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Predicting Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma

  • Original Article
  • Published:
Journal of Gastrointestinal Surgery

Abstract

Background

The objective of the current study was to develop a model to predict the likelihood of occult lymph node metastasis (LNM) prior to resection of intrahepatic cholangiocarcinoma (ICC).

Methods

Patients who underwent hepatectomy for ICC between 2000 and 2017 were identified using a multi-institutional database. A novel model incorporating clinical and preoperative imaging data was developed to predict LNM.

Results

Among 980 patients who underwent resection of ICC, 190 (19.4%) individuals had at least one LNM identified on final pathology. An enhanced imaging model incorporating clinical and imaging data was developed to predict LNM (https://k-sahara.shinyapps.io/ICC_imaging/). The performance of the enhanced imaging model was very good in the training data set (c-index 0.702), as well as the validation data set with bootstrapping resamples (c-index 0.701) and outperformed the preoperative imaging alone (c-index 0.660). The novel model predicted both 5-year overall survival (OS) (low risk 48.4% vs. high risk 18.4%) and 5-year disease-specific survival (DSS) (low risk 51.9% vs. high risk 25.2%, both p < 0.001). When applied among Nx patients, 5-year OS and DSS of low-risk Nx patients was comparable with that of N0 patients, while high-risk Nx patients had similar outcomes to N1 patients (p > 0.05).

Conclusion

This tool may represent an opportunity to stratify prognosis of Nx patients and can help inform clinical decision-making prior to resection of ICC.

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Authors and Affiliations

  1. Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA

    Diamantis I. Tsilimigras, Kota Sahara, Anghela Z. Paredes, Amika Moro, Rittal Mehta, Dimitrios Moris & Timothy M. Pawlik

  2. Department of Surgery, University of Verona, Verona, Italy

    Alfredo Guglielmi

  3. Department of Surgery, Ospedale San Raffaele, Milano, Italy

    Luca Aldrighetti

  4. Department of Surgery, Johns Hopkins Hospital, Baltimore, MD, USA

    Matthew Weiss

  5. Department of Surgery, University of Virginia, Charlottesville, VA, USA

    Todd W. Bauer

  6. Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania

    Sorin Alexandrescu

  7. Department of Surgery, Stanford University, Stanford, CA, USA

    George A. Poultsides

  8. Department of Surgery, Emory University, Atlanta, GA, USA

    Shishir K. Maithel

  9. Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal

    Hugo P. Marques

  10. Department of Surgery, University of Ottawa, Ottawa, Canada

    Guillaume Martel

  11. Department of Surgery, Royal Prince Alfred Hospital, University of Sydney, Sydney, Australia

    Carlo Pulitano

  12. Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China

    Feng Shen

  13. Department of Hepatobiliopancreatic Surgery and Liver Transplantation, AP-HP, Beaujon Hospital, Clichy, France

    Olivier Soubrane

  14. Department of Surgery, Erasmus University Medical Centre, Rotterdam, Netherlands

    Bas Groot Koerkamp

  15. Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan

    Itaru Endo

Authors
  1. Diamantis I. Tsilimigras
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  2. Kota Sahara
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  8. Luca Aldrighetti
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  9. Matthew Weiss
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  11. Sorin Alexandrescu
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  13. Shishir K. Maithel
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  14. Hugo P. Marques
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  16. Carlo Pulitano
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  17. Feng Shen
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  18. Olivier Soubrane
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  19. Bas Groot Koerkamp
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  20. Itaru Endo
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  21. Timothy M. Pawlik
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Corresponding author

Correspondence to Timothy M. Pawlik.

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Presented as an Oral Presentation at the AHPBA 2020 Annual Meeting, Miami, FL, March 2020

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Supplementary Figure 1

Online calculator to predict the probability of LNM (PNG 4491 kb)

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Tsilimigras, D.I., Sahara, K., Paredes, A.Z. et al. Predicting Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma. J Gastrointest Surg 25, 1156–1163 (2021). https://doi.org/10.1007/s11605-020-04720-5

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  • DOI: https://doi.org/10.1007/s11605-020-04720-5

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