This paper is in the following e-collection/theme issue:

Digital Health Reviews (1801) Machine Learning (2199) Imaging Informatics (322) Innovations and Technology for Cancer Prevention and Screening (210) Clinical Information and Decision Making (2037) Artificial Intelligence (2297)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69906, first published .
The Application Status of Radiomics-Based Machine Learning in Intrahepatic Cholangiocarcinoma: Systematic Review and Meta-Analysis

The Application Status of Radiomics-Based Machine Learning in Intrahepatic Cholangiocarcinoma: Systematic Review and Meta-Analysis

The Application Status of Radiomics-Based Machine Learning in Intrahepatic Cholangiocarcinoma: Systematic Review and Meta-Analysis

Authors of this article:

Lan Xu1 Author Orcid Image ;   Zian Chen1 Author Orcid Image ;   Dan Zhu2 Author Orcid Image ;   Yingjun Wang3 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Madadi F, Kohzadi Z, Rahmatizadeh S, Sabouri A, Dabbagh A. Artificial Intelligence-Driven Image and Data Analytics in Anesthesia. Anesthesiology Clinics 2025;43(3):e1 View
  2. Chen J, Xi J, Chen T, Yang L, Liu K, Ding X. Diagnostic Performance of Computed Tomography–Based Artificial Intelligence for Early Recurrence of Cholangiocarcinoma: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2025;27:e78306 View
  3. Chen X, Yu W, Lin S. Integrating B-mode ultrasound radiomics and clinical variables to predict 26-week progression-free survival in advanced hepatocellular carcinoma under targeted immunotherapy. Abdominal Radiology 2026 View