Skip to main content
SpringerLink
Log in

A multi-center diagnostic system for intrahepatic mass-forming cholangiocarcinoma based on preoperative MRI and clinical features

  • Hepatobiliary-Pancreas
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To establish a non-invasive diagnostic system for intrahepatic mass-forming cholangiocarcinoma (IMCC) via decision tree analysis.

Methods

Totally 1008 patients with 504 pathologically confirmed IMCCs and proportional hepatocellular carcinomas (HCC) and combined hepatocellular cholangiocarcinomas (cHCC-CC) from multi-centers were retrospectively included (internal cohort n = 700, external cohort n = 308). Univariate and multivariate logistic regression analyses were applied to evaluate the independent clinical and MRI predictors for IMCC, and the selected features were used to develop a decision tree–based diagnostic system. Diagnostic efficacy of the established system was calculated by the receiver operating characteristic curve analysis in the internal training-testing and external validation cohorts, and also in small lesions  ≤ 3 cm.

Results

Multivariate analysis revealed that female, no chronic liver disease or cirrhosis, elevated carbohydrate antigen 19–9 (CA19-9) level, normal alpha-fetoprotein (AFP) level, lobulated tumor shape, progressive or persistent enhancement pattern, no enhancing tumor capsule, targetoid appearance, and liver surface retraction were independent characteristics favoring the diagnosis of IMCC over HCC or cHCC-CC (odds ratio = 3.273–25.00, p < 0.001 to p = 0.021). Among which enhancement pattern had the highest weight of 0.816. The diagnostic system incorporating significant characteristics above showed excellent performance in the internal training (area under the curve (AUC) 0.971), internal testing (AUC 0.956), and external validation (AUC 0.945) cohorts, as well as in small lesions  ≤ 3 cm (AUC 0.956).

Conclusions

In consideration of the great generalizability and clinical efficacy in multi-centers, the proposed diagnostic system may serve as a non-invasive, reliable, and easy-to-operate tool in IMCC diagnosis, providing an efficient approach to discriminate IMCC from other HCC-containing primary liver cancers.

Clinical relevance statement

This study established a non-invasive, easy-to-operate, and explainable decision tree–based diagnostic system for intrahepatic mass-forming cholangiocarcinoma, which may provide essential information for clinical decision-making.

Key Points

• Distinguishing intrahepatic mass-forming cholangiocarcinoma (IMCC) from other primary liver cancers is important for both treatment planning and outcome prediction.

• The MRI-based diagnostic system showed great performance with satisfying generalization ability in the diagnosis and discrimination of IMCC.

• The diagnostic system may serve as a non-invasive, easy-to-operate, and explainable tool in the diagnosis and risk stratification for IMCC.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

AFP:

Alpha-fetoprotein

AUC:

Area under the curve

CA19-9:

Carbohydrate antigen 19-9

cHCC-CC:

Combined hepatocellular cholangiocarcinoma

CI:

Confidence interval

HCC:

Hepatocellular carcinoma

ICC:

Intrahepatic cholangiocarcinoma

IMCC:

Intrahepatic mass-forming cholangiocarcinoma

OR:

Odds ratio

T2W:

T2-weighted

References

  1. Sirica AE, Gores GJ, Groopman JD et al (2019) Intrahepatic cholangiocarcinoma: continuing challenges and translational advances. Hepatology 69:1803–1815

    Article  PubMed  Google Scholar 

  2. Saleh M, Virarkar M, Bura V et al (2020) Intrahepatic cholangiocarcinoma: pathogenesis, current staging, and radiological findings. Abdom Radiol (NY) 45:3662–3680

    Article  PubMed  Google Scholar 

  3. Lim JH (2003) Cholangiocarcinoma: morphologic classification according to growth pattern and imaging findings. AJR Am J Roentgenol 181:819–827

    Article  PubMed  Google Scholar 

  4. Forner A, Reig M, Bruix J (2018) Hepatocellular carcinoma. Lancet 391:1301–1314

    Article  PubMed  Google Scholar 

  5. Garancini M, Goffredo P, Pagni F et al (2014) Combined hepatocellular-cholangiocarcinoma: a population-level analysis of an uncommon primary liver tumor. Liver Transpl 20:952–959

    Article  PubMed  Google Scholar 

  6. You MW, Yun SJ (2019) Differentiating between hepatocellular carcinoma and intrahepatic cholangiocarcinoma using contrast-enhanced MRI features: a systematic review and meta-analysis. Clin Radiol 74: 406 e409–406 e418

  7. Dageforde LA, Vachharajani N, Tabrizian P et al (2021) Multi-center analysis of liver transplantation for combined hepatocellular carcinoma-cholangiocarcinoma liver tumors. J Am Coll Surg 232:361–371

    Article  PubMed  Google Scholar 

  8. Entezari P, Riaz A (2020) Intrahepatic cholangiocarcinoma. Semin Intervent Radiol 37:475-483

  9. Ichikawa S, Isoda H, Shimizu T et al (2020) Distinguishing intrahepatic mass-forming biliary carcinomas from hepatocellular carcinoma by computed tomography and magnetic resonance imaging using the Bayesian method: a bi-center study. Eur Radiol 30:5992–6002

    Article  PubMed  Google Scholar 

  10. Ni T, Shang XS, Wang WT, Hu XX, Zeng MS, Rao SX (2018) Different MR features for differentiation of intrahepatic mass-forming cholangiocarcinoma from hepatocellular carcinoma according to tumor size. Br J Radiol 91:20180017

    Article  PubMed  PubMed Central  Google Scholar 

  11. Choi SH, Lee SS, Park SH et al (2019) LI-RADS classification and prognosis of primary liver cancers at gadoxetic acid-enhanced MRI. Radiology 290:388–397

    Article  PubMed  Google Scholar 

  12. Nagtegaal ID, Odze RD, Klimstra D et al (2020) The 2019 WHO classification of tumours of the digestive system. Histopathology 76:182–188

    Article  PubMed  Google Scholar 

  13. Sheng R, Sun W, Huang X et al (2022) Apparent diffusion coefficient MRI shows association with early progression of unresectable intrahepatic cholangiocarcinoma with combined targeted-immunotherapy. J Magn Reson Imaging 57:275–284

    Article  PubMed  Google Scholar 

  14. Choi SY, Kim YK, Min JH et al (2018) Added value of ancillary imaging features for differentiating scirrhous hepatocellular carcinoma from intrahepatic cholangiocarcinoma on gadoxetic acid-enhanced MR imaging. Eur Radiol 28:2549–2560

    Article  PubMed  Google Scholar 

  15. Chernyak V, Fowler KJ, Kamaya A et al (2018) Liver Imaging Reporting and Data System (LI-RADS) version 2018: imaging of hepatocellular carcinoma in at-risk patients. Radiology 289:816–830

    Article  PubMed  Google Scholar 

  16. Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD (2004) An introduction to decision tree modeling. J Chemometr 18:275–285

    Article  CAS  Google Scholar 

  17. Song YY, Lu Y (2015) Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry 27:130–135

    PubMed  PubMed Central  Google Scholar 

  18. Kotsiantis SB (2013) Decision trees: a recent overview. Artif Intell Rev 39:261–283

    Article  Google Scholar 

  19. Massarweh NN, El-Serag HB (2017) Epidemiology of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Cancer Control 24:1073274817729245

    Article  PubMed  PubMed Central  Google Scholar 

  20. Weber SM, Ribero D, O'Reilly EM, Kokudo N, Miyazaki M, Pawlik TM (2015) Intrahepatic cholangiocarcinoma: expert consensus statement. HPB (Oxford) 17:669-680

  21. Bertuccio P, Malvezzi M, Carioli G et al (2019) Global trends in mortality from intrahepatic and extrahepatic cholangiocarcinoma. J Hepatol 71:104–114

    Article  PubMed  Google Scholar 

  22. Huang B, Wu L, Lu XY et al (2016) Small intrahepatic cholangiocarcinoma and hepatocellular carcinoma in cirrhotic livers may share similar enhancement patterns at multiphase dynamic MR imaging. Radiology 281:150–157

    Article  PubMed  Google Scholar 

  23. Viganò L, Lleo A, Muglia R et al (2020) Intrahepatic cholangiocellular carcinoma with radiological enhancement patterns mimicking hepatocellular carcinoma. Updates Surg 72:413–421

    Article  PubMed  Google Scholar 

  24. Kovač JD, Janković A, Đikić-Rom A, Grubor N, Antić A, Dugalić V (2022) Imaging spectrum of intrahepatic mass-forming cholangiocarcinoma and its mimickers: how to differentiate them using MRI. Curr Oncol 29:698–723

    Article  PubMed  PubMed Central  Google Scholar 

  25. Xu J, Igarashi S, Sasaki M et al (2012) Intrahepatic cholangiocarcinomas in cirrhosis are hypervascular in comparison with those in normal livers. Liver Int 32:1156–1164

    Article  PubMed  Google Scholar 

  26. Kierans AS, Lafata KJ, Ludwig DR et al (2022) Comparing survival outcomes of patients with LI-RADS-M hepatocellular carcinomas and intrahepatic cholangiocarcinomas. J Magn Reson Imaging 57:308–317

    Article  PubMed  Google Scholar 

  27. van der Pol CB, Lim CS, Sirlin CB et al (2019) Accuracy of the Liver Imaging Reporting and Data System in computed tomography and magnetic resonance image analysis of hepatocellular carcinoma or overall malignancy-a systematic review. Gastroenterology 156:976–986

    Article  PubMed  Google Scholar 

  28. Tang A, Bashir MR, Corwin MT et al (2018) Evidence supporting LI-RADS major features for CT- and MR imaging-based diagnosis of hepatocellular carcinoma: a systematic review. Radiology 286:29–48

    Article  PubMed  Google Scholar 

  29. Min JH, Kim YK, Choi SY et al (2017) Differentiation between cholangiocarcinoma and hepatocellular carcinoma with target sign on diffusion-weighted imaging and hepatobiliary phase gadoxetic acid-enhanced MR imaging: classification tree analysis applying capsule and septum. Eur J Radiol 92:1–10

    Article  PubMed  Google Scholar 

  30. Kim YY, Kim MJ, Kim EH, Roh YH, An C (2019) Hepatocellular carcinoma versus other hepatic malignancy in cirrhosis: performance of LI-RADS version 2018. Radiology 291:72–80

    Article  PubMed  Google Scholar 

  31. Chen Y, Pan Y, Shen KR et al (2017) Contrast-enhanced multiple-phase imaging features of intrahepatic mass-forming cholangiocarcinoma and hepatocellular carcinoma with cirrhosis: a comparative study. Oncol Lett 14:4213–4219

    Article  PubMed  PubMed Central  Google Scholar 

  32. Sheng R, Zeng M, Jin K, Zhang Y, Wu D, Sun H (2022) MRI-based nomogram predicts the risk of progression of unresectable hepatocellular carcinoma after combined lenvatinib and anti-PD-1 antibody therapy. Acad Radiol 29:819–829

    Article  PubMed  Google Scholar 

  33. Zhao YJ, Chen WX, Wu DS, Zhang WY, Zheng LR (2016) Differentiation of mass-forming intrahepatic cholangiocarcinoma from poorly differentiated hepatocellular carcinoma: based on the multivariate analysis of contrast-enhanced computed tomography findings. Abdom Radiol (NY) 41:978–989

    Article  PubMed  Google Scholar 

  34. Kang Y, Lee JM, Kim SH, Han JK, Choi BI (2012) Intrahepatic mass-forming cholangiocarcinoma: enhancement patterns on gadoxetic acid-enhanced MR images. Radiology 264:751–760

    Article  PubMed  Google Scholar 

  35. Zhou Y, Wang X, Xu C et al (2019) Mass-forming intrahepatic cholangiocarcinoma: can diffusion-weighted imaging predict microvascular invasion? J Magn Reson Imaging 50:315–324

    Article  PubMed  Google Scholar 

  36. Yang CM, Shu J (2021) Cholangiocarcinoma evaluation via imaging and artificial intelligence. Oncology 99:72–83

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank the statistician Minzhi Lv for her support in statistical analyses in this work.

Funding

This study was funded by the Natural Science Foundation of Shanghai [grant number 23ZR1459500], the Natural Science Foundation of Fujian Province [grant number 2021D032], the Shanghai Municipal Key Clinical Specialty [grant number shslczdzk03202], and the National Natural Science Foundation of China [grant number 82171897].

Author information

Author notes

  1. Ruofan Sheng and Yunfei Zhang contribute the same as first authors.

  2. Mengsu Zeng and Jianjun Zhou contribute the same as corresponding authors.

Authors and Affiliations

  1. Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, No. 668 Jinhu Road, Huli District, Xiamen, 361015, Fujian, China

    Ruofan Sheng, Heqing Wang & Jianjun Zhou

  2. Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China

    Ruofan Sheng, Kaipu Jin, Wei Sun, Jianjun Zhou & Mengsu Zeng

  3. Shanghai Institute of Medical Imaging, Shanghai, 200032, China

    Ruofan Sheng, Yunfei Zhang, Kaipu Jin, Wei Sun & Mengsu Zeng

  4. Central Research Institute, United Imaging Healthcare, Shanghai, 201800, China

    Yunfei Zhang & Yongming Dai

  5. Dushu Lake Public Hospital Affiliated to Soochow University, Suzhou, 215028, China

    Weiguo Zhang

  6. Xiamen Municipal Clinical Research Center for Medical Imaging, and Xiamen Key Clinical Specialty for Radiology, Xiamen, 361015, China

    Jianjun Zhou

  7. Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China

    Mengsu Zeng

Authors
  1. Ruofan Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yunfei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Heqing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weiguo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kaipu Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wei Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yongming Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jianjun Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mengsu Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jianjun Zhou or Mengsu Zeng.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Mengsu Zeng.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

The statistician Minzhi Lv kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

No.

Methodology

• retrospective

• diagnostic or prognostic study

• multi-center study

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.

Supplementary file1 (PDF 188 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sheng, R., Zhang, Y., Wang, H. et al. A multi-center diagnostic system for intrahepatic mass-forming cholangiocarcinoma based on preoperative MRI and clinical features. Eur Radiol 34, 548–559 (2024). https://doi.org/10.1007/s00330-023-10002-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-023-10002-w

Keywords

Search

Navigation