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Title: [Establishment of artificial neural network model for predicting lymph node metastasis in patients with stage Ⅱ-Ⅲ gastric cancer].
Author: Xue Z, Lu J, Lin J, Huang CM, Li P, Xie JW, Wang JB, Lin JX, Chen QY, Zheng CH.
Journal: Zhonghua Wei Chang Wai Ke Za Zhi; 2022 Apr 25; 25(4):327-335. PubMed ID: 35461201.
Abstract:
Objective: To establish a neural network model for predicting lymph node metastasis in patients with stage II-III gastric cancer. Methods: Case inclusion criteria: (1) gastric adenocarcinoma diagnosed by pathology as stage II-III (the 8th edition of AJCC staging); (2) no distant metastasis of liver, lung and abdominal cavity in preoperative chest film, abdominal ultrasound and upper abdominal CT; (3) undergoing R0 resection. Case exclusion criteria: (1) receiving preoperative neoadjuvant chemotherapy or radiotherapy; (2) incomplete clinical data; (3) gastric stump cancer.Clinicopathological data of 1231 patients with stage II-III gastric cancer who underwent radical surgery at the Fujian Medical University Union Hospital from January 2010 to August 2014 were retrospectively analyzed. A total of 1035 patients with lymph node metastasis were confirmed after operation, and 196 patients had no lymph node metastasis. According to the postoperative pathologic staging. 416 patients (33.8%) were stage Ⅱ and 815 patients (66.2%) were stage III. Patients were randomly divided into training group (861/1231, 69.9%) and validation group (370/1231, 30.1%) to establish an artificial neural network model (N+-ANN) for the prediction of lymph node metastasis. Firstly, the Logistic univariate analysis method was used to retrospectively analyze the case samples of the training group, screen the variables affecting lymph node metastasis, determine the variable items of the input point of the artificial neural network, and then the multi-layer perceptron (MLP) to train N+-ANN. The input layer of N+-ANN was composed of the variables screened by Logistic univariate analysis. Artificial intelligence analyzed the status of lymph node metastasis according to the input data and compared it with the real value. The accuracy of the model was evaluated by drawing the receiver operating characteristic (ROC) curve and obtaining the area under the curve (AUC). The ability of N+-ANN was evaluated by sensitivity, specificity, positive predictive values, negative predictive values, and AUC values. Results: There were no significant differences in baseline data between the training group and validation group (all P>0.05). Univariate analysis of the training group showed that preoperative platelet to lymphocyte ratio (PLR), preoperative systemic immune inflammation index (SII), tumor size, clinical N (cN) stage were closely related to postoperative lymph node metastasis. The N+-ANN was constructed based on the above variables as the input layer variables. In the training group, the accuracy of N+-ANN for predicting postoperative lymph node metastasis was 88.4% (761/861), the sensitivity was 98.9% (717/725), the specificity was 32.4% (44/136), the positive predictive value was 88.6% (717/809), the negative predictive value was 84.6% (44/52), and the AUC value was 0.748 (95%CI: 0.717-0.776). In the validation group, N+-ANN had a prediction accuracy of 88.4% (327/370) with a sensitivity of 99.7% (309/310), specificity of 30.0% (18/60), positive predictive value of 88.0% (309/351), negative predictive value of 94.7% (18/19), and an AUC of 0.717 (95%CI:0.668-0.763). According to the individualized lymph node metastasis probability output by N+-ANN, the cut-off values of 0-50%, >50%-75%, >75%-90% and >90%-100% were applied and patients were divided into N0 group, N1 group, N2 group and N3 group. The overall prediction accuracy of N+-ANN for pN staging in the training group and the validation group was 53.7% and 54.1% respectively, while the overall prediction accuracy of cN staging for pN staging in the training group and the validation group was 30.1% and 33.2% respectively, indicating that N+-ANN had a better prediction than cN stage. Conclusions: The N+-ANN constructed in this study can accurately predict postoperative lymph node metastasis in patients with stage Ⅱ-Ⅲ gastric cancer. The N+-ANN based on individualized lymph node metastasis probability has better accurate prediction for pN staging as compared to cN staging. 目的： 建立可预测Ⅱ~Ⅲ期胃癌患者淋巴结转移的神经网络模型，并探讨其预测价值。 方法： 病例纳入标准：（1）经病理确诊为Ⅱ~Ⅲ期（第8版AJCC分期）胃腺癌；（2）术前胸片、腹部超声及上腹部CT等检查无肝、肺、腹腔等远处转移；（3）行R(0)切除。病例排除标准：（1）术前接受过新辅助化疗或放疗；（2）一般临床资料不完整；（3）残胃癌。回顾性收集2010年1月至2014年8月期间在福建医科大学附属协和医院胃外科接受根治性切除术的1 231例Ⅱ~Ⅲ期胃癌患者的临床病理资料。全组共1 035例患者经术后证实淋巴结转移，196例患者未出现淋巴结转移。416例（33.8%）术后病理分期为Ⅱ期，815例（66.2%）为Ⅲ期。全组患者被随机分为建模组861例（69.9%）和验证组370例（30.1%）。先运用Logistic单因素分析方法，对建模组的病例样本进行回顾性分析，筛查影响淋巴结转移的变量，确定人工神经网络输入节点的变量项目，再使用多层感知器（MLP）训练N+-ANN。N+-ANN由Logistic单因素分析筛选出的变量构成输入层。人工智能依据输入数据分析患者淋巴结转移状态，并与真实值进行比较。通过绘制受试者操作特性（ROC）曲线、获取曲线下面积（AUC）来评估模型的准确性。 结果： 建模组与验证组临床资料的比较，差异均无统计学意义（均P>0.05）。建模组单因素分析结果显示，术前血小板淋巴细胞比值（PLR）、术前系统性免疫性炎性指数（SII）、肿瘤大小、临床N（cN）分期与患者出现淋巴结转移有关。将以上因素连同术前中性粒细胞淋巴细胞比值（NLR）、术前糖类抗原19-9、术前癌胚抗原、肿瘤位置、临床T（cT）分期作为输入层变量构建N+-ANN。建模组N+-ANN对术后淋巴转移预测准确率为88.4%（761/861），灵敏度为98.9%（717/725），特异度为32.4%（44/136），阳性预测值为88.6%（717/809），阴性预测值为84.6%（44/52），AUC值为0.748（95%CI：0.717~0.776）；而验证组，N+-ANN的预测准确率为88.4%（327/370），模型灵敏度为99.7%（309/310），特异度为30.0%（18/60），阳性预测值为88.0%（309/351），阴性预测值为94.7%（18/19），AUC值为0.717（95%CI：0.668~0.763）。根据N+-ANN所输出的个体化淋巴结转移概率，取截点0~50%、>50%~75%、>75%~90%、>90%~100%，将患者分为N(0)组、N(1)组、N(2)组、N(3)组。建模组和验证组的N+-ANN对pN分期总体预测准确率分别为53.7%和54.1%，而cN分期对pN分期的总体预测准确率仅为30.1%和33.2%。 结论： 本研究构建的N+-ANN能准确预测Ⅱ~Ⅲ期胃癌患者的淋巴结转移情况。基于N+-ANN的个体化淋巴结转移概率相较于cN分期，对pN分期预测的准确性更高。.

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