预测小肝癌患者生存率列线图的建立和验证

李传红; 胡鹏; 欧晏娇; 王宏; 张雷达

doi:10.16016/j.1000-5404.202007005

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预测小肝癌患者生存率列线图的建立和验证

临床医学 | 更新时间：2026-01-09

- 预测小肝癌患者生存率列线图的建立和验证
- Development and validation of nomogram for predicting survival outcomes in patients with small hepatocellular carcinoma
- 第三军医大学学报 2020年42卷第20期页码：2046-2056
- 作者机构：
  
  400038 重庆，陆军军医大学(第三军医大学)第一附属医院肝胆外科
- 作者简介：
  
  [张雷达，E-mail:2518569931@qq.com ZHANG Leida, E-mail: 2518569931@qq.com
- 基金信息：
- DOI：10.16016/j.1000-5404.202007005
  中图分类号： R181.23;R730.7;R735.7
- 收稿：2020-07-01，
  
  修回：2020-08-13，
  
  纸质出版：2020-10-30
- 稿件说明：
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李传红, 胡鹏, 欧晏娇, 等. 预测小肝癌患者生存率列线图的建立和验证[J]. 第三军医大学学报, 2020,42(20):2046-2056.

Chuanhong LI, Peng HU, Yanjiao OU, et al. Development and validation of nomogram for predicting survival outcomes in patients with small hepatocellular carcinoma[J]. Journal of Third Military Medical University, 2020, 42(20): 2046-2056.
李传红, 胡鹏, 欧晏娇, 等. 预测小肝癌患者生存率列线图的建立和验证[J]. 第三军医大学学报, 2020,42(20):2046-2056. DOI： 10.16016/j.1000-5404.202007005.

Chuanhong LI, Peng HU, Yanjiao OU, et al. Development and validation of nomogram for predicting survival outcomes in patients with small hepatocellular carcinoma[J]. Journal of Third Military Medical University, 2020, 42(20): 2046-2056. DOI： 10.16016/j.1000-5404.202007005.

摘要

目的

构建预测小肝癌患者总生存率(overall survival，OS)和癌症特异性生存率(cancer-specific survival，CSS)的列线图。

方法

从SEER数据库中提取1 916例2000-2014年诊断的小肝癌病例，采用随机数表法以1:1比例分为训练组(

=958)和验证组(

=958)。运用单因素Cox回归分析筛选与预后相关的因素，lasso Cox回归分析进一步筛选独立影响因素，并引入AIC信息准则进行评价，基于筛选后的影响因素建立预测小肝癌患者OS和CSS的列线图。在训练组和验证组中，运用C指数、受试者工作特征曲线(ROC曲线)、校准曲线对列线图的预测能力进行验证。

结果

小肝癌患者1、3、5年总生存率分别为81.3%、60.7%、49.2%，1、3、5年癌症特异性生存率分别为85.2%、66.9%、56.9%。与OS相关的6个独立影响因素构建预测OS的列线图；与CSS相关的6个独立影响因素和1个相关因素构建预测CSS的列线图。训练组中，预测OS列线图的C指数为0.727(95%

:0.705~0.750)，1、3、5年生存率ROC曲线下面积分别为0.754、0.783、0.795，预测CSS列线图的C指数为0.753(95%

:0.729~0.777)，1、3、5年生存率ROC曲线下面积分别为0.801、0.806、0.818；在验证组中，预测OS列线图的C指数为0.725(95%

:0.702~0.749)，1、3、5年生存率ROC曲线下面积分别为0.752、0.770、0.799，预测CSS列线图的C指数为0.772(95%

:0.748~0.795)，1、3、5年生存率ROC曲线下面积分别为0.806、0.815、0.837。在两组队列中，1、3、5年OS及CSS的校准曲线显示两个列线图的预测值和实际观察值一致。基于建立的列线图对患者进行了风险分层，能够准确地将小肝癌患者分成预后有显著差异的两个风险亚组。

结论

建立的列线图能较好预测小肝癌患者的OS和CSS，有助于外科医师进行临床决策。

Abstract

Objective

To develop nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of patients with small hepatocellular carcinoma (HCC).

Methods

We retrieved the data of patients with small HCC diagnosed between 2000 and 2014 from the Surveillance

Epidemiology

and End Results (SEER) database

and then randomized the eligible patients into training group (

=958) and validation group (

=958). The prognosis-related risk factors were selected using univariate Cox regression analysis

and the independent prognostic factors of OS and CSS were identified using a lasso Cox regression model. Akaike's information criterion (AIC) was used to evaluate the quality of fit of the models. The nomograms for predicting 1-

3- and 5-year OS and CSS of the patients were constructed based on the identified prognostic factors

and their prediction ability was evaluated using the concordance index (C-index)

receiver operating characteristic (ROC) curve and calibration curve in both the training and validation cohorts.

Results

In the overall cohort

the 1-

and 5-year probabilities of OS rates were 81.3%

60.7% and 49.2%

and the 1-

3- and 5-year CSS rates were 85.2%

66.9% and 56.9%

respectively. Six independent prognostic factors related to OS were selected to develop the OS nomogram

and 6 independent prognostic factors and 1 CSS-related factor were selected to develop the CSS nomogram. In the training group

the C-index for OS nomogram was 0.727 (95%

: 0.705-0.750) and the areas under the ROC curve (AUCs) for 1-

3- and 5-year OS were 0.754

0.783 and 0.795 respectively. For the CSS nomogram

the C-index was 0.753 (95%

: 0.729-0.777) and the AUCs for 1-

3- and 5-year CSS were 0.801

0.806 and

0.818

respectively. In the validation group

the C-index for OS nomogram was 0.725 (95%

: 0.702-0.749) with AUCs for 1-

3- and 5- year OS of 0.752

0.770 and 0.799 respectively; The C-index for CSS nomogram was 0.772 (95%

: 0.748-0.795) with AUCs for 1-

3- and 5-year CSS of 0.806

0.815 and 0.837 respectively. The calibration curves for the probabilities of 1-

and 5-year OS and CSS showed a good agreement between the nomogram-predicted survival and the actual survival outcomes in both the training and validation cohorts. We calculated the risk score for every patient according to the nomograms and accurately stratified the patients into high- and low-risk groups based on the median risk score.

Conclusion

The nomogram we established can accurately predict the OS and CSS of the patients with small HCC to assist the surgeons for clinical decision-making.

关键词

Keywords

references

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