构建直肠癌肠系膜下动脉(IMA)根部淋巴结转移的预测模型并验证其准确性。

回顾性分析2014年1月至2019年3月行直肠癌手术的156例患者资料，所有患者均行IMA根部淋巴结清扫。统计分析采用SPSS 20.0软件，对与IMA根部淋巴结转移相关的临床病理因素进行单因素分析(采用χ²检验)及多因素Logistic回归分析。并构建IMA根部淋巴结转移的预测模型，通过描绘受试者工作特征曲线(ROC)并计算曲线下面积(AUC)从而来评估模型的预测能力，P<0.05表示差异具有统计学意义。并以术后病理诊断结果为金标准对预测模型进行验证。

在156例患者中，IMA根部淋巴结转移21例，转移率为13.5%；平均每例送检淋巴结为2.3个(1～9个)，共送检淋巴结359个，其中有转移的淋巴结有72个，转移度为20.1%。多因素回归分析结果显示，肿瘤大小、pT分期、分化程度、是否有肝转移及术前血清CEA水平是IMA根部淋巴结转移的独立危险因素。根据Logistic回归分析各变量的回归系数构建预测模型，通过绘制ROC曲线，计算出AUC值为0.856(95%CI 0.825～0.886)，当最佳临界值为2.49时对IMA根部淋巴结转移具有预测价值，此时该预测模型的灵敏度为90.21%，特异度为86.20%，约登指数为0.56。以术后病理诊断为金标准，验证预测模型，灵敏度为85.71%，特异度为99.26%，两者灵敏度和特异度差异无统计学意义(P>0.05)。

基于Logistic多因素回归分析建立预测模型具有较高的灵敏度和特异性，对直肠癌肠系膜下动脉根部淋巴结转移的患者具有较高的预测价值，为临床实践提供一定的理论支撑。

To establish a predictive model for NO.253 lymph nodes metastasis of rectal cancer around inferior mesenteric artery (IMA) and to verify its accuracy.

The data of 156 patients underwent rectal cancer surgery from January 2014 to March 2019 were analyzed retrospectively.and all of 156 patients underwent NO.253 lymph nodes dissection. Statistical analysis were performed by using SPSS 20.0 software and were examined by using univariate analysis (χ² test) and multivariate logistic regression analysis of clinicopathological factors related to NO.253 lymph nodes metastasis. To establish a prediction model of NO.253 lymph node metastasis, by drawing the receiver operating characteristic curve (ROC) and calculating the area under the curve (AUC) to evaluate the predictive ability of the model. A P value of <0.05 was considered as statistically significant difference . And the prediction model was verified by the postoperative pathological diagnosis results as the gold standard.

Among the 156 patients, 21 cases (13.5%) of NO.253 lymph nodes metastasis ; The average harvested NO.253 lymph nodes was 2.3 (1~9), with a total of NO.359 lymph nodes were dissected, including 72 positive lymph nodes (20.1%). Logistic multivariate regression analysis was performed on clinically significant pathological data with univariate analysis. The results showed that tumor size, pT stage, degree of differentiation, presence or absence of liver metastases, and serum CEA levels were independent risk factors for NO.253 lymph nodes metastasis. According to the logistic regression analysis of the regression coefficients of each variable, a prediction model is constructed, and by plotting the ROC curve, the AUC value is calculated as 0.856 (95% CI 0.825-0.886). When the optimal critical value of Y is 0.49, it has predictive value for NO.253 lymph nodes metastasis. At this time, the sensitivity of the prediction model is 90.21%, with specificity of 86.20%, and the Jordan index of 0.56. By using the postoperative pathological diagnosis as the gold standard to verify the prediction model, with the sensitivity of 85.71%, and the specificity of 99.26%, without significant difference (P>0.05).

This study establishes a prediction model based on Logistic multivariate regression analysis with high sensitivity and specificity, with a high predictive value for NO.253 lymph nodes metastasis of rectal cancer.