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首页> 外文期刊>Gastroenterology research and practice >Using Machine Learning to Predict Progression in the Gastric Precancerous Process in a Population from a Developing Country Who Underwent a Gastroscopy for Dyspeptic Symptoms
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Using Machine Learning to Predict Progression in the Gastric Precancerous Process in a Population from a Developing Country Who Underwent a Gastroscopy for Dyspeptic Symptoms

机译：利用机器学习预测从开发胃镜检查的发展中国家胃癌患者中胃癌癌前过程的进展

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Background. Gastric cancer is the fourth most common cancer and the third most common cause of cancer deaths worldwide. Morbidity and mortality from gastric cancer may be decreased by identification of those that are at high risk for progression in the gastric precancerous process so that they can be monitored over time for early detection and implementation of preventive strategies. Method. Using machine learning, we developed prediction models for gastric precancerous progression in a population from a developing country with a high rate of gastric cancer who underwent gastroscopies for dyspeptic symptoms. In the data imputed for completeness, we divided the data into a training and a validation test set. Using the training set, we used the random forest method to rank potential predictors based on their predictive importance. Using predictors identified by the random forest method, we conducted best subset linear regressions with the leave-one-out cross-validation approach to select predictors for overall progression and progression to dysplasia or cancer. We validated the models in the test set using leave-one-out cross-validation. Results. We observed for all models that complete intestinal metaplasia and incomplete intestinal metaplasia were the strongest predictors for further progression in the precancerous process. We also observed that a diagnosis of no gastritis, superficial gastritis, or antral diffuse gastritis at baseline was a predictor of no progression in the gastric precancerous process. The sensitivities and specificities were 86% and 79% for the general model and 100% and 82% for the location-specific model, respectively. Conclusion. We developed prediction models to identify gastroscopy patients that are more likely to progress in the gastric precancerous process, among whom routine follow-up gastroscopies can be targeted to prevent gastric cancer. Future external validation is needed.
机译：背景。胃癌是第四次常见的癌症和全世界癌症死亡的第三个最常见的原因。通过鉴定胃癌癌前过程中进展高风险的那些，可能会降低来自胃癌的发病率和死亡率，以便在预防策略的早期检测和实施时可以监测它们。方法。采用机器学习，我们开发了胃癌中胃癌患者的预测模型，从发展中国家患有高胃癌的胃癌，患有消化症状的胃癌。在算入完整性的数据中，我们将数据划分为培训和验证测试集。使用培训集，我们使用随机森林方法基于预测重要性来排列潜在预测因子。使用由随机森林方法识别的预测器，我们与休假次横跨验证方法进行了最佳的副线性回归，以选择预测因子，以便对发育不良或癌症进行全面进展和进展。我们使用休假交叉验证验证了测试集中的模型。结果。我们观察到完全肠道细胞和不完全肠道成平面的所有模型是最强烈的预测因子，用于进一步进程中进一步进程。我们还观察到，基线上没有胃炎，浅表性胃炎或嗜睡弥漫性胃炎是胃癌前述过程中没有进展的预测因子。敏感性和特异性分别为一般模型的86％和79％，分别为特定于位置模型的100％和82％。结论。我们开发了预测模型，以鉴定胃镜检查患者，这些患者更有可能在胃癌前述过程中进展，其中常规随访胃镜可以靶向以防止胃癌。需要未来的外部验证。

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