Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer

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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Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers

European Radiology ◽

10.1007/s00330-020-07274-x ◽

2020 ◽

Cited By ~ 1

Author(s):

Yang Zhang ◽

Jeon-Hor Chen ◽

Yezhi Lin ◽

Siwa Chan ◽

Jiejie Zhou ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Molecular Subtypes ◽

Dce Mri

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A deep matrix factorization framework for identifying underlying tissue-specific patterns of DCE-MRI: applications for molecular subtype classification in breast cancer

Physics in Medicine and Biology ◽

10.1088/1361-6560/ac3a25 ◽

2021 ◽

Author(s):

Ming Fan ◽

Wei Yuan ◽

Weifen Liu ◽

Xin Gao ◽

Maosheng Xu ◽

...

Keyword(s):

Breast Cancer ◽

Matrix Factorization ◽

Molecular Subtypes ◽

Molecular Subtype ◽

Intratumor Heterogeneity ◽

Luminal A ◽

Tissue Specific ◽

Dce Mri ◽

Dynamic Patterns ◽

Better Than

Abstract Objective Breast cancer is heterogeneous in that different angiogenesis and blood flow characteristics could be present within a tumor. The pixel kinetics of DCE-MRI can assume several distinct signal patterns related to specific tissue characteristics. Identification of the latent, tissue-specific dynamic patterns of intratumor heterogeneity can shed light on the biological mechanisms underlying the heterogeneity of tumors. Approach To mine this information, we propose a deep matrix factorization-based dynamic decomposition (DMFDE) model specifically designed according to DCE-MRI characteristics. The time-series imaging data were decomposed into tissue-specific dynamic patterns and their corresponding proportion maps. The image pixel matrix and the reference matrix of population-level kinetics obtained by clustering the dynamic signals were used as the inputs. Two multilayer neural network branches were designed to collaboratively project the input matrix into a latent dynamic pattern and a dynamic proportion matrix, which was justified using simulated data. Clinical implications of DMFDE were assessed by radiomics analysis of proportion maps obtained from the tumor/parenchyma region for classifying the luminal A subtype. Main results The decomposition performance of DMFDE was evaluated by the root mean square error (RMSE) and was shown to be better than that of the conventional convex analysis of mixtures (CAM) method. The predictive model with K=3, 4, and 5 dynamic proportion maps generated AUC values of 0.780, 0.786 and 0.790, respectively, in distinguishing between luminal A and nonluminal A tumors, which are better than the CAM method (AUC=0.726). The combination of statistical features from images with different proportion maps has the highest prediction value (AUC= 0.813), which is significantly higher than that based on CAM. Conclusion This proposed method identified the latent dynamic patterns associated with different molecular subtypes, and radiomics analysis based on the pixel compositions of the uncovered dynamic patterns was able to determine molecular subtypes of breast cancer.

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Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization

Cancers ◽

10.3390/cancers12123763 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3763

Author(s):

R. Elena Ochoa-Albiztegui ◽

Varadan Sevilimedu ◽

Joao V. Horvat ◽

Sunitha B. Thakur ◽

Thomas H. Helbich ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Diagnosis ◽

Molecular Subtypes ◽

Breast Cancer Diagnosis ◽

Breast Tumors ◽

Resonance Imaging ◽

Dce Mri ◽

Dynamic Contrast Enhanced ◽

Contrast Enhanced ◽

Malignant Breast

The purpose of this study was to investigate whether ultra-high-field dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast at 7T using quantitative pharmacokinetic (PK) analysis can differentiate between benign and malignant breast tumors for improved breast cancer diagnosis and to predict molecular subtypes, histologic grade, and proliferation rate in breast cancer. In this prospective study, 37 patients with 43 lesions suspicious on mammography or ultrasound underwent bilateral DCE-MRI of the breast at 7T. PK parameters (KTrans, kep, Ve) were evaluated with two region of interest (ROI) approaches (2D whole-tumor ROI or 2D 10 mm standardized ROI) manually drawn by two readers (senior reader, R1, and R2) independently. Histopathology served as the reference standard. PK parameters differentiated benign and malignant lesions (n = 16, 27, respectively) with good accuracy (AUCs = 0.655–0.762). The addition of quantitative PK analysis to subjective BI-RADS classification improved breast cancer detection from 88.4% to 97.7% for R1 and 86.04% to 97.67% for R2. Different ROI approaches did not influence diagnostic accuracy for both readers. Except for KTrans for whole-tumor ROI for R2, none of the PK parameters were valuable to predict molecular subtypes, histologic grade, or proliferation rate in breast cancer. In conclusion, PK-enhanced BI-RADS is promising for the noninvasive differentiation of benign and malignant breast tumors.

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Margin selection for breast-conserving surgery in patients with different approximate molecular subtypes of breast cancer

Academic Journal of Second Military Medical University ◽

10.3724/sp.j.1008.2010.00853 ◽

2010 ◽

Vol 30 (8) ◽

pp. 853-856 ◽

Cited By ~ 1

Author(s):

Dan FENG ◽

Meng LIU ◽

Liang-zhe WANG ◽

Qing-ping CAI

Keyword(s):

Breast Cancer ◽

Molecular Subtypes ◽

Breast Conserving Surgery ◽

Selection For

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CHANGES IN THE NIPPLE-AREOLA COMPLEX IN PATIENTS WITH BREAST CANCER

Problems in oncology ◽

10.37469/0507-3758-2017-63-4-593-597 ◽

2017 ◽

Vol 63 (4) ◽

pp. 593-597

Author(s):

Aziz Zikiryakhodzhaev ◽

Nadezhda Volchenko ◽

Erik Saribekyan ◽

Yelena Rasskazova

Keyword(s):

Breast Cancer ◽

Surgical Treatment ◽

Cancer Patients ◽

Molecular Subtypes ◽

Breast Cancer Patients ◽

Nipple Areola Complex ◽

Histological Types ◽

Areola Complex

The article presents data about the lesion of the nipple-areola complex in breast cancer. In 2015-2016 surgical treatment was performed in 101 breast cancer patients, different in size but with the mandatory removal of the nipple-areola complex. There are analyzed the dependence of the lesion of the nipple-areola complex from histological types of breast cancer, molecular subtypes, multicentricity, the location of tumor in the breast. The most significant criterion was the dependence of the lesion of the nipple-areola complex from the distance between tumor node and the nipple.

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