Diamantis I Tsilimigras1, J Madison Hyer1, Anghela Z Paredes1, Adrian Diaz1, Dimitrios Moris1, Alfredo Guglielmi2, Luca Aldrighetti3, Matthew Weiss4, Todd W Bauer5, Sorin Alexandrescu6, George A Poultsides7, Shishir K Maithel8, Hugo P Marques9, Guillaume Martel10, Carlo Pulitano11, Feng Shen12, Olivier Soubrane13, Bas Groot Koerkamp14, Itaru Endo15, Timothy M Pawlik16,17. 1. Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA. 2. Department of Surgery, University of Verona, Verona, Italy. 3. Department of Surgery, Ospedale San Raffaele, Milan, Italy. 4. Department of Surgery, Johns Hopkins Hospital, Baltimore, MD, USA. 5. Department of Surgery, University of Virginia, Charlottesville, VA, USA. 6. Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania. 7. Department of Surgery, Stanford University, Stanford, CA, USA. 8. Department of Surgery, Emory University, Atlanta, GA, USA. 9. Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal. 10. Department of Surgery, University of Ottawa, Ottawa, Canada. 11. Department of Surgery, Royal Prince Alfred Hospital, University of Sydney, Sydney, NSW, Australia. 12. Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China. 13. Department of Hepatobiliopancreatic Surgery and Liver Transplantation, AP-HP, Beaujon Hospital, Clichy, France. 14. Department of Surgery, Erasmus University Medical Centre, Rotterdam, The Netherlands. 15. Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan. 16. Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA. tim.pawlik@osumc.edu. 17. Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, The Ohio State University Wexner Medical Center, Columbus, OH, USA. tim.pawlik@osumc.edu.
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.
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.
Authors: Woo Jin Choi; Phil J Williams; Marco P A W Claasen; Tommy Ivanics; Marina Englesakis; Steven Gallinger; Bettina Hansen; Gonzalo Sapisochin Journal: Ann Surg Oncol Date: 2022-02-18 Impact factor: 5.344