Li Wei, Liu Yiting
Department of Oncology, Shuyang Hospital, The Affiliated Shuyang Hospital of Xuzhou Medical University, No. 9 Yingbin Avenue, Shucheng Town, Shuyang County, Suqian, 223600, Jiangsu, China.
Discov Oncol. 2024 Nov 6;15(1):621. doi: 10.1007/s12672-024-01498-9.
Utilizing the SEER database, we developed a competing risk model along with a nomogram designed for the early identification of colon cancer-specific mortality (CSM) risk.
Clinical and pathological information, along with other significant data, were obtained from the SEER database. Patients were randomly divided into a training set and a validation set. We investigated the independent factors affecting CSM among colon cancer patients using univariate and multivariate analyses within a competing risk framework, ultimately developing a predictive tool for CSM in colon cancer.
Involving 40,261 individuals diagnosed with colon cancer, our study included 10,397 deaths directly due to the disease and an additional 5,828 from other causes. We used a competing risk model to predict cancer-specific mortality (CSM) in these patients. For the training dataset, the model's area under the curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific survival (CSS) was 0.835 (95% confidence interval [CI] 0.826 to 0.844), 0.849 (95% CI 0.843 to 0.855), and 0.843 (95% CI 0.836 to 0.850), respectively. In the validation group, the AUC values for the same time periods were 0.846 (95% CI 0.833 to 0.860), 0.853 (95% CI 0.843 to 0.862), and 0.846 (95% CI 0.835 to 0.856), respectively. In comparison, traditional survival analysis yielded higher cumulative CSM rates over time than those provided by our competing risk approach.
We created a competitive risk assessment model along with a predictive tool designed to estimate CSM in patients with colon cancer. This nomogram demonstrates high accuracy and reliability, aiding medical professionals in making clinical decisions and developing patient follow-up plans.
利用监测、流行病学和最终结果（SEER）数据库，我们开发了一种竞争风险模型以及一个列线图，用于早期识别结肠癌特异性死亡（CSM）风险。
从SEER数据库中获取临床和病理信息以及其他重要数据。患者被随机分为训练集和验证集。我们在竞争风险框架内使用单因素和多因素分析研究了影响结肠癌患者CSM的独立因素，最终开发了一种结肠癌CSM的预测工具。
我们的研究纳入了40261例被诊断为结肠癌的个体，其中10397例直接死于该疾病，另有5828例死于其他原因。我们使用竞争风险模型预测这些患者的癌症特异性死亡率（CSM）。对于训练数据集，该模型预测1年、3年和5年癌症特异性生存率（CSS）的曲线下面积（AUC）分别为0.835（95%置信区间[CI]0.826至0.844）、0.849（95%CI0.843至0.855）和0.843（95%CI0.836至0.850）。在验证组中，相同时间段的AUC值分别为0.846（95%CI0.833至0.860）、0.853（95%CI0.843至0.862）和0.846（95%CI0.835至0.856）。相比之下，传统生存分析随着时间推移产生的累积CSM率高于我们的竞争风险方法。
我们创建了一个竞争风险评估模型以及一个预测工具，用于估计结肠癌患者的CSM。该列线图显示出高准确性和可靠性，有助于医学专业人员做出临床决策并制定患者随访计划。
