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中华结直肠疾病电子杂志 ›› 2023, Vol. 12 ›› Issue (04) : 311 -318. doi: 10.3877/cma.j.issn.2095-3224.2023.04.007

论著

构建预测结直肠癌肝转移术后患者生存的列线图模型
李兆, 张颖, 宋彦呈, 李兆鹏, 刘曙光, 郭栋, 陈栋, 李宇()   
  1. 266003 青岛大学附属医院胃肠外科
    264200 威海市立第三医院普外科
  • 收稿日期:2022-12-05 出版日期:2023-08-25
  • 通信作者: 李宇
  • 基金资助:
    山东省医药卫生科技发展计划项目(202204010913)

Construct a nomogram to predict the prognosis of patients with colorectal liver metastasis after operation

Zhao Li, Ying Zhang, Yancheng Song, Zhaopeng Li, Shuguang Liu, Dong Guo, Dong Chen, Yu Li()   

  1. Department of Gastrointestinal Surgery, Affiliated Hospital of Qingdao University, Qingdao 266003, China
    Department of General Surgery, Weihai Municipal Third Hospital, Weihai 264200, China
  • Received:2022-12-05 Published:2023-08-25
  • Corresponding author: Yu Li
引用本文:

李兆, 张颖, 宋彦呈, 李兆鹏, 刘曙光, 郭栋, 陈栋, 李宇. 构建预测结直肠癌肝转移术后患者生存的列线图模型[J]. 中华结直肠疾病电子杂志, 2023, 12(04): 311-318.

Zhao Li, Ying Zhang, Yancheng Song, Zhaopeng Li, Shuguang Liu, Dong Guo, Dong Chen, Yu Li. Construct a nomogram to predict the prognosis of patients with colorectal liver metastasis after operation[J]. Chinese Journal of Colorectal Diseases(Electronic Edition), 2023, 12(04): 311-318.

目的

构建预测结直肠癌肝转移(CRLM)术后患者1、3及5年总生存率(OS)的列线图模型,并评价模型的预测能力及临床获益。

方法

回顾性分析青岛大学附属医院2008年1月至2022年1月期间经手术治疗的113例结直肠癌肝转移患者的临床病理资料。应用R软件,采用随机抽样法,选择91例(4/5)患者作为训练队列,剩余22例(1/5)患者作为验证队列。在训练队列中,采用多元COX逐步回归法构建生存预测模型,基于预测模型中的危险因素构建预测CRLM术后患者1、3及5年OS的列线图。在训练队列和验证队列中分别通过一致性指数(C-index)、受试者工作特征(ROC)曲线和校准曲线来评价该模型的预测效能,并通过临床获益曲线来评估该模型的临床获益及应用价值。

结果

基于训练队列的多元COX逐步回归分析结果显示:BMI、肝转移灶切除时机、术前CEA水平、术后是否接受化疗及CRS评分是预测CRLM患者术后预后的最佳危险因素。将以上因素纳入列线图模型并进行评价。训练队列和验证队列的一致性指数分别为0.668(95%CI:0.557~0.779)和0.708(95%CI:0.596~0.818)。训练队列和验证队列1、3及5年OS的ROC曲线下面积(AUC)均达到了0.7以上,校准曲线显示了该列线图模型具有较好的拟合度,临床获益曲线显示模型有较高的临床获益。

结论

所构建的列线图模型能较为准确地预测结直肠癌肝转移术后患者1、3及5年的总生存率,为临床肿瘤医师评估CRLM手术患者的生存预后和优化诊疗方案提供参考。

Objective

To construct a nomogram to predict the 1-,3- and 5-year overall survival (OS) of colorectal cancer liver metastases(CRLM) patients, and to validate the prediction efficiency and clinical benefit of the model.

Methods

The clinical data of 113 colorectal liver metastasis patients who underwent resection of liver metastases in the Affiliated Hospital of Qingdao University from January 2008 to January 2022 were retrospectively analyzed. Using R software to implement random sampling, ninty-one patients (4/5) were selected as the training group, and the remaining 22 patients (1/5) were selected as the validation group. In the training group, use the multivariate Cox stepwise regression analysis to select 10 risk factors in the best survival prediction model. A nomogram for predicting 1-,3-and 5-year OS was established based on these risk factors. The nomogram's prediction efficiency was evaluated by concordance index (C-index), receiver operating characteristic curve (ROC) and calibration curve. The clinical benefit and application value of the model were evaluated by clinical decision curve analysis.

Results

The results of multiple COX stepwise regression analysis based on training group showed that: age, BMI, the primary tumor histological grade, preoperative plasma CEA level, preoperative neutrophils-lymphocyte ratio (NLR), surgical margin, measured blood loss, postoperative targeted therapy, CRS score and postoperative recurrence and metastasis were the best risk factors for predicting the prognosis of CRLM patients. A nomogram was established and validated based on the risk factors. The C-index of OS in training and validation groups were 0.742(95%CI: 0.652~0.832) and 0.653(95%CI: 0.423~0.883). The average area under the curve (AUC) of ROC curves of 1-,3- and 5-year OS in the training and validation groups were all more than 0.7. Calibration curves also showed an excellent agreement between actual survival and nomogram predictive survival. The clinical decision curve analysis showed that model can bring higher clinical benefit.

Conclusions

The established nomogram can accurately predict the 1-,3-and 5-year OS of CRLM patients after operation. It may be helpful for clinical oncologists to evaluate the prognosis of CRLM patients and optimize diagnosis and treatment plan.

表1 训练集与验证集结直肠癌肝转移手术患者基线资料比较[例(%)]
临床病理特征 总列队(n=113) 训练列队(n=91) 验证列队(n=22) χ2 P
诊断时年龄(岁) 0.287 0.592
<60 61(54.0) 48(52.7) 13(59.1)
≥60 52(46.0) 43(47.3) 9(40.9)
性别 0.037 0.847
男性 84(74.3) 68(74.7) 16(72.7)
女性 29(25.7) 23(25.3) 6(27.3)
BMI(kg/m2 1.654 0.198
<23 43(38.1) 32(35.2) 11(50.0)
≥23 70(61.9) 59(64.8) 11(50.0)
原发癌部位 2.860 0.239
直肠 59(52.2) 51(56.0) 8(36.4)
左半结肠 25(22.1) 19(20.9) 6(27.3)
右半结肠 29(25.7) 21(23.1) 8(36.4)
原发癌浸润深度 0.389 0.533
T1~2 30(26.5) 23(25.3) 7(31.8)
T3~4 83(73.5) 68(74.7) 15(68.2)
原发癌区域淋巴结转移 2.722 0.099
N0 43(38.1) 38(41.8) 5(22.7)
N+ 70(61.9) 53(58.2) 17(77.3)
原发癌组织学分级 0.758 0.384
高分化 17(15.0) 15(16.5) 2(9.1)
中低分化 96(85.0) 76(83.5) 20(90.9)
肝转移时期 0.008 0.931
同时性 71(62.8) 57(62.6) 14(63.6)
异时性 42(37.2) 34(37.4) 8(36.4)
转移癌个数(个) 3.817 0.148
1 64(56.6) 51(56.0) 13(59.1)
2~5 39(34.5) 34(37.4) 5(22.7)
>5 10(8.8) 6(6.6) 4(18.2)
转移癌最大直径(cm) 0.934 0.331
<5 85(75.2) 65(71.4) 20(90.9)
≥5 28(24.7) 26(28.6) 2(9.1)
手术切缘(cm) 3.541 0.060
<1 40(35.4) 36(39.6) 4(18.2)
≥1 73(64.6) 55(60.4) 18(81.8)
术中出血量(mL) 0.013 0.910
<400 102(90.3) 82(90.1) 20(90.9)
≥400 11(9.7) 9(9.9) 2(9.1)
术前CEA水平(μg/mL) 1.049 0.306
<200 77(68.1) 60(65.9) 17(77.3)
≥200 36(31.9) 31(34.1) 5(22.7)
术前NLR 0.371 0.542
<2.8 78(69.0) 64(70.3) 14(63.6)
≥2.8 35(31.0) 27(29.7) 8(36.4)
术前新辅助治疗 0.494 0.482
75(66.4) 59(64.8) 16(72.7)
38(33.6) 32(35.2) 6(27.3)
转移癌术后化疗 0.683 0.409
19(16.8) 14(15.4) 5(22.7)
94(83.2) 77(84.6) 17(77.3)
肝转移灶切除时机 0.088 0.767
同期切除 74(65.5) 59(64.8) 15(68.2)
延期切除 39(34.5) 32(35.2) 7(31.8)
CRS评分(分) 3.268 0.071
1~2 71(62.8) 53(58.2) 18(81.8)
3~5 42(37.2) 38(41.8) 4(18.2)
图1 训练队列和验证队列总生存曲线
表2 基于训练队列的多元COX逐步回归分析
图2 列线图预测CRLM术后患者的1、3及5年OS预测模型
图3 训练队列和验证队列结直肠癌肝转移患者术后1、3及5年OS的ROC曲线。3A:为训练队列患者1、3及5年OS的ROC曲线;3B:为验证队列患者1、3及5年OS的ROC曲线;注:AUC:ROC曲线下面积,OS:总生存
图4 训练队列和验证队列结直肠癌肝转移患者术后1、3及5年OS的校准曲线。4A、4B、4C分别为训练队列患者1、3及5年OS的校准曲线,4D、4E、4F分别为验证队列患者1、3及5年OS的校准曲线
图5 训练队列和验证队列结直肠癌肝转移患者术后1、3及5年OS的临床获益曲线。5A、5B、5C分别为训练队列患者1、3及5年OS的临床获益曲线,5D、5E、5F分别为验证队列患者1、3及5年OS的临床获益曲线
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