and negative predictive value (NPV) than the ABC Method. However, adjusting the ABC Method for age and gender negated the Lasso model's significant improvement in AUC.
CONCLUSIONS
The Lasso model for gastric cancer risk prediction can classify precancerous lesions with significantly greater AUC than the ABC Method and, at constant specificity, with greater sensitivity, PPV and NPV. However, adding age and gender to the ABC Method, as included in the Lasso model, substantially improved its performance and negated the Lasso model's advantage.
背景
某些幽门螺杆菌蛋白的血清阳性可能会影响发展为可能癌变的胃部病变。此前，我们开发了一种包括性别、年龄、HP0305 血清阳性、HP1564 血清阳性、UreA 抗体滴度和血清学定义的慢性萎缩性胃炎的胃癌风险模型（称为“lasso 模型”）。
方法
我们评估了 lasso 模型在中国林曲县（胃癌高危人群）从患有癌前胃病变的个体（n=320）和患有浅表或轻度萎缩性胃炎的个体（n=226）中区分癌前病变的能力。我们还比较了它与 ABC 方法（目前在东亚使用的胃癌风险分层工具）的性能。
结果
在区分癌前病变和胃炎方面，受试者工作特征曲线下面积（AUC）为 73.41%（95%CI：69.10%，77.71%），在尤登指数下，敏感性为 78.44%（59.38%，82.50%），特异性为 64.72%（95%CI：58.85%，81.42%）。阳性预测值（PPV）为 75.38%（72.78%，82.51%）。特异性、AUC 和 PPV均显著高于 ABC 方法（p<0.05）。当特异性保持不变时，lasso 模型的敏感性、PPV 和阴性预测值（NPV）均高于 ABC 方法。然而，调整 ABC 方法以适应年龄和性别，否定了 lasso 模型在 AUC 方面的显著改善。
结论
与 ABC 方法相比，lasso 模型在预测胃癌风险方面，分类癌前病变的 AUC 显著提高，在保持特异性的情况下，敏感性、PPV 和 NPV均提高。然而，在 ABC 方法中加入年龄和性别，就像 lasso 模型所包含的那样，大大提高了它的性能，并否定了 lasso 模型的优势。
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