Skip to main content

A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma

European Radiology volume 29, pages 3725–3735 (2019)Cite this article

Abstract

Objectives

This study was conducted in order to establish and validate a radiomics model for predicting lymph node (LN) metastasis of intrahepatic cholangiocarcinoma (IHC) and to determine its prognostic value.

Methods

For this retrospective study, a radiomics model was developed in a primary cohort of 103 IHC patients who underwent curative-intent resection and lymphadenectomy. Radiomics features were extracted from arterial phase computed tomography (CT) scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method. Multivariate logistic regression analysis was adopted to establish a radiomics model incorporating radiomics signature and other independent predictors. Model performance was determined by its discrimination, calibration, and clinical usefulness. The model was internally validated in 52 consecutive patients.

Results

The radiomics signature comprised eight LN-status–related features and showed significant association with LN metastasis in both cohorts (p < 0.001). A radiomics nomogram that incorporates radiomics signature and CA 19-9 level showed good calibration and discrimination in the primary cohort (AUC 0.8462) and validation cohort (AUC 0.8921). Promisingly, the radiomics nomogram yielded an AUC of 0.9224 in the CT-reported LN-negative subgroup. Decision curve analysis confirmed the clinical utility of this nomogram. High risk for metastasis portended significantly lower overall and recurrence-free survival than low risk for metastasis (both p < 0.001). The radiomics nomogram was an independent preoperative predictor of overall and recurrence-free survival.

Conclusions

Our radiomics model provided a robust diagnostic tool for prediction of LN metastasis, especially in CT-reported LN-negative IHC patients, that may facilitate clinical decision-making.

Key Points

• The radiomics nomogram showed good performance for prediction of LN metastasis in IHC patients, particularly in the CT-reported LN-negative subgroup.

• Prognosis of high-risk patients remains dismal after curative-intent resection.

• The radiomics model may facilitate clinical decision-making and define patient subsets benefiting most from surgery.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Abbreviations

BTC:

Biliary tract cancer

DCA:

Decision curve analysis

IHC:

Intrahepatic cholangiocarcinoma

ICC:

Intraclass correlation coefficient

LASSO:

Least absolute shrinkage and selection operator

NPV:

Negative predictive value

OS:

Overall survival

PPV:

Positive predictive value

RFS:

Recurrence-free survival

ROC:

Receiver operating characteristic curve

ROI:

Region of interest

VIF:

Variance inflation factor

References

  1. Rizvi S, Khan SA, Hallemeier CL, Kelley RK, Gores GJ (2018) Cholangiocarcinoma—evolving concepts and therapeutic strategies. Nat Rev Clin Oncol 15:95–111

    Article  CAS  PubMed  Google Scholar 

  2. Joo I, Lee JM, Yoon JH (2018) Imaging diagnosis of intrahepatic and perihilar cholangiocarcinoma: recent advances and challenges. Radiology 288:7–13

    Article  PubMed  Google Scholar 

  3. Khan SA, Davidson BR, Goldin RD et al (2014) Guidelines for the diagnosis and management of intrahepatic cholangiocarcinoma. J Hepatol 60:1268–1289

    Article  PubMed  Google Scholar 

  4. Zhang XF, Beal EW, Bagante F et al (2018) Early versus late recurrence of intrahepatic cholangiocarcinoma after resection with curative intent. Br J Surg 105:848–856

    Article  PubMed  Google Scholar 

  5. Zhang XF, Chakedis J, Bagante F et al (2018) Trends in use of lymphadenectomy in surgery with curative intent for intrahepatic cholangiocarcinoma. Br J Surg 105:857–866

    Article  PubMed  Google Scholar 

  6. Adachi T, Eguchi S, Beppu T et al (2015) Prognostic impact of preoperative lymph node enlargement in intrahepatic cholangiocarcinoma: a multi-institutional study by the Kyushu Study Group of Liver Surgery. Ann Surg Oncol 22:2269–2278

    Article  PubMed  Google Scholar 

  7. de Jong MC, Nathan H, Sotiropoulos GC et al (2011) Intrahepatic cholangiocarcinoma: an international multi-institutional analysis of prognostic factors and lymph node assessment. J Clin Oncol 29:3140–3145

    Article  PubMed  Google Scholar 

  8. Morine Y, Shimada M (2015) The value of systematic lymph node dissection for intrahepatic cholangiocarcinoma from the viewpoint of liver lymphatics. J Gastroenterol 50:913–927

    Article  CAS  PubMed  Google Scholar 

  9. Benson AB 3rd, D’Angelica MI, Abrams TA et al (2014) Hepatobiliary cancers, version 2.2014. J Natl Compr Cancer Netw 12:1152–1182

    Article  CAS  Google Scholar 

  10. Razumilava N, Gores GJ (2014) Cholangiocarcinoma. Lancet 383:2168–2179

    Article  PubMed  PubMed Central  Google Scholar 

  11. Blechacz B, Komuta M, Roskams T, Gores GJ (2011) Clinical diagnosis and staging of cholangiocarcinoma. Nat Rev Gastroenterol Hepatol 8:512–522

    Article  PubMed  PubMed Central  Google Scholar 

  12. Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  Google Scholar 

  13. Limkin EJ, Sun R, Dercle L et al (2017) Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol 28:1191–1206

    Article  CAS  PubMed  Google Scholar 

  14. Aerts HJ (2016) The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol 2:1636–1642

    Article  Google Scholar 

  15. Huang YQ, Liang CH, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164

    Article  Google Scholar 

  16. Wu S, Zheng J, Li Y et al (2017) A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer. Clin Cancer Res 23:6904–6911

    Article  CAS  Google Scholar 

  17. Gu Y, She Y, Xie D et al (2018) A texture analysis-based prediction model for lymph node metastasis in stage IA lung adenocarcinoma. Ann Thorac Surg 106:214–220

    Article  PubMed  Google Scholar 

  18. Ji GW, Zhang YD, Zhang H et al (2019) Biliary tract cancer at CT: a radiomics-based model to predict lymph node metastasis and survival outcomes. Radiology 290:90–98

    Article  PubMed  Google Scholar 

  19. Kim SA, Lee JM, Lee KB et al (2011) Intrahepatic mass-forming cholangiocarcinomas: enhancement patterns at multiphasic CT, with special emphasis on arterial enhancement pattern—correlation with clinicopathologic findings. Radiology 260:148–157

    Article  PubMed  Google Scholar 

  20. Yamamoto Y, Türkoğlu MA, Aramaki T et al (2016) Vascularity of intrahepatic cholangiocarcinoma on computed tomography is predictive of lymph node metastasis. Ann Surg Oncol 23:485–493

    Article  PubMed  Google Scholar 

  21. Lee HY, Kim SH, Lee JM et al (2006) Preoperative assessment of resectability of hepatic hilar cholangiocarcinoma: combined CT and cholangiography with revised criteria. Radiology 239:113–121

    Article  Google Scholar 

  22. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Stat Methodol 58:267–288

    Google Scholar 

  24. Kutner MH, Nachtsheim C, Neter J (2004) Applied linear regression models. McGraw-Hill/Irwin

  25. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36

    Article  CAS  Google Scholar 

  26. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

    Article  CAS  Google Scholar 

  27. Youden WJ (1950) Index for rating diagnostic tests. Cancer 3:32–35

    Article  CAS  Google Scholar 

  28. Fitzgerald M, Saville BR, Lewis RJ (2015) Decision curve analysis. JAMA 313:409–410

    Article  CAS  PubMed  Google Scholar 

  29. Ruys AT, Kate FJ, Busch OR, Engelbrecht MR, Gouma DJ, van Gulik TM (2011) Metastatic lymph nodes in hilar cholangiocarcinoma: does size matter? HPB (Oxford) 13:881–886

    Article  Google Scholar 

  30. Thoeny HC, Froehlich JM, Triantafyllou M et al (2014) Metastases in normal-sized pelvic lymph nodes: detection with diffusion-weighted MR imaging. Radiology 273:125–135

    Article  PubMed  PubMed Central  Google Scholar 

  31. Bedard PL, Hansen AR, Ratain MJ, Siu LL (2013) Tumour heterogeneity in the clinic. Nature 501:355–364

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Yoh T, Hatano E, Seo S et al (2018) Preoperative criterion identifying a low-risk group for lymph node metastasis in intrahepatic cholangiocarcinoma. J Hepatobiliary Pancreat Sci 25:299–307

    Article  PubMed  Google Scholar 

  33. Asayama Y, Yoshimitsu K, Irie H et al (2006) Delayed-phase dynamic CT enhancement as a prognostic factor for mass-forming intrahepatic cholangiocarcinoma. Radiology 238:150–155

    Article  Google Scholar 

  34. Rhee H, Kim MJ, Park YN, An C (2018) A proposal of imaging classification of intrahepatic mass-forming cholangiocarcinoma into ductal and parenchymal types: clinicopathologic significance. Eur Radiol. https://doi.org/10.1007/s00330-018-5898-9

Download references

Funding

This study was supported by the Natural Science Foundation of China (81530048, 81470901, 81670570) and the Key Research and Development Program of Jiangsu Province (BE2016789).

Author information

Author notes

  1. Gu-Wei Ji and Fei-Peng Zhu contributed equally to this work.

Affiliations

  1. Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, People’s Republic of China

    Gu-Wei Ji, Ke Wang, Yong-Xiang Xia, Yao-Dong Zhang, Wang-Jie Jiang, Xiang-Cheng Li & Xue-Hao Wang

  2. Key Laboratory of Liver Transplantation, Chinese Academy of Medical Sciences, Nanjing, People’s Republic of China

    Gu-Wei Ji, Ke Wang, Yong-Xiang Xia, Yao-Dong Zhang, Wang-Jie Jiang, Xiang-Cheng Li & Xue-Hao Wang

  3. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People’s Republic of China

    Fei-Peng Zhu, Yu-Dong Zhang, Xi-Sheng Liu & Fei-Yun Wu

Authors
  1. Gu-Wei Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei-Peng Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu-Dong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xi-Sheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fei-Yun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ke Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yong-Xiang Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yao-Dong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wang-Jie Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xiang-Cheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Xue-Hao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xiang-Cheng Li or Xue-Hao Wang.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Xue-Hao Wang.

Conflict of interest

The authors declare that they have no conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

Additional information

Publisher’s note

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

Electronic supplementary material

ESM 1

(DOC 3057 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ji, GW., Zhu, FP., Zhang, YD. et al. A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma. Eur Radiol 29, 3725–3735 (2019). https://doi.org/10.1007/s00330-019-06142-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-019-06142-7

Keywords

  • Cholangiocarcinoma
  • Nomogram
  • Lymphatic metastasis
  • Decision support techniques
  • Radiomics