Magnetic Resonance Arterial Spin Tagging for Non-Invasive Pharmacokinetic Analysis of Breast Cancer

1999 ◽  
Author(s):  
Michael Buonocore
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance ◽  
Pharmacokinetic Analysis ◽  
Non Invasive ◽  
Spin Tagging

217. Non-invasive nodal staging in breast cancer patient with gadofosveset-enhanced magnetic resonance imaging

2012 ◽  
Vol 38 (9) ◽  
pp. 804
Author(s):  
R. Schipper ◽  
M.B.I. Lobbes ◽  
L.M. van Roozendaal ◽  
C. Castro ◽  
B. de Vries ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Cancer Patient ◽  
Breast Cancer Patient ◽  
Nodal Staging ◽  
Resonance Imaging ◽  
Non Invasive

scholarly journals Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics

2020 ◽  
Vol 9 (6) ◽  
pp. 1853
Author(s):  
Doris Leithner ◽  
Marius E. Mayerhoefer ◽  
Danny F. Martinez ◽  
Maxine S. Jochelson ◽  
Elizabeth A. Morris ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Molecular Subtypes ◽  
Resonance Imaging ◽  
Luminal A ◽  
Linear Discriminant ◽  
Dce Mri ◽  
Non Invasive ◽  
Multiparametric Magnetic Resonance Imaging

We evaluated the performance of radiomics and artificial intelligence (AI) from multiparametric magnetic resonance imaging (MRI) for the assessment of breast cancer molecular subtypes. Ninety-one breast cancer patients who underwent 3T dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping were included retrospectively. Radiomic features were extracted from manually drawn regions of interest (n = 704 features per lesion) on initial DCE-MRI and ADC maps. The ten best features for subtype separation were selected using probability of error and average correlation coefficients. For pairwise comparisons with >20 patients in each group, a multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used (70% of cases for training, 30%, for validation, five times each). For all other separations, linear discriminant analysis (LDA) and leave-one-out cross-validation were applied. Histopathology served as the reference standard. MLP-ANN yielded an overall median area under the receiver-operating-characteristic curve (AUC) of 0.86 (0.77–0.92) for the separation of triple negative (TN) from other cancers. The separation of luminal A and TN cancers yielded an overall median AUC of 0.8 (0.75–0.83). Radiomics and AI from multiparametric MRI may aid in the non-invasive differentiation of TN and luminal A breast cancers from other subtypes.

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