Development and validation of nomogram for predicting survival outcomes in patients with small hepatocellular carcinoma

Chuanhong LI; Peng HU; Yanjiao OU; Hong WANG; Leida ZHANG

doi:10.16016/j.1000-5404.202007005

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Development and validation of nomogram for predicting survival outcomes in patients with small hepatocellular carcinoma

Clinical Medicine | 更新时间：2026-01-09

- Development and validation of nomogram for predicting survival outcomes in patients with small hepatocellular carcinoma
- Journal of Third Military Medical University Vol. 42, Issue 20, Pages: 2046-2056(2020)
- 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：
- 基金信息：
- DOI：10.16016/j.1000-5404.202007005
  CLC： R181.23;R730.7;R735.7
- Received：01 July 2020，
  
  Revised：2020-08-13，
  
  Published：30 October 2020
- 稿件说明：
移动端阅览
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：

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

VILLANUEVA A. Hepatocellular Carcinoma[J]. N Engl J Med, 2019, 380 (15): 1450-1462. DOI: 10.1056/NEJMra1713263.

SIEGEL R L, MILLER K D, JEMAL A. Cancer statistics, 2020[J]. CA Cancer J Clin, 2020, 70 (1): 7-30. DOI: 10.3322/caac.21590.

JEMAL A, WARD E M, JOHNSON C J, et al. Annual report to the nation on the status of cancer, 1975-2014, featuring survival[J]. J Natl Cancer Inst, 2017, 109(9): 1-22. DOI: 10.1093/jnci/djx030.

ZHANG W J, WANG X C, JIANG R Q, et al. Effect of tumor size on cancer-specific survival in small hepatocellularcarcinoma[J]. Mayo Clin Proc, 2015, 90(9): 1187-1195. DOI: 10.1016/j.mayocp.2015.06.018.

WEI YY, DAI F, YI Y X, et al. Impact of local tumor lesion treatments and preoperative indicators on the survival of patients with small hepatocellular carcinomas[J]. Oncol Lett, 2018, 16(4): 5050-5058. DOI: 10.3892/ol.2018.9263.

EL-SERAG H B, MARRERO J A, RUDOLPH L, et al. Diagnosis and treatment of hepatocellular carcinoma[J]. Gastroenterology, 2008, 134(6): 1752-1763. DOI: 10.1053/j.gastro.2008.02.090.

ZHOU X D, TANG Z Y, YANG B H, et al. Experience of 1000 patients who underwent hepatectomy for small hepatocellular carcinoma[J]. Cancer, 2001, 91(8): 1479-1486. DOI: 10.1002/1097-0142(20010415)91: 81479: :aid-cncr1155>3.0.co; 2-0.

HONG S K, LEE K W, YOON K C, et al. Different prognostic factors and strategies for early and late recurrence after adult living donor liver transplantation for hepatocellular carcinoma[J].Clin Transplant, 2019, 33(10): e13703. DOI: 10.1111/ctr.13703.

ZHENG J, CHOU J F, GÖNEN M, et al. Prediction of hepato- cellular carcinoma recurrence beyond Milan criteria after resection: validation of a clinical risk score in an internationalcohort[J]. Ann Surg, 2017, 266(4): 693-701. DOI: 10.1097/SLA.0000000000002360.

ZHU G Q, SUN M, LIAO W T, et al. Comparative efficacy and safety between ablative therapies or surgery for small hepatocellular carcinoma: a network meta-analysis[J]. Expert Rev Gastroenterol Hepatol, 2018, 12(9): 935-945. DOI: 10.1080/17474124.2018.1503531.

KIM J M, KWON C H D, JOH J W, et al.Nomograms in hepatectomy patients with hepatitis B virus-related hepatocellular carcinoma[J]. J Gastrointest Surg, 2019, 23(8): 1559-1567. DOI: 10.1007/s11605-018-04074-z.

KAIBORI M, ISHIZAKI M, SAITO T, et al. Risk factors and outcome of early recurrence after resection of small hepatocellular carcinomas[J]. Am J Surg, 2009, 198(1): 39-45. DOI: 10.1016/j.amjsurg.2008.07.051.

BALACHANDRAN V P, GONEN M, SMITH J J, et al. Nomograms in oncology: more than meets the eye[J]. Lancet Oncol, 2015, 16(4): e173-e180. DOI: 10.1016/S1470-2045(14)71116-7.

LEE H W, AHN S H. Prediction models of hepatocellular carcinoma development in chronic hepatitis B patients[J]. World J Gastroenterol, 2016, 22(37): 8314-8321. DOI: 10.3748/wjg.v22.i37.8314.

IASONOS A, SCHRAG D, RAJ G V, et al. How to build and interpret anomogram for cancer prognosis[J]. J Clin Oncol, 2008, 26(8): 1364-1370. DOI: 10.1200/JCO.2007.12.9791.

ZHANG S L, WANG X, LI Z M, et al. Score for the overall survival probability of patients with first-diagnosed distantly metastatic cervical cancer: a novel nomogram-based risk assessment system[J]. Front Oncol, 2019, 9: 1106. DOI: 10.3389/fonc.2019.01106.

BRUIX J, REIG M, SHERMAN M. Evidence-based diagnosis, staging, and treatment of patients with hepatocellularcarcinoma[J]. Gastroenterology, 2016, 150(4): 835-853. DOI: 10.1053/j.gastro.2015.12.041.

KUTLU O C, CHAN J A, ALOIA T A, et al. Comparative effectiveness of first-line radiofrequency ablation versus surgical resection and transplantation for patients with early hepatocellular carcinoma[J]. Cancer, 2017, 123(10): 1817-1827. DOI: 10.1002/cncr.30531.

ANWANWAN D, SINGH S K, SINGH S, et al. Challenges in liver cancer and possible treatment approaches[J]. Biochim Biophys Acta Rev Cancer, 2020, 1873(1): 188314. DOI: 10.1016/j.bbcan.2019.188314.

MAO K, YAN Y C, ZHANG J L, et al. The impact of liver resection on survival outcomes of hepatocellular carcinoma patients with extrahepatic metastases: a propensity score matching study[J]. Cancer Med, 2018, 7(9): 4475-4484. DOI: 10.1002/cam4.1738.

NAULT J C, SUTTER O, NAHON P, et al. Percutaneous treatment of hepatocellular carcinoma: State of the art and innovations[J]. J Hepatol, 2018, 68(4): 783-797. DOI: 10.1016/j.jhep.2017.10.004.

NG K K C, CHOK K S H, CHAN A C Y, et al. Randomized clinical trial of hepatic resection versus radiofrequency ablation for early-stage hepatocellular carcinoma[J]. Br J Surg, 2017, 104(13): 1775-1784. DOI: 10.1002/bjs.10677.

DUAN C Y, LIU M Y, ZHANG Z H, et al. Radiofrequency ablation versus hepatic resection for the treatment of early-stage hepatocellular carcinoma meeting Milan criteria: a systematic review and meta-analysis[J]. World J Surg Oncol, 2013, 11(1): 190. DOI: 10.1186/1477-7819-11-190.

YOUNG J L, ROFFERS S D, RIES L A G, et al. SEER summary staging manual 2000: codes and coding instructions[M]. Bethesda: National Cancer Institute, 2001.

KIM Y, PARK H C, YOON S M, et al. Prognostic group stratification andnomogram for predicting overall survival in patients who received radiotherapy for abdominal lymph node metastasis from hepatocellular carcinoma: a multi-institutional retrospective study (KROG 15-02)[J]. Oncotarget, 2017, 8(55): 94450-94461. DOI: 10.18632/oncotarget.21775.

SHEN J Y, LIU J Y, LI C, et al. The impact of tumor differentiation on the prognosis of HBV-associated solitary hepatocellular carcinoma followinghepatectomy: a propensity score matching analysis[J]. Dig Dis Sci, 2018, 63(7): 1962-1969. DOI: 10.1007/s10620-018-5077-5.

ZHAO S, SU G, YANG W K, et al. Identification and comparison of differentiation-related proteins in hepatocellular carcinoma tissues by proteomics[J].Technol Cancer Res Treat, 2017, 16(6): 1092-1101. DOI: 10.1177/1533034617732426.

NICCOLI T, PARTRIDGE L. Ageing as a risk factor for disease[J]. Curr Biol, 2012, 22 (17): R741-752. DOI: 10.1016/j.cub.2012.07.024.

OISHI K, ITAMOTO T, KOHASHI T, et al. Safety of hepatectomy for elderly patients with hepatocellular carcinoma[J]. World J Gastroenterol, 2014, 20(41): 15028-15036. DOI: 10.3748/wjg.v20.i41.15028.

TSILIMIGRAS D I, BAGANTE F, MORIS D, et al. Defining the chance of cure after resection for hepatocellular carcinoma within and beyond the Barcelona Clinic Liver Cancer Guidelines: a multi-institutional analysis of 1, 010 patients[J]. Surgery, 2019, 166(6): 967-974. DOI: 10.1016/j.surg.2019.08.010.

HUANG W J, JENG Y M, LAI H S, et al. Tumor size is a major determinant of prognosis of resected stage I hepatocellularcarcinoma[J]. Langenbecks Arch Surg, 2015, 400(6): 725-734. DOI: 10.1007/s00423-015-1329-4.

LU X Y, XI T, LAU W Y, et al.Pathobiological features of small hepatocellular carcinoma: correlation between tumor size and biological behavior[J]. J Cancer Res Clin Oncol, 2011, 137(4): 567-575. DOI: 10.1007/s00432-010-0909-5.

FENG L H, DONG H, LAU W Y, et al. Novel microvascular invasion-based prognostic nomograms to predict survival outcomes in patients after R0 resection for hepatocellular carcinoma[J]. J Cancer Res Clin Oncol, 2017, 143(2): 293-303. DOI: 10.1007/s00432-016-2286-1.

罗治文, 陈晓, 张业繁, 等.机器学习算法和COX列线图在肝细胞癌术后生存预测中的应用价值[J].中华消化外科杂志, 2020, 19 (2): 166-178. DOI: 10.3760/cma.j.issn.1673-9752.2020.02.009.

LUO Z W, CHEN X, ZHANG Y F, et al. Application value of machine learning algorithms and COX nomogram in the survival prediction of hepatocellular carcinoma after resection[J]. Chin J Dig Surg, 2020, 19 (2): 166-178. DOI: 10.3760/cma.j.issn.1673-9752.2020.02.009.

ZHU W, XIE L X, HAN J Y, et al. The application of deep learning in cancer prognosisprediction[J]. Cancers (Basel), 2020, 12(3): E603. DOI: 10.3390/cancers12030603.

FINN R S, ZHU A X, FARAH W, et al. Therapies for advanced stage hepatocellular carcinoma with macrovascular invasion or metastatic disease: a systematic review and meta-analysis[J]. Hepatol Baltim Md, 2018, 67(1): 422-435. DOI: 10.1002/hep.29486.

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