Magnetic resonance imaging findings of invasive breast cancer in different histological grades and different histopathological types

2021 ◽  
Vol 76 ◽  
pp. 98-103
Author(s):  
Gamze Durhan ◽  
Ahmet Poker ◽  
Emil Settarzade ◽  
Jale Karakaya ◽  
Kemal Kösemehmetoğlu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Invasive Breast Cancer ◽  
Imaging Findings ◽  
Resonance Imaging

Magnetic resonance imaging characteristics of invasive breast cancer in women aged less than 35 years

2015 ◽  
Vol 56 (8) ◽  
pp. 924-932 ◽  
Author(s):  
Jin You Kim ◽  
Suck Hong Lee ◽  
Ji Won Lee ◽  
Suk Kim ◽  
Ki Seok Choo
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Invasive Breast Cancer ◽  
Resonance Imaging ◽  
Imaging Characteristics

scholarly journals Machine learning magnetic resonance imaging radiomics predicts axillary lymph node metastasis in invasive breast cancer

2020 ◽  
Author(s):  
Yunfang Yu ◽  
Zifan He ◽  
Jie Ouyang ◽  
Yujie Tan ◽  
Yong-Jian Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Lymph Node ◽  
Clinical Practice ◽  
Magnetic Resonance ◽  
Invasive Breast Cancer ◽  
Axillary Lymph Node ◽  
Axillary Lymph ◽  
Resonance Imaging

Abstract In current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer is based on the invasive procedure and many patients will suffer from operative associated complications. Hence, a novel signature incorporated tumor and lymph node magnetic resonance imaging (MRI) radiomics, clinical and pathological characteristics, and molecular subtypes based on the machine learning approach was established to accurately identify ALN metastasis in early-stage invasive breast cancer patients. Although the misjudgment of ALN status by clinicians according to preoperative MRI are common during clinical practice and even the senior radiologists make mistakes sometimes, this multiomic radiomic signature showed the superiority over clinicians and could precisely discriminate ALN metastasis among different molecular subtype patients. Furthermore, the association between MRI radiomic features and tumor-microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites were found, which revealed the potential biological underpinning of MRI radiomics.

scholarly journals Magnetic resonance imaging of invasive breast cancer

2005 ◽  
Vol 9 (3) ◽  
pp. 4
Author(s):  
P Corr ◽  
S Panday ◽  
P Seolall ◽  
H Booth
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Invasive Breast Cancer ◽  
Resonance Imaging
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