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

Digital Health Reviews (1829) Gastroenterology and Hepatology (145) Medical imaging (133) Cancer Prognosis Models and Machine Learning (80) Computer-Aided Diagnosis (278) Decision Support for Health Professionals (1640) Machine Learning (2234) Artificial Intelligence (2354) Digital Health Reviews (1829)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72420, first published .
Effectiveness of Radiomics-Based Machine Learning Models in Differentiating Pancreatitis and Pancreatic Ductal Adenocarcinoma: Systematic Review and Meta-Analysis

Effectiveness of Radiomics-Based Machine Learning Models in Differentiating Pancreatitis and Pancreatic Ductal Adenocarcinoma: Systematic Review and Meta-Analysis

Effectiveness of Radiomics-Based Machine Learning Models in Differentiating Pancreatitis and Pancreatic Ductal Adenocarcinoma: Systematic Review and Meta-Analysis

Authors of this article:

Lechang Zhang1 Author Orcid Image ;   Dewei Li2 Author Orcid Image ;   Tong Su1 Author Orcid Image ;   Tong Xiao1 Author Orcid Image ;   Shulei Zhao1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Almufareh M, Tehsin S, Humayun M, Kausar S, Farooq A, Aldossary H, Aljohani A. From radiomics to transformers in pancreatic cancer detection and prognosis. Frontiers in Medicine 2026;12 View
  2. Cong F, Tian K, Gao Q, Wang F, Sun P, Xu N. CT Radiomics–Based Machine Learning Model for Predicting Capsular and Neural Invasion in Thyroid Carcinoma: Diagnostic Accuracy Study. JMIR Medical Informatics 2026;14:e77349 View