The optimal number of examined lymph nodes for cancer specific death of intrahepatic cholangiocarcinoma: a population-based study

Increasing ELN count indicated better CSS. Our results strongly suggested 6 ELNs as the optimal cut-off number for assessing the standard of lymph node inspection and prognostic classification in ICC.

Authors

Institutions

Publication Details

Journal
Discover Oncology
Published
2025-04-16
DOI
https://doi.org/10.1007/s12672-025-02322-8
Primary Topic
Cholangiocarcinoma and Gallbladder Cancer Studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The optimal number of examined lymph nodes for cancer specific death of intrahepatic cholangiocarcinoma: a population-based study

You Tang, Zhiguo Tan, Li Ou, Xu Chen et al.
Discover Oncology
Cholangiocarcinoma and Gallbladder Cancer Studies
article

The optimal number of examined lymph nodes for cancer specific death of intrahepatic cholangiocarcinoma: a population-based study

You Tang, Zhiguo Tan, Li Ou, Xu Chen, Yufan Zhou, Chuang Peng, Chenzhang Ou
article en

Abstract

Increasing ELN count indicated better CSS. Our results strongly suggested 6 ELNs as the optimal cut-off number for assessing the standard of lymph node inspection and prognostic classification in ICC.

Discover OncologyVol. 16(1)
Hunan Normal University (CN), Hunan Provincial People's Hospital (CN), Lanzhou University (CN)
Natural Science Foundation of Hunan Province
Good health and well-being
Openalex Percentile: Top 86%
Cholangiocarcinoma and Gallbladder Cancer Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.