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

Digital Health Reviews (1888) Digital Health Reviews (1888) Decision Support for Health Professionals (1718) Machine Learning (2332) Gastroenterology and Hepatology (153) Artificial Intelligence (2493) Computer-Aided Diagnosis (291) Cancer Prognosis Models and Machine Learning (92) Medical imaging (147)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72420, first published .
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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 (3) 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
  3. Wang Z, Qu W, Cai W, Wang C, Lyu C, Xie Q, Chu Q, Shen Y, Xiao P, Li F, Zhang Q, Li J, Lee J, Li Z. PancDS in Real‐World Practice: A Prospective Multicenter Validation of a Clinical Decision‐Support System Bridging Experience Gaps in Pancreatic Lesion Diagnosis. Advanced Science 2026 View