An explainable machine learning model for predicting the risk of distant metastasis in intrahepatic cholangiocarcinoma: a population-based cohort study

The GBM model demonstrated considerable potential in predicting the risk of DM in ICC patients. This could assist clinicians in formulating personalized treatment strategies, ultimately improving the overall prognosis of ICC patients.

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Publication Details

Journal
Discover Oncology
Published
2025-06-18
DOI
https://doi.org/10.1007/s12672-025-02952-y
Primary Topic
Cholangiocarcinoma and Gallbladder Cancer Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

An explainable machine learning model for predicting the risk of distant metastasis in intrahepatic cholangiocarcinoma: a population-based cohort study

Jinzhe Bi, Yaqun Yu
Discover Oncology
Cholangiocarcinoma and Gallbladder Cancer Studies
article

An explainable machine learning model for predicting the risk of distant metastasis in intrahepatic cholangiocarcinoma: a population-based cohort study

Jinzhe Bi, Yaqun Yu
article en

Abstract

The GBM model demonstrated considerable potential in predicting the risk of DM in ICC patients. This could assist clinicians in formulating personalized treatment strategies, ultimately improving the overall prognosis of ICC patients.

Discover OncologyVol. 16(1)
Guilin Medical University (CN)
Openalex Percentile: Top 76%
Cholangiocarcinoma and Gallbladder Cancer Studies
DOIOpen Access PDFOpenAlex
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An explainable machine learning model for predicting the risk of distant metastasis in intrahepatic cholangiocarcinoma: a population-based cohort study — Jinzhe Bi, Yaqun Yu · Discover Oncology (2025) | TGRS Research Map | TGRS