Machine learning radiomics signature on magnetic resonance imaging associated with phenotypes and disease-free survival in patients with breast cancer (RBC-01): A registry-based, multicenter cohort study.

Herui Yao,Yunfang Yu,Yujie Tan,Qiu-gen Hu,Jie Ouyang,Yongjian Chen,Yang Gu,Anlin Li,Zifan He,Yaping Yang,Kai Chen,Jiafan Ma,Chenchen Li,Mudi Ma,Xiaohong Li,Haitao Zhong,Qiyun Ou,Zhuo Wu,Fengxi Su,Erwei Song

Machine learning radiomics signature on magnetic resonance imaging associated with phenotypes and disease-free survival in patients with breast cancer (RBC-01): A registry-based, multicenter cohort study.

2020

Herui Yao
Yunfang Yu
Yujie Tan
Qiu-gen Hu
Jie Ouyang
Yongjian Chen
Yang Gu
Anlin Li
Zifan He
Yaping Yang
Kai Chen
Jiafan Ma
Chenchen Li
Mudi Ma
Xiaohong Li
Haitao Zhong
Qiyun Ou
Zhuo Wu
Fengxi Su
Erwei Song

3563Background: The early stage breast cancer patients can vary in disease-free survival (DFS), innovative predictors evaluate the prognostic capacity are urgently needed. We aimed to develop and i...

Keywords:

Magnetic resonance imaging
Internal medicine
Oncology
disease free survival
Phenotype
Breast cancer
Radiomics
Pathology
in patient
Medicine
Cohort study

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