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.

scholarly journals Differentiation of breast tuberculosis and breast cancer using diffusion-weighted, T2-weighted and dynamic contrast-enhanced magnetic resonance imaging

2018 ◽  
Vol 22 (2) ◽  
Author(s):  
Dibuseng P. Ramaema ◽  
Richard J. Hift
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Adc Value ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Diffusion Weighted ◽  
Breast Tuberculosis ◽  
Contrast Enhanced

Background: The use of multi-parametric magnetic resonance imaging (MRI) in the evaluation of breast tuberculosis (BTB).Objectives: To evaluate the value of diffusion-weighted imaging (DWI), T2-weighted (T2W) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating breast cancer (BCA) from BTB.Method: We retrospectively studied images of 17 patients with BCA who had undergone preoperative MRI and 6 patients with pathologically proven BTB who underwent DCE-MRI during January 2014 to January 2015.Results: All patients were female, with the age range of BTB patients being 23–43 years and the BCA patients being 31–74 years. Breast cancer patients had a statistically significant lower mean apparent diffusion coefficient (ADC) value (1072.10 +/- 365.14), compared to the BTB group (1690.77 +/- 624.05, p = 0.006). The mean T2-weighted signal intensity (T2SI) was lower for the BCA group (521.56 +/- 233.73) than the BTB group (787.74 +/- 196.04, p = 0.020). An ADC mean cut-off value of 1558.79 yielded 66% sensitivity and 94% specificity, whilst the T2SI cut-off value of 790.20 yielded 83% sensitivity and 83% specificity for differentiating between BTB and BCA. The homogeneous internal enhancement for focal mass was seen in BCA patients only.Conclusion: Multi-parametric MRI incorporating the DWI, T2W and DCE-MRI may be a useful tool to differentiate BCA from BTB.

scholarly journals Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Yu Ji ◽  
Hui Li ◽  
Alexandra V. Edwards ◽  
John Papaioannou ◽  
Wenjuan Ma ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Magnetic Resonance ◽  
Breast Lesions ◽  
Resonance Imaging ◽  
Single Institution ◽  
Dce Mri ◽  
Benign Breast Lesions ◽  
Mass Lesions

Abstract Background As artificial intelligence methods for the diagnosis of disease advance, we aimed to evaluate machine learning in the predictive task of distinguishing between malignant and benign breast lesions on an independent clinical magnetic resonance imaging (MRI) dataset within a single institution for subsequent use as a computer aid for radiologists. Methods Computer analysis was conducted on consecutive dynamic contrast-enhanced MRI (DCE-MRI) studies from 1483 breast cancer and 496 benign patients who underwent MRI examinations between February 2015 and October 2017; with the age ranges of the cancer and benign patients being 19 to 77 and 16 to 76 years old, respectively. Cases were separated into a training dataset (years 2015 & 2016; 1444 cases) and an independent testing dataset (year 2017; 535 cases) based solely on MRI examination date. After radiologist indication of the lesion, the computer automatically segmented and extracted radiomic features, which were subsequently merged with a support-vector machine (SVM) to yield a lesion signature. Area under the receiving operating characteristic (ROC) curve (AUC) with 95% confidence intervals (CI) served as the primary figure of merit in the statistical evaluation for this clinical classification task. Results In the task of distinguishing malignant and benign breast lesions DCE-MRI, the trained predictive model yielded an AUC value of 0.89 (95% CI: 0.858, 0.922) on the independent image set. AUC values of 0.88 (95% CI: 0.845, 0.926) and 0.90 (95% CI: 0.837, 0.940) were obtained for mass lesions only and non-mass lesions only, respectively. Compared with actual clinical management decisions, the predictive model achieved 99.5% sensitivity with 9.6% fewer recommended biopsies. Conclusion On an independent, consecutive clinical dataset within a single institution, a trained machine learning system yielded promising performance in distinguishing between malignant and benign breast lesions.

scholarly journals Non-invasive assessment of liver disease in rats using multiparametric magnetic resonance imaging: a feasibility study

Biology Open ◽  
2018 ◽  
Vol 7 (7) ◽  
pp. bio033910 ◽  
Author(s):  
Anna M. Hoy ◽  
Natasha McDonald ◽  
Ross J. Lennen ◽  
Matteo Milanesi ◽  
Amy H. Herlihy ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Liver Disease ◽  
Magnetic Resonance ◽  
Feasibility Study ◽  
Resonance Imaging ◽  
Non Invasive ◽  
Multiparametric Magnetic Resonance Imaging

scholarly journals Associations Between Dynamic Contrast Enhanced Magnetic Resonance Imaging and Clinically Relevant Histopathological Features in Breast Cancer. A Multicenter Analysis

2020 ◽  
Author(s):  
Alexey Surov ◽  
Maciej Pech ◽  
Jin You Kim ◽  
Marco Aiello ◽  
Wei Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Hormone Receptor ◽  
Hormone Receptor Status ◽  
Resonance Imaging ◽  
Ki 67 ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

Abstract Background: To provide evident data regarding relationships between quantitative dynamic contrast enhanced magnetic resonance imaging (DCE MRI) and prognostic factors in breast cancer (BC).Methods: Data from 4 centers (200 female patients, mean age, 51.2 ± 11.5 years) were acquired. The following data were collected: histopathological diagnosis, tumor grade, stage, hormone receptor status, KI 67, and DCE MRI values including Ktrans (volume transfer constant), Ve (volume of the extravascular extracellular leakage space (EES) and Kep (diffusion of contrast medium from the EES back to the plasma). DCE MRI values between different groups were compared using the Mann–Whitney U test and by the Kruskal-Wallis H test. The association between DCE MRI and Ki 67 values was calculated by Spearman’s rank correlation coefficient. Results: DCE MRI values of different tumor subtypes overlapped significantly. There were no statistically significant differences of DCE MRI values between different tumor grades. All DCE MRI parameters correlated with KI 67: Ktrans, r = 0.44, p=0.0001; Ve, r = 0.34, p=0.0001; Kep, r = 0.28, p=0.002. ROC analysis identified a Ktrans threshold of 0.3 min-1 for discrimination of tumors with low KI 67 expression (<25%) and high KI 67 expression (≥25%): sensitivity, 75.5%, specificity, 73.0%, accuracy, 74.0%, AUC, 0.78. DCE MRI values overlapped between tumors with different T and N stages.Conclusion: Ktrans, Kep, and Ve cannot be used as reliable a surrogate marker for hormone receptor status, tumor stage and grade in BC. Ktrans may discriminate lesions with high and lower proliferation activity.

scholarly journals Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging

2021 ◽  
Vol 1 ◽  
Author(s):  
David E. Frankhouser ◽  
Eric Dietze ◽  
Ashish Mahabal ◽  
Victoria L. Seewaldt
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Blood Vessels ◽  
Breast Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Breast Magnetic Resonance

Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.

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