Accessibility settings

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

Reviews in Medical Informatics (123) Machine Learning (2229) Colon and Rectal Cancer (53) Imaging Informatics (331) Clinical Information and Decision Making (2070) Decision Support for Health Professionals (1637) Secondary Use of Clinical Data for Research and Surveillance (493)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78644, first published .
Predictive Performance of Radiomics-Based Machine Learning for Colorectal Cancer Recurrence Risk: Systematic Review and Meta-Analysis

Predictive Performance of Radiomics-Based Machine Learning for Colorectal Cancer Recurrence Risk: Systematic Review and Meta-Analysis

Predictive Performance of Radiomics-Based Machine Learning for Colorectal Cancer Recurrence Risk: Systematic Review and Meta-Analysis

Authors of this article:

Yuan Sun1, 2 Author Orcid Image ;   Bo Li3 Author Orcid Image ;   Chuanlan Ju4 Author Orcid Image ;   Liming Hu5 Author Orcid Image ;   Huiyi Sun6 Author Orcid Image ;   Jing An7 Author Orcid Image ;   Tae-Hun Kim8 Author Orcid Image ;   Zhijun Bu1 Author Orcid Image ;   Zeyang Shi1 Author Orcid Image ;   Jianping Liu1 Author Orcid Image ;   Zhaolan Liu1 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Elengickal D, Nizich M, Toma M. Stratified Procedural Risk Assessment in Colorectal Surgery: A Comparative Analysis of Statistical and Machine Learning Approaches Using Combined Surgical Approach and Operative Duration Categories. Surgeries 2026;7(2):42 View