School of Medicine, Huaqiao University, Quanzhou, China.
Front Endocrinol (Lausanne). 2023 Jul 31;14:1073360. doi: 10.3389/fendo.2023.1073360. eCollection 2023.
Current studies on the establishment of prognostic models for colon cancer with lung metastasis (CCLM) were lacking. This study aimed to construct and validate prediction models of overall survival (OS) and cancer-specific survival (CSS) probability in CCLM patients.
Data on 1,284 patients with CCLM were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly assigned with 7:3 (stratified by survival time) to a development set and a validation set on the basis of computer-calculated random numbers. After screening the predictors by the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression, the suitable predictors were entered into Cox proportional hazard models to build prediction models. Calibration curves, concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were used to perform the validation of models. Based on model-predicted risk scores, patients were divided into low-risk and high-risk groups. The Kaplan-Meier (K-M) plots and log-rank test were applied to perform survival analysis between the two groups.
Building upon the LASSO and multivariate Cox regression, six variables were significantly associated with OS and CSS (i.e., tumor grade, AJCC T stage, AJCC N stage, chemotherapy, CEA, liver metastasis). In development, validation, and expanded testing sets, AUCs and C-indexes of the OS and CSS prediction models were all greater than or near 0.7, which indicated excellent predictability of models. On the whole, the calibration curves coincided with the diagonal in two models. DCA indicated that the models had higher clinical benefit than any single risk factor. Survival analysis results showed that the prognosis was worse in the high-risk group than in the low-risk group, which suggested that the models had significant discrimination for patients with different prognoses.
After verification, our prediction models of CCLM are reliable and can predict the OS and CSS of CCLM patients in the next 1, 3, and 5 years, providing valuable guidance for clinical prognosis estimation and individualized administration of patients with CCLM.
目前缺乏关于建立结肠癌伴肺转移（CCLM）预后模型的研究。本研究旨在构建和验证 CCLM 患者总生存（OS）和癌症特异性生存（CSS）概率的预测模型。
从监测、流行病学和最终结果（SEER）数据库中收集了 1284 例 CCLM 患者的数据。根据计算机计算的随机数，患者被 7:3（按生存时间分层）随机分配到开发集和验证集中。在通过最小绝对收缩和选择算子（LASSO）和多变量 Cox 回归筛选预测因子后，将合适的预测因子输入 Cox 比例风险模型中构建预测模型。校准曲线、一致性指数（C 指数）、时间依赖性接收者操作特征（ROC）曲线和决策曲线分析（DCA）用于模型验证。基于模型预测的风险评分，患者被分为低风险组和高风险组。采用 Kaplan-Meier（K-M）图和对数秩检验比较两组之间的生存分析。
基于 LASSO 和多变量 Cox 回归，有 6 个变量与 OS 和 CSS 显著相关（即肿瘤分级、AJCC T 分期、AJCC N 分期、化疗、CEA、肝转移）。在开发、验证和扩展测试集中，OS 和 CSS 预测模型的 AUC 和 C 指数均大于或接近 0.7，表明模型具有良好的预测能力。总的来说，两个模型的校准曲线与对角线重合。DCA 表明模型比任何单一风险因素都具有更高的临床获益。生存分析结果表明，高风险组的预后比低风险组差，这表明模型对不同预后的患者具有显著的区分能力。
经过验证，我们的 CCLM 预测模型是可靠的，可以预测 CCLM 患者未来 1、3 和 5 年的 OS 和 CSS，为临床预后评估和 CCLM 患者的个体化治疗提供有价值的指导。
