Department of Geriatric VIP NO.1, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang Province, China.
Department of Gastroenterology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang Province, China.
PLoS One. 2020 Dec 31;15(12):e0244869. doi: 10.1371/journal.pone.0244869. eCollection 2020.
The diagnosis of gastric cancer mainly relies on endoscopy, which is invasive and costly. The aim of this study is to develop a predictive model for the diagnosis of gastric cancer based on noninvasive characteristics.
To construct a predictive model for the diagnosis of gastric cancer with high accuracy based on noninvasive characteristics.
A retrospective study of 709 patients at Zhejiang Provincial People's Hospital was conducted. Variables of age, gender, blood cell count, liver function, kidney function, blood lipids, tumor markers and pathological results were analyzed. We used gradient boosting decision tree (GBDT), a type of machine learning method, to construct a predictive model for the diagnosis of gastric cancer and evaluate the accuracy of the model.
Of the 709 patients, 398 were diagnosed with gastric cancer; 311 were health people or diagnosed with benign gastric disease. Multivariate analysis showed that gender, age, neutrophil lymphocyte ratio, hemoglobin, albumin, carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125) and carbohydrate antigen 199 (CA199) were independent characteristics associated with gastric cancer. We constructed a predictive model using GBDT, and the area under the receiver operating characteristic curve (AUC) of the model was 91%. For the test dataset, sensitivity was 87.0% and specificity 84.1% at the optimal threshold value of 0.56. The overall accuracy was 83.0%. Positive and negative predictive values were 83.0% and 87.8%, respectively.
We construct a predictive model to diagnose gastric cancer with high sensitivity and specificity. The model is noninvasive and may reduce the medical cost.
胃癌的诊断主要依赖于内窥镜检查，这种方法具有侵入性且费用高昂。本研究旨在基于非侵入性特征建立一种用于胃癌诊断的预测模型。
构建一种基于非侵入性特征的胃癌诊断预测模型，以提高准确性。
对浙江省人民医院的 709 例患者进行回顾性研究。分析年龄、性别、血细胞计数、肝功能、肾功能、血脂、肿瘤标志物和病理结果等变量。我们使用梯度提升决策树（GBDT），一种机器学习方法，构建用于胃癌诊断的预测模型，并评估模型的准确性。
在 709 例患者中，有 398 例被诊断为胃癌；311 例为健康人群或被诊断为良性胃部疾病。多变量分析表明，性别、年龄、中性粒细胞与淋巴细胞比值、血红蛋白、白蛋白、癌胚抗原（CEA）、糖类抗原 125（CA125）和糖类抗原 199（CA199）是与胃癌相关的独立特征。我们使用 GBDT 构建了一个预测模型，该模型的受试者工作特征曲线下面积（AUC）为 91%。对于测试数据集，最佳阈值为 0.56 时，模型的敏感性为 87.0%，特异性为 84.1%。整体准确率为 83.0%。阳性预测值和阴性预测值分别为 83.0%和 87.8%。
我们构建了一个具有高敏感性和特异性的预测模型来诊断胃癌。该模型是非侵入性的，可能会降低医疗成本。
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