Hu Zhe-Xing, Li Yin, Yang Xuan, Li Yu-Xia, He Yao-Yao, Niu Xiao-Hui, Nie Ting-Ting, Guo Xiao-Fang, Yuan Zi-Long
Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei Province, China.
Department of Radiology, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, Hubei Province, China.
World J Gastrointest Oncol. 2024 Oct 15;16(10):4104-4114. doi: 10.4251/wjgo.v16.i10.4104.
The colon cancer prognosis is influenced by multiple factors, including clinical, pathological, and non-biological factors. However, only a few studies have focused on computed tomography (CT) imaging features. Therefore, this study aims to predict the prognosis of patients with colon cancer by combining CT imaging features with clinical and pathological characteristics, and establishes a nomogram to provide critical guidance for the individualized treatment.
To establish and validate a nomogram to predict the overall survival (OS) of patients with colon cancer.
A retrospective analysis was conducted on the survival data of 249 patients with colon cancer confirmed by surgical pathology between January 2017 and December 2021. The patients were randomly divided into training and testing groups at a 1:1 ratio. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors associated with OS, and a nomogram model was constructed for the training group. Survival curves were calculated using the Kaplan-Meier method. The concordance index (C-index) and calibration curve were used to evaluate the nomogram model in the training and testing groups.
Multivariate logistic regression analysis revealed that lymph node metastasis on CT, perineural invasion, and tumor classification were independent prognostic factors. A nomogram incorporating these variables was constructed, and the C-index of the training and testing groups was 0.804 and 0.692, respectively. The calibration curves demonstrated good consistency between the actual values and predicted probabilities of OS.
A nomogram combining CT imaging characteristics and clinicopathological factors exhibited good discrimination and reliability. It can aid clinicians in risk stratification and postoperative monitoring and provide important guidance for the individualized treatment of patients with colon cancer.
结肠癌的预后受多种因素影响，包括临床、病理和非生物学因素。然而，仅有少数研究关注计算机断层扫描（CT）成像特征。因此，本研究旨在通过结合CT成像特征与临床和病理特征来预测结肠癌患者的预后，并建立列线图为个体化治疗提供关键指导。
建立并验证用于预测结肠癌患者总生存期（OS）的列线图。
对2017年1月至2021年12月间经手术病理确诊的249例结肠癌患者的生存数据进行回顾性分析。患者按1:1比例随机分为训练组和测试组。进行单因素和多因素逻辑回归分析以确定与OS相关的独立危险因素，并为训练组构建列线图模型。采用Kaplan-Meier法计算生存曲线。使用一致性指数（C指数）和校准曲线评估训练组和测试组中的列线图模型。
多因素逻辑回归分析显示，CT上的淋巴结转移、神经周围侵犯和肿瘤分级是独立的预后因素。构建了包含这些变量 的列线图，训练组和测试组的C指数分别为0.804和0.692。校准曲线显示实际值与OS预测概率之间具有良好的一致性。
结合CT成像特征和临床病理因素的列线图具有良好的区分度和可靠性。它可帮助临床医生进行风险分层和术后监测，并为结肠癌患者的个体化治疗提供重要指导。
