Predicting Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma.

Journal Article (Journal Article)


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).


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.


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 ( ). 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).


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

Full Text

Duke Authors

Cited Authors

  • Tsilimigras, DI; Sahara, K; Paredes, AZ; Moro, A; Mehta, R; Moris, D; Guglielmi, A; Aldrighetti, L; Weiss, M; Bauer, TW; Alexandrescu, S; Poultsides, GA; Maithel, SK; Marques, HP; Martel, G; Pulitano, C; Shen, F; Soubrane, O; Koerkamp, BG; Endo, I; Pawlik, TM

Published Date

  • May 1, 2021

Published In

Volume / Issue

  • 25 / 5

Start / End Page

  • 1156 - 1163

PubMed ID

  • 32757124

Electronic International Standard Serial Number (EISSN)

  • 1873-4626

International Standard Serial Number (ISSN)

  • 1091-255X

Digital Object Identifier (DOI)

  • 10.1007/s11605-020-04720-5


  • eng