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北京大学学报（医学版） ›› 2026, Vol. 58 ›› Issue (2): 290-300. doi: 10.19723/j.issn.1671-167X.2026.02.011

• 论著 • 上一篇    下一篇

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

王楠楠1,2,*, 袁大晋1,*, 朱昱冰1, 丁磊1,*()   

  1. 1. 首都医科大学附属北京世纪坛医院胃肠外科，北京 100038
    2. 江苏省肿瘤医院(南京医科大学附属肿瘤医院，江苏省癌症中心)结直肠外科，南京 210009
  • 收稿日期:2025-11-25 出版日期:2026-04-18 发布日期:2026-02-25
  • 通讯作者: 丁磊
  • 作者简介:

    * These authors contributed equally to this work

  • 基金资助:
    北京世纪坛医院院青年基金(2023-q19)

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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摘要：

目的: 分析Ⅰ~Ⅲ期结直肠癌(colorectal cancer, CRC)根治性切除术后患者发生异时性肝转移的危险因素, 并构建用于预测患者术后1年、3年及5年无肝转移生存期(liver metastasis-free survival, LMFS)的列线图模型。方法: 采用多中心回顾性队列研究设计, 连续收集2020年1月至2024年12月于首都医科大学附属北京世纪坛医院行根治性切除术的746例CRC患者的病例资料, 按7 ∶3比例随机划分为训练集(523例)与内部验证集(223例); 连续选择同期江苏省肿瘤医院的119例患者作为独立外部验证集。纳入指标包括患者的临床病理特征及微卫星不稳定性(microsatellite instability, MSI)、KRAS/BRAF基因状态等分子标志物。采用单因素及多因素Cox比例风险回归分析筛选独立预测因子, 并据此构建LMFS列线图模型。通过一致性(concordance, C)指数、时间依赖性受试者工作特征(receiver operating characteristic, ROC)曲线的曲线下面积(area under the curve, AUC)、校准曲线及决策曲线分析(decision curve analysis, DCA)综合评估模型的区分度、校准度与临床实用性。结果: 研究的多中心队列基线资料均衡(P>0.05)。多因素分析显示, 高龄(≥65岁)、低分化、T分期进展、N分期进展、脉管侵犯、神经侵犯及分子标志物状态均为异时性肝转移的独立预后因素, 其中, KRAS突变(HR=1.42, 95%CI: 1.27~1.63)与BRAF突变(HR=1.53, 95%CI: 1.29~1.84)为异时性肝转移的独立危险因素, 而微卫星高度不稳定(microsatellite instability-high, MSI-H)状态(HR=0.71, 95%CI: 0.54~0.92)为异时性肝转移的独立保护因素。列线图模型在训练集、内部验证集和外部验证集中的C指数分别为0.85(95%CI: 0.82~0.89)、0.81(95%CI: 0.77~0.83)和0.75(95%CI: 0.71~0.79);训练集预测1年、3年、5年LMFS的AUC分别为0.81(95%CI: 0.77~0.86)、0.83(95%CI: 0.80~0.89)和0.85(95%CI: 0.78~0.92)。校准曲线显示预测值与实测值高度一致; DCA表明该预测模型相较于AJCC(American Joint Committee on Cancer)的TNM分期系统具有更高的临床净收益; 各队列中高风险组与低风险组的LMFS差异均具有统计学意义(P < 0.001)。结论: 本研究整合临床病理特征与KRAS、BRAF、MSI分子标志物构建的列线图模型, 在预测Ⅰ~Ⅲ期CRC患者术后异时性肝转移风险方面具有良好的区分度、校准度及临床实用性, 且优于传统TNM分期系统, 有助于指导术后个性化随访监测与治疗决策。

关键词: 结直肠肿瘤, 根治性切除, 异时性肝转移, 无肝转移生存期, 预测模型, 列线图

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

中图分类号: 

  • R735.34

图1

患者筛选与研究设计的CONSORT流程图 CRC, colorectal cancer; LMFS, liver metastasis-free survival; ROC, receiver operating characteristic; DCA, decision curve analysis."

表1

多中心CRC患者的基线临床病理特征比较"

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)

表2

CRC患者术后无肝转移生存期的Cox回归分析"

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)

图2

预测CRC患者1年、3年和5年LMFS的列线图模型 Abbreviations as in Table 1."

图3

预测CRC患者LMFS列线图模型的综合评估 A，B，C，ROC analysis of the nomogram for the LMFS in the training, internal validation, and external validation cohorts；D，E，F，calibration curves of the nomogram for predicting 1-, 3-, and 5-year LMFS in the training, internal validation, and external validation cohorts；G，decision curves of the nomogram for predicting 1-, 3-, and 5-year LMFS；H，I，J，Kaplan-Meier curves of LMFS for patients in the low-and high-risk groups in the training set, internal validation set, and external validation set, respectively. AJCC, American Joint Committee on Cancer."

表3

2015—2025年发表的代表性结直肠癌肝转移预测模型特征对比"

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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ISSN 1671-167X
CN 11-4691/R
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