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

Machine Learning (2130) Clinical Information and Decision Making (1957) Artificial Intelligence (2198) Secondary Use of Clinical Data for Research and Surveillance (480) Innovations and Technology in Cancer Care (544) Tools, Programs and Algorithms (387) Decision Support for Health Professionals (1569) Cancer Prognosis Models and Machine Learning (72)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/73765, first published .
Predicting Lymph Node Metastasis in Rectal Cancer: Development and Validation of a Machine Learning Model Using Clinical Data

Predicting Lymph Node Metastasis in Rectal Cancer: Development and Validation of a Machine Learning Model Using Clinical Data

Predicting Lymph Node Metastasis in Rectal Cancer: Development and Validation of a Machine Learning Model Using Clinical Data

Authors of this article:

Wei Hou1, 2, 3 Author Orcid Image ;   Chuangwei Li1, 2, 3 Author Orcid Image ;   Zhen Wang1, 2, 3 Author Orcid Image ;   Wanqin Wang2, 4 Author Orcid Image ;   Shouhong Wan2, 5 Author Orcid Image ;   Bingbing Zou1, 2, 3 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Zhu C, An J, Zhou L. Neutrophil Percentage–to-Albumin Ratio as a Novel Prognostic Biomarker in Adult Diffuse Gliomas: Retrospective Study Integrating 3 Machine Learning Models and Cox Regression. JMIR Medical Informatics 2026;14:e79945 View