Sign in
MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays
Journal article   Open access  Peer reviewed

MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays

Hui Li, Yitan Zhu, Elizabeth S Burnside, Karen Drukker, Katherine A Hoadley, Cheng Fan, Suzanne D Conzen, Gary J Whitman, Elizabeth J Sutton, Jose M Net, …
Radiology, Vol.281(2), pp.382-391
2016-11
PMCID: PMC5069147
PMID: 27144536

Abstract

Predictive Value of Tests Gene Expression Risk Assessment Biomarkers, Tumor - analysis Image Enhancement Humans Middle Aged Image Interpretation, Computer-Assisted Magnetic Resonance Imaging - methods Neoplasm Recurrence, Local - pathology Phenotype Breast Neoplasms - genetics Breast Neoplasms - pathology Aged, 80 and over Adult Female Neoplasm Recurrence, Local - genetics Aged Retrospective Studies Genomics - methods
url
https://doi.org/10.1148/radiol.2016152110View
Published (Version of record) Open

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Highly Cited Paper 
Collaboration types
Domestic collaboration
Citation topics
1 Clinical & Life Sciences
1.119 Breast Cancer Scanning
1.119.583 Mammography
Web Of Science research areas
Radiology, Nuclear Medicine & Medical Imaging
ESI research areas
Clinical Medicine

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#3 Good Health and Well-Being
#5 Gender Equality

Source: InCites

Details