[Development and validation of a multicenter nomogram predicting the risk of liver metastasis after curative resection of colorectal cancer].

OBJECTIVE

To identify independent clinicopathological and molecular risk factors for metachronous 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 analyzed 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 randomly 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 evaluated using the concordance index (C-index), time-dependent area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS

Baseline characteristics 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 factor. The nomogram demonstrated robust discrimination with C-indices of 0.85 (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 clinicopathological features with KRAS, BRAF, and MSI status. This model exhibited enhanced predictive accuracy and generalizability compared with conventional staging systems. It serves as a valuable tool for identifying high-risk patients and guiding individualized postoperative surveillance strategies to improve long-term survival outcomes.