Nomograms for Predicting Hepatocellular Carcinoma Recurrence and Overall Postoperative Patient Survival

Overview

Journal Front Oncol

Specialty Oncology

Date 2022 Mar 17

PMID 35296018

Authors

Lidi Ma

Kan Deng

Cheng Zhang

Haixia Li

Yingwei Luo

Yingsi Yang

Congrui Li

Xinming Li

Zhijun Geng

Chuanmiao Xie

Affiliations

Soon will be listed here.

Abstract

Background: Few studies have focused on the prognosis of patients with hepatocellular carcinoma (HCC) of Barcelona Clinic Liver Cancer (BCLC) stage 0‒C in terms of early recurrence and 5-years overall survival (OS). We sought to develop nomograms for predicting 5-year OS and early recurrence after curative resection of HCC, based on a clinicopathological‒radiological model. We also investigated whether different treatment methods influenced the OS of patients with early recurrence.

Methods: Retrospective data, including clinical pathology, radiology, and follow-up data, were collected for 494 patients with HCC who underwent hepatectomy. Nomograms estimating OS and early recurrence were constructed using multivariate Cox regression analysis, based on the random survival forest (RSF) model. We evaluated the discrimination and calibration abilities of the nomograms using concordance indices (C-index), calibration curves, and Kaplan‒Meier curves. OS curves of different treatments for patients who had recurrence within 2 years after curative surgery were depicted and compared using the Kaplan-Meier method and the log-rank test.

Results: Multivariate Cox regression revealed that BCLC stage, non-smooth margin, maximum tumor diameter, age, aspartate aminotransferase levels, microvascular invasion, and differentiation were prognostic factors for OS and were incorporated into the nomogram with good predictive performance in the training (C-index: 0.787) and testing cohorts (C-index: 0.711). A nomogram for recurrence-free survival was also developed based on four prognostic factors (BCLC stage, non-smooth margin, maximum tumor diameter, and microvascular invasion) with good predictive performance in the training (C-index: 0.717) and testing cohorts (C-index: 0.701). In comparison to the BCLC staging system, the C-index (training cohort: 0.787 vs. 0.678, 0.717 vs. 0.675; external cohort 2: 0.748 vs. 0.624, 0.729 vs. 0.587 respectively, for OS and RFS; external cohort1:0.716 vs. 0.627 for RFS, all p value<0.05), and model calibration curves all showed improved performance. Patients who underwent surgery after tumor recurrence had a higher reOS than those who underwent comprehensive treatments and supportive care.

Conclusions: The nomogram, based on clinical, pathological, and radiological factors, demonstrated good accuracy in estimating OS and recurrence, which can guide follow-up and treatment of individual patients. Reoperation may be the best option for patients with recurrence in good condition.

Citing Articles

Imaging-Based Prediction of Ki-67 Expression in Hepatocellular Carcinoma: A Retrospective Study.

Cai C, Wang L, Tao L, Zhu H, Ren Y, Li D Cancer Med. 2025; 14(4):e70562.

PMID: 39964132 PMC: 11834164. DOI: 10.1002/cam4.70562.

Construction of a random survival forest model based on a machine learning algorithm to predict early recurrence after hepatectomy for adult hepatocellular carcinoma.

Zhang J, Chen Q, Zhang Y, Zhou J BMC Cancer. 2024; 24(1):1575.

PMID: 39722042 PMC: 11670344. DOI: 10.1186/s12885-024-13366-4.

Risk factors and nomogram construction for predicting women with chronic pelvic pain:a cross-sectional population study.

Zhu M, Huang F, Xu J, Chen W, Ding B, Shen Y Heliyon. 2024; 10(14):e34534.

PMID: 39156584 PMC: 11330104. DOI: 10.1016/j.heliyon.2024.e34534.

Deep learning model based on contrast-enhanced MRI for predicting post-surgical survival in patients with hepatocellular carcinoma.

Ma L, Li C, Li H, Zhang C, Deng K, Zhang W Heliyon. 2024; 10(11):e31451.

PMID: 38868019 PMC: 11167253. DOI: 10.1016/j.heliyon.2024.e31451.

Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score.

Wang Q, Sheng S, Xiong Y, Han M, Jin R, Hu C Front Immunol. 2024; 15:1409443.

PMID: 38863693 PMC: 11165108. DOI: 10.3389/fimmu.2024.1409443.

References

Lu L, Mei J, Kan A, Ling Y, Li S, Wei W . Treatment optimization for recurrent hepatocellular carcinoma: Repeat hepatic resection versus radiofrequency ablation. Cancer Med. 2020; 9(9):2997-3005. PMC: 7196061. DOI: 10.1002/cam4.2951. View

Ng K, Cheng N, Huang J, Liao M, Chong C, Lee K . Development and validation of a novel nomogram predicting 10-year actual survival after curative hepatectomy for hepatocellular carcinoma. Surgeon. 2021; 19(6):329-337. DOI: 10.1016/j.surge.2020.11.013. View

Kim J, Joh J, Yi N, Choi G, Kim K, Lee K . Predicting Hepatocellular Carcinoma Recurrence Beyond Milan Criteria After Liver Resection for Solitary Hepatocellular Carcinoma. J Gastrointest Surg. 2019; 24(10):2219-2227. DOI: 10.1007/s11605-019-04363-1. View

Sung H, Ferlay J, Siegel R, Laversanne M, Soerjomataram I, Jemal A . Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021; 71(3):209-249. DOI: 10.3322/caac.21660. View

Park J, Chen M, Colombo M, Roberts L, Schwartz M, Chen P . Global patterns of hepatocellular carcinoma management from diagnosis to death: the BRIDGE Study. Liver Int. 2015; 35(9):2155-66. PMC: 4691343. DOI: 10.1111/liv.12818. View

Tan J, Zhao C, Peng N, Yang Y, Zhong J, Yang T . Association between age and overall survival of patients with hepatocellular carcinoma after hepatic resection. J Surg Oncol. 2016; 114(8):966-970. DOI: 10.1002/jso.24434. View

Hu C, Steingrimsson J . Personalized Risk Prediction in Clinical Oncology Research: Applications and Practical Issues Using Survival Trees and Random Forests. J Biopharm Stat. 2017; 28(2):333-349. PMC: 7196339. DOI: 10.1080/10543406.2017.1377730. View

Giannini E, Romagnoli P, Fasoli A, Botta F, Risso D, Testa R . Influence of age on clinical presentation, therapeutic options, and prognosis in anti-HCV positive cirrhotic patients with hepatocellular carcinoma. Age Ageing. 2002; 31(6):457-62. DOI: 10.1093/ageing/31.6.457. View

Zhang Z, Chen J, Jiang H, Wei Y, Zhang X, Cao L . Gadoxetic acid-enhanced MRI radiomics signature: prediction of clinical outcome in hepatocellular carcinoma after surgical resection. Ann Transl Med. 2020; 8(14):870. PMC: 7396783. DOI: 10.21037/atm-20-3041. View

10.

Renzulli M, Brocchi S, Cucchetti A, Mazzotti F, Mosconi C, Sportoletti C . Can Current Preoperative Imaging Be Used to Detect Microvascular Invasion of Hepatocellular Carcinoma?. Radiology. 2015; 279(2):432-42. DOI: 10.1148/radiol.2015150998. View

11.

Tang M, Zhou Q, Huang M, Sun K, Wu T, Li X . Nomogram development and validation to predict hepatocellular carcinoma tumor behavior by preoperative gadoxetic acid-enhanced MRI. Eur Radiol. 2021; 31(11):8615-8627. DOI: 10.1007/s00330-021-07941-7. View

12.

Yang D, Zhuang B, Wang Y, Xie X, Xie X . Radiofrequency ablation versus hepatic resection for recurrent hepatocellular carcinoma: an updated meta-analysis. BMC Gastroenterol. 2020; 20(1):402. PMC: 7693504. DOI: 10.1186/s12876-020-01544-0. View

13.

Li X, Qi Z, Du H, Geng Z, Li Z, Qin S . Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs. Eur Radiol. 2021; 32(2):771-782. DOI: 10.1007/s00330-021-08198-w. View

14.

Chan A, Zhong J, Berhane S, Toyoda H, Cucchetti A, Shi K . Development of pre and post-operative models to predict early recurrence of hepatocellular carcinoma after surgical resection. J Hepatol. 2018; 69(6):1284-1293. DOI: 10.1016/j.jhep.2018.08.027. View

15.

Ariizumi S, Kitagawa K, Kotera Y, Takahashi Y, Katagiri S, Kuwatsuru R . A non-smooth tumor margin in the hepatobiliary phase of gadoxetic acid disodium (Gd-EOB-DTPA)-enhanced magnetic resonance imaging predicts microscopic portal vein invasion, intrahepatic metastasis, and early recurrence after hepatectomy in patients.... J Hepatobiliary Pancreat Sci. 2011; 18(4):575-85. DOI: 10.1007/s00534-010-0369-y. View

16.

Tabrizian P, Jibara G, Shrager B, Schwartz M, Roayaie S . Recurrence of hepatocellular cancer after resection: patterns, treatments, and prognosis. Ann Surg. 2014; 261(5):947-55. DOI: 10.1097/SLA.0000000000000710. View

17.

Chernyak V, Fowler K, Kamaya A, Kielar A, Elsayes K, Bashir M . Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. Radiology. 2018; 289(3):816-830. PMC: 6677371. DOI: 10.1148/radiol.2018181494. View

18.

Peng S, Chen W, Lai P, Jeng Y, Sheu J, Hsu H . High alpha-fetoprotein level correlates with high stage, early recurrence and poor prognosis of hepatocellular carcinoma: significance of hepatitis virus infection, age, p53 and beta-catenin mutations. Int J Cancer. 2004; 112(1):44-50. DOI: 10.1002/ijc.20279. View

19.

Marrero J, Kulik L, Sirlin C, Zhu A, Finn R, Abecassis M . Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology. 2018; 68(2):723-750. DOI: 10.1002/hep.29913. View

20.

Gruttadauria S, Barbera F, Conaldi P, Pagano D, Liotta R, Gringeri E . Clinical and Molecular-Based Approach in the Evaluation of Hepatocellular Carcinoma Recurrence after Radical Liver Resection. Cancers (Basel). 2021; 13(3). PMC: 7866287. DOI: 10.3390/cancers13030518. View