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Annals of Surgical Oncology
Published in: Annals of Surgical Oncology 13/2020

01-12-2020 | Cholangiocarcinoma | Hepatobiliary Tumors

A Novel Classification of Intrahepatic Cholangiocarcinoma Phenotypes Using Machine Learning Techniques: An International Multi-Institutional Analysis

Authors: Diamantis I. Tsilimigras, MD, J. Madison Hyer, MS, Anghela Z. Paredes, MD, MS, Adrian Diaz, MD, MPH, Dimitrios Moris, MD, PhD, Alfredo Guglielmi, MD, Luca Aldrighetti, MD, Matthew Weiss, MD, Todd W. Bauer, MD, Sorin Alexandrescu, MD, George A. Poultsides, MD, Shishir K. Maithel, MD, Hugo P. Marques, MD, Guillaume Martel, MD, Carlo Pulitano, MD, Feng Shen, MD, Olivier Soubrane, MD, Bas Groot Koerkamp, MD, Itaru Endo, MD, PhD, Timothy M. Pawlik, MD, MPH, PhD, FACS

Published in: Annals of Surgical Oncology | Issue 13/2020

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Abstract

Introduction

Patients with intrahepatic cholangiocarcinoma (ICC) generally have a poor prognosis, yet there can be heterogeneity in the patterns of presentation and associated outcomes. We sought to identify clusters of ICC patients based on preoperative characteristics that may have distinct outcomes based on differing patterns of presentation.

Methods

Patients undergoing curative-intent resection of ICC between 2000 and 2017 were identified using a multi-institutional database. A cluster analysis was performed based on preoperative variables to identify distinct patterns of presentation. A classification tree was built to prospectively assign patients into cluster assignments.

Results

Among 826 patients with ICC, three distinct presentation patterns were noted. Specifically, Cluster 1 (common ICC, 58.9%) consisted of individuals who had a small-size ICC (median 4.6 cm) and median carbohydrate antigen (CA) 19-9 and neutrophil-to-lymphocyte ratio (NLR) levels of 40.3 UI/mL and 2.6, respectively; Cluster 2 (proliferative ICC, 34.9%) consisted of patients who had larger-size tumors (median 9.0 cm), higher CA19-9 levels (median 72.0 UI/mL), and similar NLR (median 2.7); Cluster 3 (inflammatory ICC, 6.2%) comprised of patients with a medium-size ICC (median 6.2 cm), the lowest range of CA19-9 (median 26.2 UI/mL), yet the highest NLR (median 13.5) (all p < 0.05). Median OS worsened incrementally among the three different clusters {Cluster 1 vs. 2 vs. 3; 60.4 months (95% confidence interval [CI] 43.0–77.8) vs. 27.2 months (95% CI 19.9–34.4) vs. 13.3 months (95% CI 7.2–19.3); p < 0.001}. The classification tree used to assign patients into different clusters had an excellent agreement with actual cluster assignment (κ = 0.93, 95% CI 0.90–0.96).

Conclusion

Machine learning analysis identified three distinct prognostic clusters based solely on preoperative characteristics among patients with ICC. Characterizing preoperative patient heterogeneity with machine learning tools can help physicians with preoperative selection and risk stratification of patients with ICC.
Appendix
Available only for authorised users
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Metadata
Title
A Novel Classification of Intrahepatic Cholangiocarcinoma Phenotypes Using Machine Learning Techniques: An International Multi-Institutional Analysis
Authors
Diamantis I. Tsilimigras, MD
J. Madison Hyer, MS
Anghela Z. Paredes, MD, MS
Adrian Diaz, MD, MPH
Dimitrios Moris, MD, PhD
Alfredo Guglielmi, MD
Luca Aldrighetti, MD
Matthew Weiss, MD
Todd W. Bauer, MD
Sorin Alexandrescu, MD
George A. Poultsides, MD
Shishir K. Maithel, MD
Hugo P. Marques, MD
Guillaume Martel, MD
Carlo Pulitano, MD
Feng Shen, MD
Olivier Soubrane, MD
Bas Groot Koerkamp, MD
Itaru Endo, MD, PhD
Timothy M. Pawlik, MD, MPH, PhD, FACS
Publication date
01-12-2020
Publisher
Springer International Publishing
Published in
Annals of Surgical Oncology / Issue 13/2020
Print ISSN: 1068-9265
Electronic ISSN: 1534-4681
DOI
https://doi.org/10.1245/s10434-020-08696-z

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