Skip to main content
SpringerLink
Log in

Preoperative prediction of postsurgical outcomes in mass-forming intrahepatic cholangiocarcinoma based on clinical, radiologic, and radiomics features

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

Abstract

Objectives

Current prognostic systems for intrahepatic cholangiocarcinoma (IHCC) rely on surgical pathology data and are not applicable to a preoperative setting. We aimed to develop and validate preoperative models to predict postsurgical outcomes in mass-forming IHCC patients based on clinical, radiologic, and radiomics features.

Methods

This multicenter retrospective cohort study included patients who underwent curative-intent resection for mass-forming IHCC. In the development cohort (single institution data), three preoperative multivariable Cox models for predicting recurrence-free survival (RFS) were constructed, including the clinical-radiologic, radiomics, and clinical-radiologic-radiomics (CRR) models based on clinical and CT findings, CT-radiomics features, and a combination of both, respectively. Model performance was evaluated in the test cohort (data from five institutions) using Harrell’s C-index and compared with postoperative prognostic systems.

Results

A total of 345 patients (233, development cohort; 112, test cohort) were evaluated. The clinical-radiologic model included five independent CT predictors (infiltrative contour, multiplicity, periductal infiltration, extrahepatic organ invasion, and suspicious metastatic lymph node) and showed similar performance in predicting RFS to the radiomics model (C-index, 0.65 vs. 0.68; p = 0.43 in the test cohort). The CRR model showed significantly improved performance (C-index, 0.71; p = 0.01) than the clinical-radiologic model and demonstrated similar performance to the postoperative prognostic systems in predicting RFS (C-index, 0.71–0.73 vs. 0.70–0.73; p ≥ 0.40) and overall survival (C-index, 0.68–0.71 vs. 0.64–0.74; p ≥ 0.27) in the test cohort.

Conclusions

A model integrating clinical, CT, and radiomics information may be useful for the preoperative assessment of postsurgical outcomes in patients with mass-forming IHCC.

Key Points

• The radiomics analysis had incremental value in predicting recurrence-free survival of patients with intrahepatic mass-forming cholangiocarcinoma.

• The clinical-radiologic-radiomics model demonstrated similar performance to the postoperatively available prognostic systems (including 8th AJCC system) in predicting recurrence-free survival and overall survival.

• The clinical-radiologic-radiomics model may be useful for the preoperative assessment of postsurgical outcomes in patients with mass-forming intrahepatic cholangiocarcinoma.

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
Fig. 5

Similar content being viewed by others

Abbreviations

AJCC:

American Joint Committee on Cancer

AP:

Arterial phase

CA19-9:

Carbohydrate antigen 19-9

CEA:

Carcinoembryonic antigen

CI:

Confidence interval

CR:

Clinical-radiologic

CRR:

Clinical-radiologic-radiomics

HR:

Hazard ratio

ICC:

Intraclass correlation coefficient

IDI:

Integrated discrimination improvement

IHCC:

Intrahepatic cholangiocarcinoma

LASSO:

Least absolute shrinkage and selection operator

NRI:

Net reclassification improvement

OS:

Overall survival

PVP:

Portal venous phase

RFS:

Recurrence-free survival

VOI:

Volume of interest

References

  1. Banales JM, Marin JJG, Lamarca A et al (2020) Cholangiocarcinoma 2020: the next horizon in mechanisms and management. Nat Rev Gastroenterol Hepatol 17:557–588

    Article  Google Scholar 

  2. Shaib YH, El-Serag HB, Davila JA, Morgan R, McGlynn KA (2005) Risk factors of intrahepatic cholangiocarcinoma in the United States: a case-control study. Gastroenterology 128:620–626

    Article  Google Scholar 

  3. Parkin DM, Ohshima H, Srivatanakul P, Vatanasapt V (1993) Cholangiocarcinoma: epidemiology, mechanisms of carcinogenesis and prevention. Cancer Epidemiol Biomarkers Prev 2:537–544

    CAS  PubMed  Google Scholar 

  4. Bridgewater J, Galle PR, Khan SA et al (2014) Guidelines for the diagnosis and management of intrahepatic cholangiocarcinoma. J Hepatol 60:1268–1289

    Article  Google Scholar 

  5. Wang Y, Li J, Xia Y et al (2013) Prognostic nomogram for intrahepatic cholangiocarcinoma after partial hepatectomy. J Clin Oncol 31:1188–1195

    Article  Google Scholar 

  6. 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  Google Scholar 

  7. Jiang W, Zeng ZC, Tang ZY et al (2011) A prognostic scoring system based on clinical features of intrahepatic cholangiocarcinoma: the Fudan score. Ann Oncol 22:1644–1652

    Article  CAS  Google Scholar 

  8. Mavros MN, Economopoulos KP, Alexiou VG, Pawlik TM (2014) Treatment and prognosis for patients with intrahepatic cholangiocarcinoma: systematic review and meta-analysis. JAMA Surg 149:565–574

    Article  Google Scholar 

  9. Amin MB, Edge S, Greene F et al (2017) AJCC cancer staging manual, 8th edn. Springer International Publishing

  10. Hyder O, Marques H, Pulitano C et al (2014) A nomogram to predict long-term survival after resection for intrahepatic cholangiocarcinoma: an Eastern and Western experience. JAMA Surg 149:432–438

    Article  Google Scholar 

  11. Nathan H, Aloia TA, Vauthey JN et al (2009) A proposed staging system for intrahepatic cholangiocarcinoma. Ann Surg Oncol 16:14–22

    Article  Google Scholar 

  12. Min JH, Kim YK, Choi SY et al (2019) Intrahepatic mass-forming cholangiocarcinoma: arterial enhancement patterns at MRI and prognosis. Radiology 290:691–699

    Article  Google Scholar 

  13. Fujita N, Asayama Y, Nishie A et al (2017) Mass-forming intrahepatic cholangiocarcinoma: enhancement patterns in the arterial phase of dynamic hepatic CT - Correlation with clinicopathological findings. Eur Radiol 27:498–506

    Article  Google Scholar 

  14. 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  Google Scholar 

  15. Ariizumi S, Kotera Y, Takahashi Y et al (2011) Mass-forming intrahepatic cholangiocarcinoma with marked enhancement on arterial-phase computed tomography reflects favorable surgical outcomes. J Surg Oncol 104:130–139

    Article  Google Scholar 

  16. Park HJ, Park B, Lee SS (2020) Radiomics and deep learning: hepatic applications. Korean J Radiol 21:387–401

    Article  Google Scholar 

  17. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  Google Scholar 

  18. Moons KG, Altman DG, Reitsma JB et al (2015) Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 162:W1–W73

    Article  Google Scholar 

  19. Zwanenburg A, Vallieres M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328–338

    Article  Google Scholar 

  20. Zwanenburg A, Leger S, Vallieres M, L¨ock S (2018) Image biomarker standardisation initiative. arXiv preprint arXiv:1612.07003. Available via https://arxiv.org/abs/1612.07003v7. Accessed 20 Aug 2020

  21. Punt CJ, Buyse M, Kohne CH et al (2007) Endpoints in adjuvant treatment trials: a systematic review of the literature in colon cancer and proposed definitions for future trials. J Natl Cancer Inst 99:998–1003

    Article  Google Scholar 

  22. Strasberg SM, Phillips C (2013) Use and dissemination of the brisbane 2000 nomenclature of liver anatomy and resections. Ann Surg 257:377–382

    Article  Google Scholar 

  23. Rubin DB (2004) Multiple imputation for nonresponse in surveys. John Wiley & Sons

  24. Vergouwe Y, Royston P, Moons KG, Altman DG (2010) Development and validation of a prediction model with missing predictor data: a practical approach. J Clin Epidemiol 63:205–214

    Article  Google Scholar 

  25. Austin PC, Tu JV (2004) Bootstrap methods for developing predictive models. Am Stat 58:131–137

    Article  Google Scholar 

  26. Wei T (2017) Corrplot: visualization of a correlation matrix. R package version 0.84. Available via http://CRAN.R-project.org/package=corrplot. Accessed 10 May 2020

  27. Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16:385–395

    Article  CAS  Google Scholar 

  28. Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15:361–387

    Article  Google Scholar 

  29. Kang L, Chen W, Petrick NA, Gallas BD (2015) Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach. Stat Med 34:685–703

    Article  Google Scholar 

  30. Moons KG, Kengne AP, Woodward M et al (2012) Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart 98:683–690

    Article  Google Scholar 

  31. Kotronen A, Peltonen M, Hakkarainen A et al (2009) Prediction of non-alcoholic fatty liver disease and liver fat using metabolic and genetic factors. Gastroenterology 137:865–872

    Article  CAS  Google Scholar 

  32. Crowson CS, Atkinson EJ, Therneau TM (2016) Assessing calibration of prognostic risk scores. Stat Methods Med Res 25:1692–1706

    Article  Google Scholar 

  33. Koo TK, Li MY (2016) A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med 15:155–163

    Article  Google Scholar 

  34. Loosen SH, Roderburg C, Kauertz KL et al (2017) CEA but not CA19-9 is an independent prognostic factor in patients undergoing resection of cholangiocarcinoma. Sci Rep 7:16975

    Article  Google Scholar 

  35. Bergquist JR, Ivanics T, Storlie CB et al (2016) Implications of CA19-9 elevation for survival, staging, and treatment sequencing in intrahepatic cholangiocarcinoma: a national cohort analysis. J Surg Oncol 114:475–482

    Article  CAS  Google Scholar 

  36. Berenguer R, Pastor-Juan MDR, Canales-Vazquez J et al (2018) Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 288:407–415

    Article  Google Scholar 

  37. Le Roy B, Gelli M, Pittau G et al (2018) Neoadjuvant chemotherapy for initially unresectable intrahepatic cholangiocarcinoma. Br J Surg 105:839–847

    Article  Google Scholar 

  38. Yadav S, Xie H, Bin-Riaz I et al (2019) Neoadjuvant vs. adjuvant chemotherapy for cholangiocarcinoma: a propensity score matched analysis. Eur J Surg Oncol 45:1432–1438

    Article  Google Scholar 

  39. (2020) NCCN clinical practice guidelines in oncology: hepatobiliary cancers. Version 5. Available via https://www.nccn.org/professionals/physician_gls/default.aspx#site. Accessed 9 Sep 2020

Download references

Funding

This study has received funding by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Grant No. 2020R1F1A1048826).

Author information

Authors and Affiliations

  1. Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea

    Hyo Jung Park, Sang Hyun Choi & Seung Soo Lee

  2. Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea

    Bumwoo Park

  3. Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea

    Seo Young Park

  4. Department of Radiology, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea

    Hyungjin Rhee & Mi-Suk Park

  5. Department of Radiology, Seoul National University Bundang Hospital, Gyeonggi-do, Republic of Korea

    Ji Hoon Park

  6. Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea

    Eun-Suk Cho

  7. Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan, Republic of Korea

    Suk-Keu Yeom

  8. Department of Radiology, National Health Insurance Service Ilsan Hospital, Goyang, Republic of Korea

    Sumi Park

Authors
  1. Hyo Jung Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bumwoo Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seo Young Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sang Hyun Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hyungjin Rhee
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ji Hoon Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Eun-Suk Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Suk-Keu Yeom
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sumi Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Mi-Suk Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Seung Soo Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Mi-Suk Park or Seung Soo Lee.

Ethics declarations

Guarantor

The scientific guarantor of this publication is S.S.L. and M.S.P.

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

One of the authors (S.Y.P.) has significant statistical expertise.

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

• multicenter study

Additional information

Publisher’s note

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

Supplementary information

ESM 1

(DOCX 69 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Park, H.J., Park, B., Park, S.Y. et al. Preoperative prediction of postsurgical outcomes in mass-forming intrahepatic cholangiocarcinoma based on clinical, radiologic, and radiomics features. Eur Radiol 31, 8638–8648 (2021). https://doi.org/10.1007/s00330-021-07926-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-021-07926-6

Keywords

Search

Navigation