Prognostic prediction for inflammatory breast cancer patients using random survival forest modeling

Prognostic prediction for inflammatory breast cancer patients using random survival forest modeling

Background: Inflammatory breast cancer (IBC) is an aggressive and rare phenotype of breast cancer, which has a poor prognosis. Thus, it is necessary to establish a novel predictive model of high accuracy for the prognosis of IBC patients. Methods: Clinical information of 1,230 IBC patients from 2010...

Full description

Saved in:
Bibliographic Details
Main Authors: Yiwei Jia, Chaofan Li, Cong Feng, Shiyu Sun, Yifan Cai, Peizhuo Yao, Xinyu Wei, Zeyao Feng, Yanbin Liu, Wei Lv, Huizi Wu, Fei Wu, Lu Zhang, Shuqun Zhang, Xingcong Ma
Format: Article
Language:English
Published: Elsevier 2025-02-01
Series:Translational Oncology
Subjects:
Inflammatory breast cancer
Random survival forest
Machine learning
SEER
Online Access:http://www.sciencedirect.com/science/article/pii/S1936523324003723
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S1936523324003723

Similar Items