Paper Info
Title
Lymph-vascular space invasion prediction in cervical cancer: Exploring radiomics and deep learning multilevel features of tumor and peritumor tissue on multiparametric MRI.
Abstract
•A radiomics and deep learning fusion model using multiparametric MRI was built for LVSI prediction in early-stage cervical cancer.•Different from previous studies with focus on tumor region, the proposed method takes both tumor tissues and peri-tumor tissues with different radial dilation distances outside tumor in consideration for the prediction.•We demonstrated that the peritumoral tissue contains remarkable information about the development process of LVSI in early-stage cervical cancer.
Year
DOI
Venue
2020
10.1016/j.bspc.2020.101869
Biomedical Signal Processing and Control
Keywords
Field
DocType
Cervical cancer,Radiomics,Deep learning,Lymph-vascular space invasion,Magnetic resonance imaging
Cervical cancer,Surgical planning,Multiparametric Magnetic Resonance Imaging,Pattern recognition,Artificial intelligence,Radiology,Deep learning,Confidence interval,Cohort,Discriminative model,Mathematics,Feature learning
Journal
Volume
ISSN
Citations 
58
1746-8094
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Wenqing Hua100.34
Taohui Xiao2122.16
Xiran Jiang300.34
Zaiyi Liu400.34
Meiyun Wang500.34
Hairong Zheng65628.24
Shanshan Wang7279.31