目前结直肠癌（CRC）的预测模型仅包括肿瘤标志物的术前测量，它们可用的术后重复测量未得到充分利用。本研究构建了CRC预测模型，以澄清是否以及在多大程度上包括CEA、CA19-9和CA125术中长期测量是否能提高模型性能，并进行动态预测。培训和验证队列包括1453名和444名行手术治疗的CRC患者，分别进行了术前测量和手术后12个月内两次或更多次测量。使用人口学和临床病理变量构建预测CRC总体生存的预测模型，通过包括术前CEA、CA19-9和CA125以及其术中长期测量来进行。在内部验证中，仅包括术前CEA、CA19-9和CA125的模型表现比仅包括CEA的模型更好，其受试者工作特征曲线下面积（AUC：0.774 vs 0.716）、碧尔得分（BS：0.057 vs 0.058）和净重分类改进（NRI = 33.5％，95％CI：12.3~54.8％）在手术后36个月时均更优。此外，通过包括手术后12个月内CEA、CA19-9和CA125的长期测量来构建的预测模型具有更高的预测精度，具有更高的AUC（0.849）和更低的BS（0.049）。与术前模型相比，包括三种标志物长期测量的模型在手术后36个月的NRI显著（40.8％，95％CI：19.6至62.1％）。外部验证显示了与内部验证类似的结果。建议在CRC预后监测中重复测量CEA、CA19-9和CA125。所提出的长期预测模型可以为新患者提供个性化的动态预测，在手术后12个月内收集新的测量时更新估计的生存概率。包括CEA、CA19-9和CA125的长期测量的预测模型提高了预测CRC患者预后的准确性。

Current prognostic prediction models of colorectal cancer (CRC) include only the preoperative measurement of tumor markers, with their available repeated postoperative measurements underutilized. CRC prognostic prediction models were constructed in this study to clarify whether and to what extent the inclusion of perioperative longitudinal measurements of CEA, CA19-9, and CA125 can improve the model performance, and perform a dynamic prediction.The training and validating cohort included 1453 and 444 CRC patients who underwent curative resection, with preoperative measurement and two or more measurements within 12 months after surgery, respectively. Prediction models to predict CRC overall survival were constructed with demographic and clinicopathological variables, by incorporating preoperative CEA, CA19-9, and CA125, as well as their perioperative longitudinal measurements.In internal validation, the model with preoperative CEA, CA19-9, and CA125 outperformed the model including CEA only, with the better area under the receiver operating characteristic curves (AUCs: 0.774 vs 0.716), brier scores (BSs: 0.057 vs 0.058), and net reclassification improvement (NRI = 33.5%, 95% CI: 12.3 ~ 54.8%) at 36 months after surgery. Furthermore, the prediction models, by incorporating longitudinal measurements of CEA, CA19-9, and CA125 within 12 months after surgery, had improved prediction accuracy, with higher AUC (0.849) and lower BS (0.049). Compared with preoperative models, the model incorporating longitudinal measurements of the three markers had significant NRI (40.8%, 95% CI: 19.6 to 62.1%) at 36 months after surgery. External validation showed similar results to internal validation. The proposed longitudinal prediction model can provide a personalized dynamic prediction for a new patient, with estimated survival probability updated when a new measurement is collected during 12 months after surgery.Prediction models including longitudinal measurements of CEA, CA19-9, and CA125 have improved accuracy in predicting the prognosis of CRC patients. We recommend repeated measurements of CEA, CA19-9, and CA125 in the surveillance of CRC prognosis.© 2023. The Author(s).