结直肠癌根治术后肝转移风险多中心列线图预测模型的构建与验证OA

Objective:To identify independent clinicopathological and molecular risk factors for meta-chronous liver metastasis and to construct a novel multicenter nomogram for predicting 1-,3-,and 5-year liver metastasis-free survival(LMFS).Methods:In this multicenter retrospective cohort study,we ana-lyzed clinical data from 865 patients with stages Ⅰ-Ⅲ CRC who underwent curative resection between January 2020 and December 2024.The population was derived from two institutions:Beijing Shijitan Hospital(n=746)and Jiangsu Cancer Hospital(n=119).Patients from the primary center were ran-domly assigned to a training cohort(n=523)and an internal validation cohort(n=223)at a 7∶3 ratio,while patients from the second center served as an independent external validation cohort(n=119).Candidate variables included demographics,tumor markers,pathological features,and molecular biomarkers[KRAS/BRAF mutation and microsatellite instability(MSI)].Multivariable Cox proportional hazards regression analyses were utilized to identify independent predictors.Model performance was eval-uated using the concordance index(C-index),time-dependent area under the receiver operating charac-teristic curve(AUC),calibration curves,and decision curve analysis(DCA).Results:Baseline char-acteristics were balanced across cohorts(P＞0.05).Multivariable analysis identified nine independent prognostic factors:age,differentiation,T stage,N stage,vascular invasion,perineural invasion,and molecular markers.Notably,KRAS mutation(HR=1.42,95％CI:1.27-1.63)and BRAF mutation(HR=1.53,95％CI:1.29-1.84)were associated with significantly increased risk,whereas micro-satellite instability-high(MSI-H)status(HR=0.71,95％CI:0.54-0.92)served as a protective fac-tor.The nomogram demonstrated robust discrimination with C-indices of0.85(95％CI:0.82-0.89)in the training cohort,0.81(95％CI:0.77-0.83)in the internal validation cohort,and 0.75(95％CI:0.71-0.79)in the external validation cohort.In the training set,AUCs for predicting 1-,3-,and 5-year LMFS were 0.81(95％CI:0.77-0.86),0.83(95％CI:0.80-0.89),and 0.85(95％CI:0.78-0.92),respectively.Calibration curves showed excellent agreement,and DCA indicated higher net clinical benefit than the American Joint Committee on Cancer(AJCC)tumor node metastasis(TNM)staging system.Conclusion:We established and externally validated a nomogram integrating clinicopathologi-cal features with KRAS,BRAF,and MSI status.This model exhibited enhanced predictive accuracy and gener-alizability compared with conventional staging systems.It serves as a valuable tool for identifying high-risk pa-tients and guiding individualized postoperative surveillance strategies to improve long-term survival outcomes.