Puren Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, 430000 Hubei, China.
J Immunol Res. 2022 Jul 4;2022:2736676. doi: 10.1155/2022/2736676. eCollection 2022.
Our aim is to make accurate and robust predictions of the risk of postoperative death in young colorectal cancer patients (18-44 years old) by combining tumor characteristics with medical and demographic information about the patient.
We used the SEER database to retrieve young patients diagnosed with colorectal cancer who had undergone surgery between 2010 and 2015 as the study cohort. After excluding cases with missing information, the study cohort was divided in a 7 : 3 ratio into a training dataset and a validation dataset. To assess the predictive ability of each predictor on the prognosis of colorectal cancer patients, we used two steps of Cox univariate analysis and Cox stepwise regression to screen variables, and the screened variables were included in a multifactorial Cox proportional risk regression model for modeling. The performance of the model was tested using calibration curves, decision curves, and area under the curve (AUC) for receiver operating characteristic (ROC).
After excluding cases with missing information ( = 23,606), a total of 11,803 patients were included in the study with a median follow-up time of 45 months (1-119). In the training set, we determined that ethnicity, marital status, insurance status, median annual household income, degree of tumor differentiation, type of pathology, degree of infiltration, and tumor location had independent effects on prognosis. In the training dataset, taking 1 year, 3 years, and 5 years as the time nodes, the areas under the working characteristic curve of subjects are 0.825, 0.851, and 0.839, respectively, and in the validation dataset, they are 0.834, 0.837, and 0.829, respectively.
We trained and validated a model using a large multicenter cohort of young colorectal cancer patients with stable and excellent performance in both training and validation datasets.
通过结合肿瘤特征与患者的医疗和人口统计学信息，对年轻结直肠癌患者（18-44 岁）的术后死亡风险进行准确和稳健的预测。
我们使用 SEER 数据库检索了 2010 年至 2015 年间接受手术治疗的年轻结直肠癌患者作为研究队列。排除信息缺失的病例后，研究队列按 7:3 的比例分为训练数据集和验证数据集。为了评估每个预测因子对结直肠癌患者预后的预测能力，我们使用两步 Cox 单因素分析和 Cox 逐步回归筛选变量，并将筛选出的变量纳入多因素 Cox 比例风险回归模型进行建模。使用校准曲线、决策曲线和接受者操作特征曲线（ROC）下的曲线下面积（AUC）来测试模型的性能。
排除信息缺失的病例（n=23606）后，共有 11803 例患者纳入研究，中位随访时间为 45 个月（1-119）。在训练集中，我们确定种族、婚姻状况、保险状况、中位数年度家庭收入、肿瘤分化程度、病理类型、浸润程度和肿瘤位置对预后有独立影响。在训练数据集，以 1 年、3 年和 5 年为时间节点，受试者的工作特征曲线下面积分别为 0.825、0.851 和 0.839，在验证数据集，它们分别为 0.834、0.837 和 0.829。
我们使用一个大型多中心年轻结直肠癌患者队列进行了训练和验证，该模型在训练和验证数据集中均具有稳定且优异的性能。
