ASO Author Reflections: Use of Machine Learning to Identify Patients with Intrahepatic Cholangiocarcinoma Who Could Benefit More from Neoadjuvant Therapies

  • Diamantis I. Tsilimigras
  • Rittal Mehta
  • Timothy M. PawlikEmail author
ASO Author Reflections



The authors have no conflicts of interest to disclose.


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    Endo I, Gonen M, Yopp AC, et al. Intrahepatic cholangiocarcinoma: rising frequency, improved survival, and determinants of outcome after resection. Ann Surg. 2008;248(1):84–96.CrossRefGoogle Scholar
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    Wu L, Tsilimigras DI, Paredes AZ, et al. Trends in the incidence, treatment and outcomes of patients with intrahepatic cholangiocarcinoma in the USA: facility type is associated with margin status, use of lymphadenectomy and overall survival. World J Surg. 2019;43(7):1777–1787.CrossRefGoogle Scholar
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    Yadav S, Xie H, Bin-Riaz I, et al. Neoadjuvant vs. adjuvant chemotherapy for cholangiocarcinoma: a propensity score matched analysis. Eur J Surg Oncol. 2019;45(8):1432–1438.CrossRefGoogle Scholar
  4. 4.
    Tsilimigras DI, Mehta R, Moris D, et al. A machine-based approach to preoperatively identify patients with the most and least benefit associated with resection for intrahepatic cholangiocarcinoma: an international multi-institutional analysis of 1,146 patients. Ann Surg Oncol. 2019. Scholar

Copyright information

© Society of Surgical Oncology 2019

Authors and Affiliations

  1. 1.Department of Surgery, Division of Surgical Oncology, Wexner Medical Center and James Comprehensive Cancer CenterThe Ohio State UniversityColumbusUSA

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