A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer.

Journal: PloS one

PMID: 40132000

Abstract

BACKGROUND: The unique anatomical characteristics and blood supply of the rectosigmoid junction confer particular significance to its physiological functions and clinical surgeries. However, research on the prognosis of rectosigmoid junction cancer (RSC) is scarce, and reliable clinical prediction models are lacking.

Authors

Yifei Wang

Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Bingbing Chen

Department of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, China.
Jinhai Yu

School of Optometry, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.

Keywords

Aged Female Humans Machine Learning Male Middle Aged Prognosis Proportional Hazards Models Rectal Neoplasms Retrospective Studies Risk Factors Sigmoid Neoplasms

External Resources

View on PubMed Access via DOI PubMed (40132000)

A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals