Online citations, reference lists, and bibliographies.
← Back to Search

Radiogenomic Analysis Of Breast Cancer: Luminal B Molecular Subtype Is Associated With Enhancement Dynamics At MR Imaging.

M. Mazurowski, J. Zhang, L. Grimm, S. Yoon, James I. Silber
Published 2014 · Medicine

Cite This
Download PDF
Analyze on Scholarcy
PURPOSE To investigate associations between breast cancer molecular subtype and semiautomatically extracted magnetic resonance (MR) imaging features. MATERIALS AND METHODS Imaging and genomic data from the Cancer Genome Atlas and the Cancer Imaging Archive for 48 patients with breast cancer from four institutions in the United States were used in this institutional review board approval-exempt study. Computer vision algorithms were applied to extract 23 imaging features from lesions indicated by a breast radiologist on MR images. Morphologic, textural, and dynamic features were extracted. Molecular subtype was determined on the basis of genomic analysis. Associations between the imaging features and molecular subtype were evaluated by using logistic regression and likelihood ratio tests. The analysis controlled for the age of the patients, their menopausal status, and the orientation of the MR images (sagittal vs axial). RESULTS There is an association (P = .0015) between the luminal B subtype and a dynamic contrast material-enhancement feature that quantifies the relationship between lesion enhancement and background parenchymal enhancement. Cancers with a higher ratio of lesion enhancement rate to background parenchymal enhancement rate are more likely to be luminal B subtype. CONCLUSION The luminal B subtype of breast cancer is associated with MR imaging features that relate the enhancement dynamics of the tumor and the background parenchyma.
This paper references
Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers.
N. Bhooshan (2010)
Feature Selection in Computer-Aided Breast Cancer Diagnosis via Dynamic Contrast-Enhanced Magnetic Resonance Images
Megan Rakoczy (2012)
Breast cancer classification by proteomic technologies: current state of knowledge.
S. W. Lam (2014)
Gene expression profiling identifies molecular subtypes of inflammatory breast cancer.
F. Bertucci (2005)
Breast cancer subtype approximated by estrogen receptor, progesterone receptor, and HER-2 is associated with local and distant recurrence after breast-conserving therapy.
P. Nguyen (2008)
Breast Cancer Molecular Subtypes Respond Differently to Preoperative Chemotherapy
R. Rouzier (2005)
Estrogen receptor, progesterone receptor, HER-2, and response to postmastectomy radiotherapy in high-risk breast cancer: the Danish Breast Cancer Cooperative Group.
M. Kyndi (2008)
The Triple Negative Paradox: Primary Tumor Chemosensitivity of Breast Cancer Subtypes
L. Carey (2007)
Predictive value of breast cancer molecular subtypes in Chinese patients with four or more positive nodes after postmastectomy radiotherapy.
S. Wu (2012)
Analysis of the pathologic response to primary chemotherapy in patients with locally advanced breast cancer grouped according to estrogen receptor, progesterone receptor, and HER2 status.
L. Fernández-Morales (2007)
Prognostic value of pathologic complete response after primary chemotherapy in relation to hormone receptor status and other factors.
V. Guarneri (2006)
Subtypes of breast cancer show preferential site of relapse.
M. Smid (2008)
Computer-aided diagnosis for dynamic contrast-enhanced breast MRI of mass-like lesions using a multiparametric model combining a selection of morphological, kinetic, and spatiotemporal features.
S. Agliozzo (2012)
Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape.
S. Yamamoto (2012)
Accuracy and interpretation time of computer-aided detection among novice and experienced breast MRI readers.
C. Lehman (2013)
Molecular subtype can predict the response and outcome of Chinese locally advanced breast cancer patients treated with preoperative therapy.
X. S. Chen (2010)
Molecular classification system identifies invasive breast carcinoma patients who are most likely and those who are least likely to achieve a complete pathologic response after neoadjuvant chemotherapy
N. Goldstein (2007)
Tumour molecular subtyping according to hormone receptors and HER2 status defines different pathological complete response to neoadjuvant chemotherapy in patients with locally advanced breast cancer
A. Sánchez-muñoz (2008)
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
J. Sled (1998)
A retrospective study of breast cancer subtypes: the risk of relapse and the relations with treatments
Y. Wang (2011)
The erbB2+ cluster of the intrinsic gene set predicts tumor response of breast cancer patients receiving neoadjuvant chemotherapy with docetaxel, doxorubicin and cyclophosphamide within the GEPARTRIO trial.
A. Rody (2007)
Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study.
L. Carey (2006)
Computerized assessment of breast lesion malignancy using DCE-MRI robustness study on two independent clinical datasets from two manufacturers.
W. Chen (2010)
A margin sharpness measurement for the diagnosis of breast cancer from magnetic resonance imaging examinations.
Jacob E. D. Levman (2011)
Textural Features for Image Classification
R. Haralick (1973)
On cluster validity for the fuzzy c-means model
N. Pal (1995)
Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications
T. Sørlie (2001)
Presenting Features of Breast Cancer Differ by Molecular Subtype
L. Wiechmann (2009)
Fibroblast growth factor/fibroblast growth factor receptor system in angiogenesis.
M. Presta (2005)
Luminal-B breast cancer and novel therapeutic targets
B. Tran (2011)
Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics.
W. Chen (2004)
Potential for targeting the fibroblast growth factor receptors in breast cancer.
N. Hynes (2010)
Assessment of Feasibility to Use Computer Aided Texture Analysis Based Tool for Parametric Images of Suspicious Lesions in DCE-MR Mammography
M. C. Kale (2013)
Breast cancer molecular subtypes in patients with locally advanced disease: impact on prognosis, patterns of recurrence, and response to therapy.
K. Huber (2009)
Drug resistance by evasion of antiangiogenic targeting of VEGF signaling in late-stage pancreatic islet tumors.
O. Casanovas (2005)
Comprehensive molecular portraits of human breast tumors
D. Koboldt (2012)
Molecular subclasses of breast cancer: how do we define them? The IMPAKT 2012 Working Group Statement.
S. Guiu (2012)
Molecular portraits of human breast tumours
C. Perou (2000)
A Multichannel Markov Random Field Framework for Tumor Segmentation With an Application to Classification of Gene Expression-Based Breast Cancer Recurrence Risk
A. Ashraf (2013)
Computed-aided diagnosis (CAD) in the detection of breast cancer.
C. Dromain (2013)
Computer-aided detection in breast MRI: a systematic review and meta-analysis
M. Dorrius (2011)

This paper is referenced by
Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer
Ming Fan (2017)
TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review
Mario Zanfardino (2019)
Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features.
W. Ma (2019)
Promoting Collaborations Between Radiologists and Scientists.
John-Paul J. Yu (2018)
Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with benign lesions and evaluation of heterogeneity in different tumor regions with prognostic factors and molecular classification
M. Zhao (2018)
Optoacoustic imaging of the breast: correlation with histopathology and histopathologic biomarkers
Gisela L. G. Menezes (2019)
MRI-based radiogenomics analysis for predicting genetic alterations in oncogenic signalling pathways in invasive breast carcinoma.
Peihong Lin (2020)
Imaging Surveillance for Survivors of Breast Cancer: Correlation between Cancer Characteristics and Method of Detection
A. Chu (2017)
Acute Tumor Transition Angle on Computed Tomography Predicts Chromosomal Instability Status of Primary Gastric Cancer: Radiogenomics Analysis from TCGA and Independent Validation
Ying-Chieh Lai (2019)
Additive Benefit of Radiomics Over Size Alone in the Distinction Between Benign Lesions and Luminal A Cancers on a Large Clinical Breast MRI Dataset.
H. M. Whitney (2019)
Molecular subtypes and imaging phenotypes of breast cancer
N. Cho (2016)
Clinical applications of optical and optoacoustic imaging techniques in the breast
Wei Tse Yang (2018)
Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Method
H. M. Whitney (2020)
Differentiation between Luminal A and B Molecular Subtypes of Breast Cancer Using Pharmacokinetic Quantitative Parameters with Histogram and Texture Features on Preoperative Dynamic Contrast-Enhanced Magnetic Resonance Imaging.
Hongbing Luo (2019)
Radiogenomic analysis of breast cancer: dynamic contrast enhanced - magnetic resonance imaging based features are associated with molecular subtypes
Shijian Wang (2016)
Breast Cancer Radiogenomics: Current Status and Future Directions.
L. Grimm (2020)
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
M. Mazurowski (2019)
A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features
Ashirbani Saha (2018)
Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter‐reader variability in annotating tumors
Ashirbani Saha (2018)
Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm
R. Ha (2019)
Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data
M. Mazurowski (2017)
Comparison of Different Dynamic Contrast Enhanced-Magnetic Resonance Imaging Descriptors and Clinical Findings Among Breast Cancer Subtypes Determined Based on Molecular Assessment
S. Doğan (2018)
Background parenchymal enhancement on breast MRI: A comprehensive review
G. Liao (2019)
Radiogenomics: bridging imaging and genomics
Z. Bodalal (2019)
Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma
Y. Zhu (2015)
Recurrence-free survival in breast cancer is associated with MRI tumor enhancement dynamics quantified using computer algorithms.
M. Mazurowski (2015)
Breast cancer subtype intertumor heterogeneity: MRI‐based features predict results of a genomic assay
E. Sutton (2015)
Physically Motivated Feature Development for Machine Learning Applications
Nicholas Czarnek (2017)
Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer.
J. Wu (2017)
Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics.
Ashirbani Saha (2016)
A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models
Ashirbani Saha (2018)
DCE-MRI-based Tumor Subregion Partitioning with Texture Feature Extraction for Prediction of Ki-67 Status of Estrogen Receptor-Positive Breast Cancers
Ming Fan (2017)
See more
Semantic Scholar Logo Some data provided by SemanticScholar