Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2026
[Development and validation of a multicenter nomogram predicting the risk of liver metastasis after curative resection of colorectal cancer].
СтатьяМногоцентровое исследованиеВалидационное исследованиеАннотация на английскомНазвание и аннотация: Перевод пока не подготовлен
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Wang Nannan, Yuan Dajin, Zhu Yubing, Ding Lei
Аннотация
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.
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.