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Journal of Peking University (Health Sciences) ›› 2026, Vol. 58 ›› Issue (2): 290-300. doi: 10.19723/j.issn.1671-167X.2026.02.011

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

Nannan WANG1,2, Dajin YUAN1, Yubing ZHU1, Lei DING1,*()   

  1. 1. Department of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China
    2. Department of Colorectal Surgery, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210009, China
  • Received:2025-11-25 Online:2026-04-18 Published:2026-02-25
  • Contact: Lei DING
  • Supported by:
    the Youth Research Fund of Beijing Shijitan Hospital(2023-q19)

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Abstract:

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.

Key words: Colorectal cancer, Curative resection, Metachronous liver metastasis, Liver metastasis-free survival, Predictive model, Nomogram

CLC Number: 

  • R735.34

Figure 1

CONSORT flow diagram of patient selection and study design"

Table 1

Comparison of baseline clinicopathologic characteristics of CRC patients across multiple centers"

Variables SJTH cohort JSCH cohort P
Training set (n=523) Internal validation set (n=223) External validation set (n=119)
Age/years, n (%) 0.246
   < 65 298 (57.0) 138 (61.9) 77 (64.7)
  ≥65 225 (43.0) 85 (38.1) 42 (35.3)
Gender, n (%) 0.867
  Male 325 (62.1) 136 (61.0) 71 (59.7)
  Female 198 (37.9) 87 (39.0) 48 (40.3)
BMI/(kg/m2), n (%) 0.115
   < 18.5 43 (8.2) 17 (7.6) 15 (12.6)
  18.5 － < 24 251 (48.0) 109 (48.9) 55 (46.2)
  24 － < 28 206 (39.4) 87 (39.0) 44 (37.0)
  ≥28 23 (4.4) 10 (4.5) 5 (4.2)
CEA/(μg/L), n (%) 0.372
   < 5.0 240 (45.9) 95 (42.6) 53 (44.5)
  ≥5.0 283 (54.1) 128 (57.4) 66 (55.5)
CA199/(U/mL), n (%) 0.391
   < 37 245 (46.8) 97 (43.5) 47 (39.5)
  ≥37 278 (53.2) 126 (56.5) 72 (60.5)
Tumor site, n (%) 0.092
  Ascending colon 103 (19.7) 38 (17.0) 31 (26.1)
  Transverse colon 13 (2.5) 13 (5.8) 4 (3.4)
  Descending colon 29 (5.5) 14 (6.3) 12 (10.1)
  Sigmoid colon 155 (29.6) 61 (27.4) 28 (23.5)
  Rectum 223 (42.6) 97 (43.5) 44 (37.0)
Differentiation grade, n (%) 0.238
  High 78 (14.9) 26 (11.7) 21 (17.6)
  Moderate 333 (63.7) 151 (67.7) 66 (55.5)
  Low 112 (21.4) 46 (20.6) 32 (26.9)
Tumor size/cm, n (%) 0.155
   < 5 352 (67.3) 148 (66.3) 82 (68.9)
  ≥5 171 (32.7) 75 (33.6) 37 (31.1)
T stage, n (%) 0.054
  T1 47 (9.0) 18 (8.1) 6 (5.0)
  T2 93 (17.8) 31 (13.9) 15 (12.6)
  T3 348 (66.5) 166 (74.4) 85 (71.4)
  T4 35 (6.7) 8 (3.6) 13 (10.9)
N stage, n (%) 0.875
  N0 321 (61.4) 133 (59.6) 74 (62.2)
  N1 128 (24.5) 58 (26.0) 32 (26.9)
  N2 74 (14.1) 32 (14.3) 13 (10.9)
KRAS, n (%) 0.779
  Mutant type 256 (48.9) 112 (50.2) 55 (46.2)
  Wild type 267 (51.1) 111 (49.8) 64 (53.8)
NRAS, n (%) 0.425
  Mutant type 85 (16.3) 45 (20.2) 20 (16.8)
  Wild type 438 (83.7) 178 (79.8) 99 (83.2)
BRAF, n (%) 0.309
  Mutant type 79 (15.1) 40 (17.9) 14 (11.8)
  Wild type 444 (84.9) 183 (82.1) 105 (88.2)
MSI status, n (%) 0.287
  MSI-H 110 (21.0) 49 (22.0) 18 (15.1)
  MSS 413 (79.0) 174 (78.0) 101 (84.9)
Vascular invasion, n (%) 0.296
  Yes 170 (32.5) 71 (31.8) 30 (25.2)
  No 353 (67.5) 152 (68.2) 89 (74.8)
Nerve invasion, n (%) 0.313
  Yes 174 (33.3) 71 (31.8) 31 (26.1)
  No 349 (66.7) 152 (68.2) 88 (73.9)
Chemotherapy, n (%) 0.197
  Yes 258 (49.3) 120 (53.8) 52 (43.7)
  No 265 (50.7) 103 (46.2) 67 (56.3)
Targeted therapy, n (%) 0.112
  Yes 46 (8.8) 21 (9.4) 4 (3.4)
  No 477 (91.2) 202 (90.6) 115 (96.6)

Table 2

Cox regression for liver metastasis-free survival in patients with CRC"

Variables Univariate analysis Multivariate analysis
P HR (95%CI) P HR (95%CI)
Age/years
   < 65 Reference Reference
  ≥65 0.039 1.55 (1.12－1.95) 0.041 1.52 (1.22－1.74)
Gender
  Female Reference
  Male 0.610 1.12 (0.72－1.73)
BMI/(kg/m2)
   < 18.5 Reference
  18.5 － < 24 0.571 1.00 (0.94－1.05)
  24 － < 28 0.173 1.23 (0.94－1.45)
  ≥28 0.119 1.35 (0.95－1.59)
CEA/(μg/L)
   < 5.0 Reference
  ≥5.0 0.422 1.19 (0.78－1.82)
CA199/(U/mL)
   < 37 Reference
  ≥37 0.237 1.35 (0.82－2.27)
Tumor site
  Ascending colon Reference
  Transverse colon 0.352 0.38 (0.05－2.90)
  Descending colon 0.751 0.84 (0.27－2.54)
  Sigmoid colon 0.420 1.30 (0.69－2.47)
  Rectum 0.652 1.15 (0.63－2.11)
Differentiation grade
  High Reference Reference
  Moderate 0.019 1.85 (1.68－2.11) 0.005 1.19 (1.04－1.28)
  Low 0.001 2.41 (2.24－2.70) 0.005 2.54 (2.30－2.97)
Tumor size/cm
   < 5 Reference
  ≥5 0.119 1.26 (0.95－1.54)
T stage
  T1 Reference Reference
  T2 0.005 2.89 (1.97－3.42) 0.038 1.59 (1.03－2.38)
  T3 0.001 4.75 (3.55－5.97) 0.021 3.55(2.20－4.68)
  T4 < 0.001 8.22 (7.41－10.24) < 0.001 5.21(3.58－6.04)
N stage
  N0 Reference Reference
  N1 0.001 2.17 (1.69－2.8) 0.003 1.62 (1.18－2.02)
  N2 < 0.001 3.96 (2.55－4.80) < 0.001 2.88 (1.96－3.14)
KRAS
  Wild type Reference Reference
  Mutant type 0.012 1.56 (1.35－1.88) 0.005 1.42 (1.27－1.63)
NRAS
  Wild type Reference Reference
  Mutant type 0.105 1.24 (0.89－1.45) 0.275 1.27 (0.93－ 1.43)
BRAF
  Wild type Reference Reference
  Mutant type 0.031 1.43 (1.20－1.93) 0.029 1.53 (1.29－1.84)
MSI status
  MSS Reference Reference
  MSI-H 0.024 0.60 (0.47－0.81) 0.035 0.71 (0.54－0.92)
Vascular invasion
  No Reference Reference
  Yes < 0.001 2.78 (1.82－4.25) 0.017 1.90 (1.67－2.28)
Nerve invasion
  No Reference Reference
  Yes < 0.001 2.80 (1.83－4.28) 0.046 1.12 (1.08－1.38)
Chemotherapy
  No Reference Reference
  Yes < 0.001 4.29 (2.55－7.21) < 0.001 2.65 (1.49－4.72)
Targeted therapy
  No Reference Reference
  Yes < 0.001 9.26 (5.16－15.43) < 0.001 8.35 (4.91－14.20)

Figure 2

Nomogram predicting 1-, 3- and 5-year probabilities for LMFS in patients with CRC"

Figure 3

Comprehensive evaluation of the nomogram for predicting liver metastasis-free survival (LMFS) in patients with colorectal cancer (CRC)"

Table 3

Comparison of representative prediction models for colorectal cancer liver metastasis published from 2015 to 2025"

Study Data source Sample size Prediction endpoint Methodology Variables Model performance Validation strategy
Yan, et al. (2019)[10] SEER database 32 819 LM & LNM Logistic regression Age, CEA, tumor size, grade, N stage AUC: 0.74－0.81 Internal random split
Li, et al. (2020)[12] SEER database 9 958 OS & CSS Cox regression Age, marital status, race, tumor location, pathological grade, histological type, T stage, N stage, colectomy, hepatic surgery, CEA C-index: 0.749 Internal validation
Hao, et al. (2022)[14] Single left 623 Metachronous LM Lasso regression Age, CEA, vascular invasion, T stage, N stage, family history of cancer, KRAS mutation C-index: 0.787 Internal validation
Xiao, et al. (2022)[15] Single left 611 Metachronous LM Deep learning (ResNet-50)+Cox HE image risk score, VELIPI, pT, pN C-index: 0.807 Internal validation split (7 ∶3)
Shao, et al. (2024)[16] SEER database 4 981 OS & CSS Cox & competing risk models Age, race, grade, T stage, N stage, surgery, chemotherapy, CEA, tumor deposits, lung metastasis, tumor size C-index: 0.74－ 0.79 Internal random split (7 ∶3)
Jing, et al. (2025)[17] Single left 212 Postoperative liver metastasis Random forest (fusion model) CT radiomics features + Clinical data AUC: 0.751 Internal random split
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