Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients
Abstract Background Heterogeneity is a common finding within tumours. We evaluated the imaging features of tumours based on the decomposition of tumoural dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data to identify their prognostic value for breast cancer survival and to explore t...
Main Authors: | Ming Fan, Pingping Xia, Bin Liu, Lin Zhang, Yue Wang, Xin Gao, Lihua Li |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2019-10-01
|
Series: | Breast Cancer Research |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s13058-019-1199-8 |
Similar Items
-
Use of Bayesian Mixture Models in Analyzing Heterogeneous Survival Data: A Simulation Study
by: Naser Ahmadi, et al.
Published: (2020-01-01) -
Evaluation of prognostic factors affecting long and short term survival rates of Hodgkin\'s lymphoma patients using the cure fraction models
by: Amir Elhaei, et al.
Published: (2019-04-01) -
Evaluation of prognostic factors affecting long and short term survival rates of Hodgkin's lymphoma patients using the cure fraction models
by: Elhaei A, et al.
Published: (2019-04-01) -
Semiparametric Bayesian Kernel Survival Model for Highly Correlated High-Dimensional Data.
by: Zhang, Lin
Published: (2019) -
Effect of CyberKnife radiosurgery on survival rate of patients with recurrent liver cancer after surgery
by: LI Huan
Published: (2017-12-01)