目前关于结直肠癌肺转移（CCLM）预后模型的研究还不足。本研究旨在构建和验证CCLM患者整体生存（OS）和癌症特异性生存（CSS）概率的预测模型。从监测、流行病学和结果（SEER）数据库中收集了1,284名CCLM患者的数据。根据计算机生成的随机数以7:3（按存活时间分层）的比例随机分配病人到开发集和验证集。通过最小绝对收缩和选择算子（LASSO）和多变量Cox回归进行预测因素筛选，将合适的预测因素输入Cox比例风险模型来构建预测模型。校准曲线、一致性指数（C-index）、时间依赖性接收者操作特征曲线（ROC曲线）和决策曲线分析（DCA）用于对模型进行验证。根据模型预测的风险评分，将患者分为低风险组和高风险组。Kaplan-Meier（K-M）曲线和log-rank检验用于低风险组和高风险组的生存分析。在LASSO和多变量Cox回归的基础上，有六个变量与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）。在开发集、验证集和扩展测试集中，OS和CSS预测模型的AUC和C-index均大于或接近0.7，表明模型具有良好的预测性能。总体上，这两个模型的校准曲线与对角线吻合。DCA显示，模型的临床效益优于任何单个风险因素。生存分析结果显示，高风险组的预后较低风险组差，这表明该模型对不同预后的患者具有显著辨识能力。经验证后，我们构建的CCLM预测模型是可靠的，可以预测未来1、3和5年内CCLM患者的OS和CSS，为临床预后评估和个体化治疗提供有价值的指导。版权所有©2023 Ma、Yang、Yang、Luo、Zhou和Yang。

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.Copyright © 2023 Ma, Yang, Yang, Luo, Zhou and Yang.