In contrast to the declining incidence in older populations, the incidence of very early onset colorectal cancer (VEO-CRC) patients (aged ≤40 years) has been increasing in different regions of the world. In this study, we aimed to establish nomogram models for the prognostic prediction of patients with VEO-CRC for both overall survival (OS) and cancer-specific survival (CSS). Patients diagnosed with VEO-CRC between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were collected and randomly assigned to the training cohort and validation cohort at a ratio of 7:3 for model construction and internal validation. Using univariate and multivariate Cox regression analysis to screen important variables, which were then used to construct a nomogram. The nomogram was evaluated using calibration curves and the receiver operating characteristic (ROC) curves. A total of 3061 patients were included and randomly divided into the training cohort (n = 2145) and validation cohort (n = 916). Five independent prognostic factors, including race, grade, tumor size, AJCC stage, and AJCC T stage were all significantly identified in OS multivariate Cox regression analysis. Meanwhile in CSS, multivariate Cox regression analysis demonstrated that race, grade, tumor size, AJCC stage, AJCC T stage, AJCC N stage, and SEER stage were independent prognostic factors. The calibration plots of the established nomograms indicated high correlations between the predicted and observed results. C-index and ROC analysis implied that our nomogram model has a strong predictive ability. Moreover, nomograms also showed higher C-index values compared to tumor-node-metastasis (TNM) and SEER stages. We established and validated a simple-to-use nomogram to evaluate the 1-, 3-, and 5-year OS and CSS prognosis of patients with VEO-CRC. This tool can assist clinicians to optimize individualized treatment plans.

中文翻译：

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预测极早发性结直肠癌患者总生存期和癌症特异性生存期的预后列线图：基于人群的分析。

与老年人群发病率下降相比，极早发性结直肠癌（VEO-CRC）患者（年龄≤40岁）的发病率在世界不同地区一直在增加。在这项研究中，我们旨在建立列线图模型，用于预测 VEO-CRC 患者的总生存期 (OS) 和癌症特异性生存期 (CSS)。收集了 2010 年至 2015 年间从监测、流行病学和最终结果 (SEER) 数据库中诊断为 VEO-CRC 的患者，并以 7:3 的比例随机分配到训练队列和验证队列，用于模型构建和内部验证。使用单变量和多变量 Cox 回归分析筛选重要变量，然后将其用于构建列线图。使用校准曲线和接受者操作特征 (ROC) 曲线评估列线图。共纳入 3061 名患者，随机分为训练队列（n = 2145）和验证队列（n = 916）。OS 多变量 Cox 回归分析显着确定了五个独立的预后因素，包括种族、分级、肿瘤大小、AJCC 分期和 AJCC T 分期。同时在CSS中，多因素Cox回归分析表明种族、分级、肿瘤大小、AJCC分期、AJCC T分期、AJCC N分期和SEER分期是独立的预后因素。已建立的列线图的校准图表明预测结果和观察结果之间的高度相关性。C-index 和 ROC 分析表明我们的列线图模型具有很强的预测能力。而且，与肿瘤淋巴结转移 (TNM) 和 SEER 阶段相比，列线图还显示出更高的 C 指数值。我们建立并验证了一个简单易用的列线图来评估 VEO-CRC 患者的 1 年、3 年和 5 年 OS 和 CSS 预后。该工具可以帮助临床医生优化个性化治疗计划。