Radiomics for ultrafast dynamic contrast-enhanced breast MRI in the diagnosis of breast cancer: a pilot study

Author(s):  
Maryellen L. Giger ◽  
Karen Drukker ◽  
Rachel Anderson ◽  
Fred Pineda ◽  
Alexandra Edwards ◽  
...  
2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Natsuko Onishi ◽  
Meredith Sadinski ◽  
Mary C. Hughes ◽  
Eun Sook Ko ◽  
Peter Gibbs ◽  
...  

2019 ◽  
Vol 26 (10) ◽  
pp. 1358-1362
Author(s):  
Amie Y. Lee ◽  
Ryan Navarro ◽  
Lindsay P. Busby ◽  
Heather I. Greenwood ◽  
Matthew D. Bucknor ◽  
...  

2019 ◽  
Vol 51 (1) ◽  
pp. 164-174 ◽  
Author(s):  
Maya Honda ◽  
Masako Kataoka ◽  
Natsuko Onishi ◽  
Mami Iima ◽  
Akane Ohashi ◽  
...  

2019 ◽  
Vol 30 (2) ◽  
pp. 756-766 ◽  
Author(s):  
Natsuko Onishi ◽  
Meredith Sadinski ◽  
Peter Gibbs ◽  
Katherine M. Gallagher ◽  
Mary C. Hughes ◽  
...  

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jennifer Xiao ◽  
Habib Rahbar ◽  
Daniel S. Hippe ◽  
Mara H. Rendi ◽  
Elizabeth U. Parker ◽  
...  

AbstractAngiogenesis is a critical component of breast cancer development, and identification of imaging-based angiogenesis assays has prognostic and treatment implications. We evaluated the association of semi-quantitative kinetic and radiomic breast cancer features on dynamic contrast-enhanced (DCE)-MRI with microvessel density (MVD), a marker for angiogenesis. Invasive breast cancer kinetic features (initial peak percent enhancement [PE], signal enhancement ratio [SER], functional tumor volume [FTV], and washout fraction [WF]), radiomics features (108 total features reflecting tumor morphology, signal intensity, and texture), and MVD (by histologic CD31 immunostaining) were measured in 27 patients (1/2016–7/2017). Lesions with high MVD levels demonstrated higher peak SER than lesions with low MVD (mean: 1.94 vs. 1.61, area under the receiver operating characteristic curve [AUC] = 0.79, p = 0.009) and higher WF (mean: 50.6% vs. 22.5%, AUC = 0.87, p = 0.001). Several radiomics texture features were also promising for predicting increased MVD (maximum AUC = 0.84, p = 0.002). Our study suggests DCE-MRI can non-invasively assess breast cancer angiogenesis, which could stratify biology and optimize treatments.


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