We aimed to develop a clinical predictive model for predicting the overall survival (OS) in stage I–III CRC patients after radical resection with normal preoperative CEA. This study included 1082 consecutive patients. They were further divided into a training set (70%) and a validation set (30%). The selection of variables for the model was informed by the Akaike information criterion. After that, the clinical predictive model was constructed, evaluated, and validated. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were employed to compare the models. Age, histologic type, pT stage, pN stage, carbohydrate antigen 242 (CA242), and carbohydrate antigen 125 (CA125) were selected to establish a clinical prediction model for OS. The concordance index (C-index) (0.748 for the training set and 0.702 for the validation set) indicated that the nomogram had good discrimination ability. The decision curve analysis highlighted that the model has superior efficiency in clinical decision-making. NRI and IDI showed that the established nomogram markedly outperformed the TNM stage. The new clinical prediction model was notably superior to the AJCC 8th TNM stage, and it can be used to accurately assess the OS of stage I–III CRC patients undergoing radical resection with normal preoperative CEA.
本研究旨在构建一个临床预测模型，用于预测术前癌胚抗原（CEA）正常的I–III期结直肠癌（CRC）患者根治性切除术后的总生存期（OS）。研究共纳入1082例连续患者，并将其进一步分为训练集（70%）和验证集（30%）。模型变量的筛选基于赤池信息准则（AIC）进行。随后，构建、评估并验证了该临床预测模型。采用净重分类指数（NRI）和综合判别改善指数（IDI）对不同模型进行比较。 最终选取年龄、组织学类型、pT分期、pN分期、糖类抗原242（CA242）及糖类抗原125（CA125）构建OS的临床预测模型。一致性指数（C-index）显示（训练集为0.748，验证集为0.702），列线图具有良好的区分能力。决策曲线分析表明该模型在临床决策中具有优越的效能。NRI和IDI结果显示，所构建的列线图显著优于TNM分期系统。 这一新的临床预测模型明显优于美国癌症联合委员会（AJCC）第八版TNM分期，可用于准确评估术前CEA正常的I–III期CRC患者接受根治性切除术后的总生存期。
肿瘤(癌症)患者之家