Research on gastric cancer lymph node metastasis prediction model based on machine learning algorithms
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摘要:目的
基于4种机器学习(ML)算法构建胃癌淋巴结转移的预测模型并验证。
方法回顾性收集531例胃癌根治术患者的临床资料, 按3∶1比例将患者随机分为训练集399例和测试集132例。通过单因素分析筛选胃癌淋巴结转移的特征选择变量，分别建立逻辑回归、随机森林、K-邻近算法、支持向量机算法模型并进行变量重要性排序。将所有ML算法模型在测试集中进行验证，绘制受试者工作特征(ROC)曲线，基于曲线下面积(AUC)、灵敏度、特异度、准确度确定最优ML算法模型。基于最优ML算法模型的变量重要性排序构建列线图模型，通过ROC曲线、校准曲线、决策曲线评价列线图模型的区分能力、校准能力和临床适用性。
结果4种ML算法模型比较结果显示，随机森林模型为最优算法模型，其在训练集中的准确度、灵敏度、特异度分别为72.7%、69.9%、75.0%, AUC为0.803, 其在测试集中的准确度、灵敏度、特异度分别为64.4%、66.7%、62.5%, AUC为0.751。基于随机森林算法模型的变量构建列线图模型, ROC曲线显示列线图模型在训练集、测试集中的AUC分别为0.721、0.776, 校准曲线和决策曲线显示列线图模型在训练集与测试集中均有较好的校准能力和临床适用性。
结论随机森林模型是4种ML算法模型中的最优算法模型，基于随机森林模型构建的列线图模型能够较准确地预测胃癌淋巴结转移风险，从而更好地指导临床诊断和治疗决策。
Abstract:ObjectiveTo establish and validate a prediction model for gastric cancer lymph node metastasis based on four machine learning (ML) algorithms.
MethodsA retrospective analysis was conducted on clinical data of 531 patients who underwent radical gastrectomy. The patients were randomly divided into training set (399 patients) and test set (132 patients) in a ratio of 3 to 1. Univariate analysis was used to screen for variables associated with gastric cancer lymph node metastasis, and Logistic regression, random forest, K-nearest neighbor algorithm, and support vector machine algorithm models were established to rank the importance of variables. All ML algorithm models were validated in the test set, and receiver operating characteristic (ROC) curves were plotted. The optimal ML algorithm model was determined based on the area under the curve (AUC), sensitivity, specificity, and accuracy. A nomogram model was constructed based on the variable importance ranking of the optimal ML algorithm model. The discrimination, calibration, and clinical applicability of the nomogram model were evaluated using ROC curves, calibration curves, and decision curves.
ResultsThe results of the comparison of the four ML algorithm models showed that the random forest model was the optimal algorithm model. The accuracy, sensitivity, and specificity of the random forest model in the training set were 72.7%, 69.9%, and 75.0%, respectively, with an AUC of 0.803. The accuracy, sensitivity, and specificity of the random forest model in the test set were 64.4%, 66.7%, and 62.5%, respectively, with an AUC of 0.751. A nomogram model was constructed based on the variables of the random forest algorithm model. The ROC curve showed that the AUCs of the nomogram model in the training set and test set were 0.721 and 0.776, respectively. Calibration curves and decision curves showed that the nomogram model had good calibration and clinical applicability in both the training set and test set.
ConclusionThe random forest model is the optimal algorithm model among the four ML algorithm models. The nomogram model based on the random forest model can accurately predict the risk of gastric cancer lymph node metastasis, thereby better guiding clinical diagnosis and treatment decisions.
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图 2 不同算法模型在训练集中的混淆矩阵和ROC曲线
A: 逻辑回归模型的混淆矩阵; B: 随机森林模型的混淆矩阵; C: K-邻近算法模型的混淆矩阵; D: 支持向量机模型的混淆矩阵; E: 逻辑回归模型的ROC曲线; F: 随机森林模型的ROC曲线; G: K-邻近算法模型的ROC曲线; H: 支持向量机模型的ROC曲线。
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图 3 不同算法模型在测试集中的混淆矩阵和ROC曲线
A: 逻辑回归模型的混淆矩阵; B: 随机森林模型的混淆矩阵; C: K-邻近算法模型的混淆矩阵; D: 支持向量机模型的混淆矩阵; E: 逻辑回归模型的ROC曲线; F: 随机森林模型的ROC曲线; G: K-邻近算法模型的ROC曲线; H: 支持向量机模型的ROC曲线。
表 1 训练集与测试集患者临床资料比较[n(%)][M(P25, P75)]
临床资料 分类 训练集(n=399) 测试集(n=132) χ2/Z P 性别 男 312(78.2) 102(77.3) 0.049 0.825 女 87(21.8) 30(22.7) 年龄/岁 58.0(50.0, 63.5) 59.0(50.5, 64.0) -0.650 0.516 民族 汉族 304(76.2) 106(80.3) 2.365 0.500 回族 42(10.5) 8(6.1) 藏族 37(9.3) 13(9.8) 其他 16(4.0) 5(3.8) 体质量指数/(kg/m2) 22.5(20.4, 24.9) 22.5(20.7, 24.9) -0.086 0.932 高血压 否 348(87.2) 116(87.9) 0.039 3 0.843 是 51(12.8) 16(12.1) 糖尿病 否 382(95.7) 121(91.7) 3.294 0.070 是 17(4.3) 11(8.3) 白蛋白/(g/L) 39.3(36.8, 41.9) 39.3(36.7, 41.5) -0.684 0.494 肿瘤标志物 AFP/(ng/mL) 2.2(1.7, 3.1) 2.3(1.6, 3.3) -0.106 0.916 CEA/(ng/mL) 2.0(1.3, 3.5) 2.2(1.4, 3.7) -1.169 0.242 CA125/(U/mL) 11.0(8.1, 16.4) 11.1(8.4, 14.6) -0.461 0.645 CA199/(U/mL) 7.2(3.3, 18.4) 9.8(3.2, 24.7) -1.524 0.127 肿瘤直径 < 2 cm 40(10.0) 10(7.6) 0.698 0.404 ≥2 cm 359(90.0) 122(92.4) 肿瘤位置 胃上部1/3 93(23.3) 40(30.3) 2.590 0.274 胃中部1/3 282(70.7) 85(64.4) 胃下部1/3 24(6.0) 7(5.3) 大体分型 溃疡型 350(87.7) 115(87.1) 1.355 0.508 隆起型 30(7.5) 13(9.8) 浸润型 19(4.8) 4(3.0) 分化程度 高分化 29(7.3) 17(12.9) 4.803 0.091 中分化 221(55.4) 63(47.7) 低分化 149(37.3) 52(39.4) 脉管侵犯 否 148(37.1) 53(40.2) 0.394 0.530 是 251(62.9) 79(59.8) 神经侵犯 否 174(43.6) 64(48.5) 0.953 0.329 是 225(56.4) 68(51.5) T分期 T1a期 13(3.3) 6(4.5) 5.143 0.273 T1b期 5(1.3) 4(3.0) T2期 122(30.6) 44(33.3) T3期 32(8.0) 5(3.8) T4期 227(56.9) 73(55.3) 淋巴结转移 否 183(45.9) 60(45.5) 0.006 0.935 是 216(54.1) 72(54.5) AFP: 甲胎蛋白; CEA: 癌胚抗原; CA125: 糖类抗原125; CA199: 糖类抗原199。 
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表 2 无淋巴结转移组与淋巴结转移组患者临床资料比较[n(%)][M(P25, P75)]
特征 分类 无淋巴结转移组(n=243) 淋巴结转移组(n=288) χ2/Z P 性别 男 192(79.0) 222(77.1) 0.285 0.593 女 51(21.0) 66(22.9) 年龄/岁 58.0(50.0, 63.0) 59.0(50.0, 64.0) -0.593 0.553 民族 汉族 195(80.2) 215(74.7) 2.370 0.499 回族 20(8.2) 30(10.4) 藏族 20(8.2) 30(10.4) 其他 8(3.3) 13(4.5) 体质量指数/(kg/m2) 22.4(20.7, 24.7) 22.5(20.2, 25.0) -0.189 0.850 高血压 否 213(87.7) 251(87.2) 0.030 0.862 是 30(12.3) 37(12.8) 糖尿病 否 231(95.1) 272(94.4) 0.101 0.751 是 12(4.9) 16(5.6) 白蛋白/(g/L) 39.4(36.8, 41.5) 39.3(36.6, 42.1) -0.065 0.948 肿瘤标志物 AFP/(ng/mL) 2.3(1.8, 3.2) 2.2(1.6, 3.0) -1.219 0.223 CEA/(ng/mL) 2.0(1.3, 3.1) 2.0(1.3, 4.0) -1.328 0.184 CA125/(U/mL) 10.5(7.8, 14.3) 11.4(8.4, 17.0) -2.385 0.017 CA199/(U/mL) 7.3(3.3, 14.4) 9.2(3.2, 28.3) -1.927 0.054 肿瘤直径 < 2 cm 29(11.9) 21(7.3) 3.330 0.068 ≥2 cm 214(88.1) 267(92.7) 肿瘤位置 胃上部1/3 59(24.3) 74(25.7) 0.203 0.903 胃中部1/3 169(69.5) 198(68.8) 胃下部1/3 15(6.2) 16(5.6) 大体分型 溃疡型 216(88.9) 249(86.5) 8.582 0.014 隆起型 23(9.5) 20(6.9) 浸润型 4(1.6) 19(6.6) 分化程度 高分化 24(9.9) 22(7.6) 1.318 0.517 中分化 132(54.3) 152(52.8) 低分化 87(35.8) 114(39.6) 脉管侵犯 否 139(57.2) 62(21.5) 71.299 < 0.001 是 104(42.8) 226(78.5) 神经侵犯 否 140(57.6) 98(34.0) 29.644 < 0.001 是 103(42.4) 190(66.0) T分期 T1a期 16(6.6) 3(1.0) 41.015 < 0.001 T1b期 6(2.5) 3(1.0) T2期 100(41.2) 66(22.9) T3期 16(6.6) 21(7.3) T4期 105(43.2) 195(67.7)
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表 3 不同机器学习算法模型在训练集和测试集中的预测效能
数据集 模型 准确度/% 灵敏度/% 特异度/% AUC 训练集 逻辑回归模型 69.2 60.7 76.4 0.727 随机森林模型 72.7 69.9 75.0 0.803 K-邻近算法模型 68.9 58.5 77.8 0.772 支持向量机模型 71.9 64.5 78.2 0.792 测试集 逻辑回归模型 71.9 66.7 76.4 0.766 随机森林模型 64.4 66.7 62.5 0.751 K-邻近算法模型 58.3 48.3 66.7 0.612 支持向量机模型 60.6 55.0 65.3 0.637
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