Skip to main content

Advertisement

Log in

Novel combined fibrosis-based index predicts the long-term outcomes of hepatocellular carcinoma after hepatic resection

  • Original Article
  • Published:
International Journal of Clinical Oncology Aims and scope Submit manuscript

Abstract

Aim

Liver fibrosis influences liver regeneration and surgical outcomes. The fibrosis-4 (FIB-4) index is strongly associated with liver fibrosis and cirrhosis. This study aimed to examine the prognostic significance of the combination of FIB-4 index and Protein Induced by Vitamin K Absence or Antagonist-II (PIVKA-II) (PIVKA-II–FIB-4 index score) in patients who underwent curative resection for hepatocellular carcinoma (HCC).

Methods

We included 284 patients who underwent elective hepatic resection for HCC between January 2000 and December 2018. We retrospectively investigated how FIB-4 index is related to disease-free survival and overall survival.

Results

According to a receiver operating characteristic (ROC) analysis, the optimal cutoff value of the FIB-4 index was 3.44. In a multivariate analysis, high PIVKA-II and FIB-4 index values were independent predictors of both disease-free survival (P = 0.013 and P = 0.005, respectively) and overall survival (P = 0.048 and P < 0.001, respectively). We classified the PIVKA-II and FIB-4 index levels into two groups (high vs. low) and calculated a new score (PIVKA-II–FIB-4 index score; 0–2) by the sum of each measurement (high, 1; low, 0). The 5 year overall survival rates of patients with PIVKA-II–FIB-4 index scores of 0, 1, and 2 were 84.9, 74.4, and 47.1%, respectively (P < 0.001).

Conclusion

The combination of the preoperative PIVKA-II and FIB-4 index may be a prognostic factor of HCC after hepatic resection, suggesting that the combined score is useful in assessing the liver fibrosis status in cancer cases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

HCC:

Hepatocellular carcinoma

HBV:

Hepatitis B virus

HCV:

Hepatitis C virus

ICGR15 :

Retention rate of indocyanine green at 15 min

AFP:

Alpha-fetoprotein

PNI:

Prognostic Nutritional Index

TACE:

Transarterial chemoembolization

mALBI:

Modified albumin bilirubin

ROC:

Receiver operating characteristic

SD:

Standard deviation

IQR:

Interquartile range

CI:

Confidence interval

NS:

Not significant

References

  1. Torre LA, Bray F, Siegel RL et al (2015) Global cancer statistics, 2012. CA Cancer J Clin 65:87–108

    Article  Google Scholar 

  2. Yang HJ, Guo Z, Yang YT et al (2016) Blood neutrophil-lymphocyte ratio predicts survival after hepatectomy for hepatocellular carcinoma: a propensity score-based analysis. World J Gastroenterol 22:5088–5095

    Article  CAS  Google Scholar 

  3. Yoshida H, Shiratori Y, Moriyama M et al (1999) Interferon therapy reduces the risk for hepatocellular carcinoma: national surveillance program of cirrhotic and noncirrhotic patients with Chronic Hepatitis C in Japan. IHIT study group. inhibition of hepatocarcinogenesis by interferon therapy. Ann Intern Med 131:174–181

    Article  CAS  Google Scholar 

  4. Ko S, Kanehiro H, Hisanaga M et al (2002) Liver fibrosis increases the risk of intrahepatic recurrence after hepatectomy for hepatocellular carcinoma. Br J Surg 89:57–62

    Article  CAS  Google Scholar 

  5. Gassmann P, Spieker T, Haier J et al (2010) Prognostic impact of underlying liver fibrosis and cirrhosis after curative resection of hepatocellular carcinoma. World J Surg 34:2442–2451

    Article  Google Scholar 

  6. Kaibori M, Kubo S, Nagano H et al (2013) Clinicopathological features of recurrence in patients after 10-year disease-free survival following curative hepatic resection of hepatocellular carcinoma. World J Surg 37:820–828

    Article  Google Scholar 

  7. Sterling RK, Lissen E, Clumeck N et al (2006) Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology 43:1317–1325

    Article  CAS  Google Scholar 

  8. Wai CT, Greenson JK, Fontana RJ et al (2003) A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology 38:518–526

    Article  Google Scholar 

  9. Sebastiani G, Vario A, Guido M et al (2006) Stepwise combination algorithms of non-invasive markers to diagnose significant fibrosis in chronic hepatitis C. J Hepatol 44:686–693

    Article  CAS  Google Scholar 

  10. Sumida Y, Yoneda M, Hyogo H et al (2012) Validation of the FIB4 index in a Japanese nonalcoholic fatty liver disease population. BMC Gastroenterol 12:2

    Article  CAS  Google Scholar 

  11. Shah AG, Lydecker A, Murray K et al (2009) Comparison of noninvasive markers of fibrosis in patients with nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol 7:1104–1112

    Article  CAS  Google Scholar 

  12. Naveau S, Gaude G, Asnacios A et al (2009) Diagnostic and prognostic values of noninvasive biomarkers of fibrosis in patients with alcoholic liver disease. Hepatology 49:97–105

    Article  Google Scholar 

  13. Okamura Y, Ashida R, Yamamoto Y et al (2016) FIB-4 index is a predictor of background liver fibrosis and long-term outcomes after curative resection of hepatocellular carcinoma. Ann Surg Oncol 23:467–474

    Article  Google Scholar 

  14. Wang H, Liu A, Bo W et al (2018) Fibrosis-4 model influences results of patients with hepatocellular carcinoma undergoing hepatectomy. Biomed Res Int 2018:4305408

    PubMed  PubMed Central  Google Scholar 

  15. Toyoda H, Kumada T, Tada T et al (2015) A laboratory marker, FIB-4 index, as a predictor for long-term outcomes of hepatocellular carcinoma patients after curative hepatic resection. Surgery 157:699–707

    Article  Google Scholar 

  16. Payancé A, Dioguardi Burgio M, Peoc’h K et al (2020) Biological response under treatment and prognostic value of protein induced by vitamin K absence or antagonist-II in a French cohort of patients with hepatocellular carcinoma. Eur J Gastroenterol Hepatol 32:1364–1372

    Article  Google Scholar 

  17. Miyagawa S, Makuuchi M, Kawasaki S et al (1995) Criteria for safe hepatic resection. Am J Surg 169:589–594

    Article  CAS  Google Scholar 

  18. Strasberg SM (2005) Nomenclature of hepatic anatomy and resections: a review of the Brisbane 2000 system. J Hepatobiliary Pancreat Surg 12:351–355

    Article  Google Scholar 

  19. Ueno S, Tanabe G, Nuruki K et al (2002) Prognostic performance of the new classification of primary liver cancer of Japan (4th edition) for patients with hepatocellular carcinoma: a validation analysis. Hepatol Res 24:395–403

    Article  Google Scholar 

  20. Sato Y, Iwata T, Yoshimoto J et al (2003) Clinicalpathological analysis of risk factors for the development of hepatocellular carcinoma after surgery for esophageal varices due to underlying cirrhosis or pre-cirrhosis in the 397 patients. Hepatol Res 25:62–70

    Article  Google Scholar 

  21. Hiraoka A, Kumada T, Tsuji K et al (2019) Validation of modified ALBI grade for more detailed assessment of hepatic function in hepatocellular carcinoma patients: a multicenter analysis. Liver Cancer 8:121–129

    Article  Google Scholar 

  22. Nagai S, Mangus RS, Kubal CA et al (2015) Prognosis after recurrence of hepatocellular carcinoma in liver transplantation: predictors for successful treatment and survival. Clin Transplant 29:1156–1163

    Article  CAS  Google Scholar 

  23. Toyoda H, Lai PB, O’Beirne J et al (2016) Long-term impact of liver function on curative therapy for hepatocellular carcinoma: application of the ALBI grade. Br J Cancer 114:744–750

    Article  CAS  Google Scholar 

  24. Bruix J, Castells A, Bosch J et al (1996) Surgical resection of hepatocellular carcinoma in cirrhotic patients: prognostic value of preoperative portal pressure. Gastroenterol 111:1018–1022

    Article  CAS  Google Scholar 

  25. Park YK, Kim BW, Wang HJ et al (2009) Hepatic resection for hepatocellular carcinoma meeting Milan criteria in Child-Turcotte-Pugh class a patients with cirrhosis. Transplant Proc 41:1691–1697

    Article  Google Scholar 

  26. Kamarajah SK (2018) Fibrosis score impacts survival following resection for hepatocellular carcinoma (HCC): a surveillance, end results and epidemiology (SEER) database analysis. Asian J Surg 41:551–561

    Article  Google Scholar 

  27. Akiyama T, Miyamoto Y, Imai K et al (2020) Fibrosis-4 index, a noninvasive fibrosis marker, predicts survival outcomes after hepatectomy for colorectal cancer liver metastases. Ann Surg Oncol 27:3534–3541

    Article  Google Scholar 

  28. Anstee QM, Lawitz EJ, Alkhouri N et al (2019) Noninvasive tests accurately identify advanced fibrosis due to NASH: baseline data from the STELLAR trials. Hepatology 70:1521–1530

    Article  Google Scholar 

  29. Zhou P, Chen B, Miao XY et al (2020) Comparison of FIB-4 index and Child-Pugh Score in predicting the outcome of hepatic resection for hepatocellular carcinoma. J Gastrointest Surg 24:823–831

    Article  Google Scholar 

  30. Feng JW, Qu Z, Wu BQ et al (2019) The preoperative fibrosis score 4 predicts posthepatectomy liver failure in patients with hepatocellular carcinoma. Ann Hepatol 18:701–707

    Article  Google Scholar 

  31. Itoh S, Yoshizumi T, Kitamura Y et al (2021) Impact of metabolic activity in hepatocellular carcinoma: association with immune status and vascular formation. Hepatol Commun 5:1278–1289

    Article  CAS  Google Scholar 

  32. Yugawa K, Itoh S, Yoshizumi T et al (2021) CMTM6 Stabilizes PD-L1 expression and is a new prognostic impact factor in hepatocellular carcinoma. Hepatol Commun 5:334–348

    Article  CAS  Google Scholar 

  33. Iseda N, Itoh S, Yoshizumi T et al (2021) Lymphocyte-to-C-reactive protein ratio as a prognostic factor for hepatocellular carcinoma. Int J Clin Oncol 26:1890–1900

    Article  CAS  Google Scholar 

  34. Pfister D, Nunez NG, Pinyol R et al (2021) NASH limits anti-tumour surveillance in immunotherapy-treated HCC. Nature 592:450–456

    Article  CAS  Google Scholar 

  35. Li C, Zhang Z, Zhang P et al (2014) Diagnostic accuracy of des-gamma-carboxy prothrombin versus alpha-fetoprotein for hepatocellular carcinoma: a systematic review. Hepatol Res 44:E11-25

    Article  CAS  Google Scholar 

  36. Bertino G, Ardiri AM, Boemi PM et al (2008) A study about mechanisms of des-gamma-carboxy prothrombin’s production in hepatocellular carcinoma. Panminerva Med 50:221–226

    CAS  PubMed  Google Scholar 

  37. Basile U, Miele L, Napodano C et al (2020) The diagnostic performance of PIVKA-II in metabolic and viral hepatocellular carcinoma: a pilot study. Eur Rev Med Pharmacol Sci 24:12675–12685

    CAS  PubMed  Google Scholar 

  38. Yu R, Tan Z, Xiang X et al (2017) Effectiveness of PIVKA-II in the detection of hepatocellular carcinoma based on real-world clinical data. BMC Cancer 17:608

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Enago (www.enago.jp) for the English language review.

Funding

This work was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 20K17665 (Dr. Yoshihiro Shirai).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yoshihiro Shirai.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest in association with the present study.

Ethics statement

The Ethics Committee of The Jikei University School of Medicine approved this study (Approval No.: 27–177 [8062]).

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

10147_2021_2111_MOESM1_ESM.tif

Supplementary file1 ROC curve of the FIB-4 index for the survival status at 5 years of follow-up. The optimal cutoff value for the FIB-4 index was 3.44, with an AUC of 0.611 (95% CI, 0.519–0.703; P = 0.017). AUC, area under the curve; CI, confidence interval; FIB-4, fibrosis-4; ROC, receiver operating characteristic analysis. (TIF 368 KB)

10147_2021_2111_MOESM2_ESM.tif

Supplementary file2 ROC curve of the FIB-4 index, serum PIVKA-II, and PIVKA-II–FIB-4 index score for the survival status at 5 years of follow-up. The AUC of the PIVKA-II–FIB-4 index score (AUC, 0.661; 95% CI, 0.568–0.755; P = 0.001) for the survival status at the 5-year follow-up was greater than that of the FIB-4 index (AUC, 0.625; 95% CI, 0.528–0.722; P = 0.011) and serum PIVKA-II (AUC, 0.603; 95% CI, 0.508–0.698; P = 0.037). AUC, area under the curve; CI, confidence interval; FIB-4, fibrosis-4; PIVKA-II, Protein Induced by Vitamin K Absence or Antagonist-II; ROC, receiver operating characteristic analysis. (TIF 429 KB)

Supplementary file3 (DOCX 16 KB)

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yanagaki, M., Shirai, Y., Hamura, R. et al. Novel combined fibrosis-based index predicts the long-term outcomes of hepatocellular carcinoma after hepatic resection. Int J Clin Oncol 27, 717–728 (2022). https://doi.org/10.1007/s10147-021-02111-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10147-021-02111-7

Keywords

Navigation