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Abstract：
OBJECTIVE: The aim of study was to screen factors associated with the overall survival of colorectal cancer patients with lymph nodes metastasis who received neoadjuvant therapy and construct a nomogram model.
METHODS: All enrolled subjects of the SEER database were randomly assigned to the training and testing group in a ratio of 3:2. The patients of Tangdu Hospital were seemed as validation group. Univariate cox regression analysis, lasso regression and random forest survival were used to screen variables related to the survival of advanced CRC patients received neoadjuvant therapy in the training group. Area under curves were adopted to evaluate the 1,3,5-year prediction value of the optimal model in three cohorts. Calibration curves were drawn to observe the prediction accuracy of the nomogram model. Decision curve analysis was used to assess the potential clinical value of the nomogram model.
RESULTS: A total of 1833 subjects were enrolled in this study. After random allocation, 1055 cases of the SEER database served as the training group, 704 cases as the testing group and 74 patients from our center as the external validation group. Variables were screened by univariate cox regression used to construct a nomogram survival prediction model, including M, age, chemotherapy, CEA, perineural invasion, tumor size, LODDS, liver metastasis and radiation. The AUCs of the model for predicting 1-year OS in the training group, testing and validation group were 0.765 (0.703,0.827), 0.772 (0.697,0.847) and 0.742 (0.601,0.883), predicting 3-year OS were 0.761 (0.725,0.780), 0.742 (0.699,0.785), 0.733 (0.560,0.905) and 5-year OS were 0.742 (0.711,0.773), 0.746 (0.709,0.783), 0.838 (0.670,0.980), respectively. The calibration curves showed the difference between prediction probability of the model and the actual survival was not significant in three cohorts and the decision curve analysis revealed the practice clinical application value. And the prediction value of model was better for young CRC than older CRC patients.
CONCLUSIONS: A nomogram model including LODDS for the prognosis of advanced CRC received neoadjuvant therapy was constructed and verified based on the SEER database and single center practice. The accuracy and potential clinical application value of the model performed well, and the model had better predictive value for EOCRC than LOCRC.
METHODS: All enrolled subjects of the SEER database were randomly assigned to the training and testing group in a ratio of 3:2. The patients of Tangdu Hospital were seemed as validation group. Univariate cox regression analysis, lasso regression and random forest survival were used to screen variables related to the survival of advanced CRC patients received neoadjuvant therapy in the training group. Area under curves were adopted to evaluate the 1,3,5-year prediction value of the optimal model in three cohorts. Calibration curves were drawn to observe the prediction accuracy of the nomogram model. Decision curve analysis was used to assess the potential clinical value of the nomogram model.
RESULTS: A total of 1833 subjects were enrolled in this study. After random allocation, 1055 cases of the SEER database served as the training group, 704 cases as the testing group and 74 patients from our center as the external validation group. Variables were screened by univariate cox regression used to construct a nomogram survival prediction model, including M, age, chemotherapy, CEA, perineural invasion, tumor size, LODDS, liver metastasis and radiation. The AUCs of the model for predicting 1-year OS in the training group, testing and validation group were 0.765 (0.703,0.827), 0.772 (0.697,0.847) and 0.742 (0.601,0.883), predicting 3-year OS were 0.761 (0.725,0.780), 0.742 (0.699,0.785), 0.733 (0.560,0.905) and 5-year OS were 0.742 (0.711,0.773), 0.746 (0.709,0.783), 0.838 (0.670,0.980), respectively. The calibration curves showed the difference between prediction probability of the model and the actual survival was not significant in three cohorts and the decision curve analysis revealed the practice clinical application value. And the prediction value of model was better for young CRC than older CRC patients.
CONCLUSIONS: A nomogram model including LODDS for the prognosis of advanced CRC received neoadjuvant therapy was constructed and verified based on the SEER database and single center practice. The accuracy and potential clinical application value of the model performed well, and the model had better predictive value for EOCRC than LOCRC.
摘要：
目的：本研究旨在筛选结直肠癌淋巴结转移患者接受新辅助治疗后的总生存期相关因素，并构建列线图模型。
方法：将SEER数据库中所有登记的受试者以3:2的比例随机分配到训练和测试组。唐都医院的患者被视为验证组。单变量cox回归分析，套索回归和随机森林生存率用于筛选与训练组接受新辅助治疗的晚期CRC患者生存率相关的变量。采用曲线下面积评估三个队列中最佳模型的1、3、5年预测值。绘制校准曲线以观察列线图模型的预测准确性。决策曲线分析用于评估列线图模型的潜在临床价值。
结果：本研究共纳入1833名受试者。随机分配后，SEER数据库的1055例作为训练组，704例作为试验组,74例来自本中心的患者作为外部验证组。通过单变量cox回归筛选变量，构建列线图生存预测模型，包括M,年龄,化疗,CEA,神经周浸润,肿瘤大小,LODDS,肝转移和放疗。训练组1年OS预测模型的AUC，测试和验证组为0.765(0.703,0.827)，0.772（0.697,0.847）和0.742（0.601,0.883），预测3年OS为0.761(0.725,0.780)，0.742（0.699,0.785），0.733(0.560,0.905)，5年OS为0.742(0.71,0.773)，0.746(0.709,0.783),0.838(0.670,0.980),分别。校正曲线显示模型预测概率与实际生存率在3个队列中差异不显著,决策曲线分析显示实际临床应用价值。模型对年轻CRC的预测价值优于老年CRC患者。
结论：基于SEER数据库和单中心实践，构建并验证了包含LODDS的晚期CRC新辅助治疗预后的列线图模型。模型的准确性和潜在的临床应用价值,模型对EOCRC的预测价值优于LOCRC。
方法：将SEER数据库中所有登记的受试者以3:2的比例随机分配到训练和测试组。唐都医院的患者被视为验证组。单变量cox回归分析，套索回归和随机森林生存率用于筛选与训练组接受新辅助治疗的晚期CRC患者生存率相关的变量。采用曲线下面积评估三个队列中最佳模型的1、3、5年预测值。绘制校准曲线以观察列线图模型的预测准确性。决策曲线分析用于评估列线图模型的潜在临床价值。
结果：本研究共纳入1833名受试者。随机分配后，SEER数据库的1055例作为训练组，704例作为试验组,74例来自本中心的患者作为外部验证组。通过单变量cox回归筛选变量，构建列线图生存预测模型，包括M,年龄,化疗,CEA,神经周浸润,肿瘤大小,LODDS,肝转移和放疗。训练组1年OS预测模型的AUC，测试和验证组为0.765(0.703,0.827)，0.772（0.697,0.847）和0.742（0.601,0.883），预测3年OS为0.761(0.725,0.780)，0.742（0.699,0.785），0.733(0.560,0.905)，5年OS为0.742(0.71,0.773)，0.746(0.709,0.783),0.838(0.670,0.980),分别。校正曲线显示模型预测概率与实际生存率在3个队列中差异不显著,决策曲线分析显示实际临床应用价值。模型对年轻CRC的预测价值优于老年CRC患者。
结论：基于SEER数据库和单中心实践，构建并验证了包含LODDS的晚期CRC新辅助治疗预后的列线图模型。模型的准确性和潜在的临床应用价值,模型对EOCRC的预测价值优于LOCRC。
