Breast MRI Radiogenomics: Current Status And Research Implications.
Lars J. Grimm
Published 2016 · Medicine
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Breast magnetic resonance imaging (MRI) radiogenomics is an emerging area of research that has the potential to directly influence clinical practice. Clinical MRI scanners today are capable of providing excellent temporal and spatial resolution, which allows extraction of numerous imaging features via human extraction approaches or complex computer vision algorithms. Meanwhile, advances in breast cancer genetics research has resulted in the identification of promising genes associated with cancer outcomes. In addition, validated genomic signatures have been developed that allow categorization of breast cancers into distinct molecular subtypes as well as predict the risk of cancer recurrence and response to therapy. Current radiogenomics research has been directed towards exploratory analysis of individual genes, understanding tumor biology, and developing imaging surrogates to genetic analysis with the long-term goal of developing a meaningful tool for clinical care. The background of breast MRI radiogenomics research, image feature extraction techniques, approaches to radiogenomics research, and promising areas of investigation are reviewed. J. Magn. Reson. Imaging 2016;43:1269-1278.
This paper references
Molecular Profiling for Breast Cancer: A Comprehensive Review
Muaiad Kittaneh (2013)
BI-RADS: magnetic resonance imaging
DM Ikeda (2013)
Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study.
Lisa A. Carey (2006)
Breast cancer molecular subtypes in patients with locally advanced disease: impact on prognosis, patterns of recurrence, and response to therapy.
Kathryn E. Huber (2009)
NF - kappaB protein expression associates with ( 18 ) F - FDG PET tumor uptake in non - small
Correlations between apparent diffusion coefficient values of invasive ductal carcinoma and pathologic factors on diffusion-weighted MRI at 3.0 Tesla.
Sung Hee Park (2015)
American society of clinical oncology.
Walter Alexander (2008)
MaZda - A software package for image texture analysis
Piotr M. Szczypinski (2009)
The probably benign assessment.
Jessica W T Leung (2007)
Long noncoding RNA HOTAIR reprograms chromatin state to promote cancer metastasis
Rajnish A. Gupta (2010)
A Multichannel Markov Random Field Framework for Tumor Segmentation With an Application to Classification of Gene Expression-Based Breast Cancer Recurrence Risk
Ahmed Bilal Ashraf (2013)
Magnetic resonance imaging texture analysis classification of primary breast cancer
Shelley A. Waugh (2015)
Prognostic value of pre-treatment DCE-MRI parameters in predicting disease free and overall survival for breast cancer patients undergoing neoadjuvant chemotherapy.
Martin D. Pickles (2009)
Textural Features for Image Classification
Robert M. Haralick (1973)
Radiogenomics: what it is and why it is important.
Maciej A. Mazurowski (2015)
Breast cancer classification by proteomic technologies: current state of knowledge.
Siu W. Lam (2014)
Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations.
Christoph Alexander Karlo (2014)
Can breast cancer molecular subtype help to select patients for preoperative MR imaging?
Lars J. Grimm (2015)
Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures
Pratyaksha Wirapati (2008)
Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013
Aron Goldhirsch (2013)
Subtypes of breast cancer show preferential site of relapse.
Marcel Smid (2008)
Behind the numbers: Decoding molecular phenotypes with radiogenomics--guiding principles and technical considerations.
Michael D. Kuo (2014)
Molecular subclasses of breast cancer: how do we define them? The IMPAKT 2012 Working Group Statement.
Sévérine Guiu (2012)
Recurrence-free survival in breast cancer is associated with MRI tumor enhancement dynamics quantified using computer algorithms.
Maciej A. Mazurowski (2015)
Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles
Kayoung Shin (2015)
Multigene assays and isolated tumor cells for early breast cancer treatment: time for bionetworks
Dimitrios H Roukos (2010)
The Triple Negative Paradox: Primary Tumor Chemosensitivity of Breast Cancer Subtypes
Lisa A. Carey (2007)
Imaging genomics of Glioblastoma: state of the art bridge between genomics and neuroradiology.
Mohamed G Elbanan (2015)
Hepatocellular carcinoma: can circulating tumor cells and radiogenomics deliver personalized care?
Richard L Hesketh (2015)
American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer.
Lyndsay N. Harris (2007)
NF-kappaB protein expression associates with (18)F-FDG PET tumor uptake in nonsmall cell lung cancer: a radiogenomics validation study to understand tumor metabolism. Lung Cancer 2014;83:189–196
VS Nair (2014)
Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles.
Ahmed Bilal Ashraf (2014)
NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures
Rivka R. Colen (2014)
Analysis of complete response by MRI following neoadjuvant chemotherapy predicts pathological tumor responses differently for molecular subtypes of breast cancer.
Yuji Hayashi (2013)
Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications
Therese Sørlie (2001)
Clustering approaches to identifying gene expression patterns from DNA microarray data.
Jin Hwan Do (2008)
MRI phenotype of breast cancer: Kinetic assessment for molecular subtypes.
Eric M Blaschke (2015)
NF-κB protein expression associates with (18)F-FDG PET tumor uptake in non-small cell lung cancer: a radiogenomics validation study to understand tumor metabolism.
Viswam S. Nair (2014)
American Cancer Society Guidelines for Breast Screening with MRI as an Adjunct to Mammography
Debbie Saslow (2007)
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.
Luís Fernández-Morales (2007)
Breast cancer molecular subtype as a predictor of the utility of preoperative MRI.
Richard Ha (2015)
Texture analysis methodologies for magnetic resonance imaging
Andrzej Materka (2004)
ACR practice parameter for the performance of contrast-enhanced mangnetic resonance imaging (MRI) of the breast
MC Mahoney (2014)
Histogram analysis of apparent diffusion coefficient at 3.0t: Correlation with prognostic factors and subtypes of invasive ductal carcinoma.
Eun Jeong Kim (2015)
Breast cancer version 3.2014.
William John Gradishar (2014)
Molecular portraits of human breast tumours
Charles M. Perou (2000)
Controlling the false discovery rate in behavior genetics research
Yoav Benjamini (2001)
Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging.
Aaron M Rutman (2009)
Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay.
Elizabeth J. Sutton (2015)
Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis.
Shota Yamamoto (2015)
Texture Analysis Methods - A Review
Andrzej Materka (1998)
Pre-operative staging of breast cancer with breast MRI: one step forward, two steps back?
Christiane K. Kuhl (2007)
Luminal-B breast cancer and novel therapeutic targets
Ben Tran (2011)
Correlations between diffusion-weighted imaging and breast cancer biomarkers
Laura Martincich (2012)
Survival outcomes of breast cancer patients who receive neoadjuvant chemotherapy: association with dynamic contrast-enhanced MR imaging with computer-aided evaluation.
Ann Yi (2013)
Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer: comparison based on the molecular subtypes of invasive breast cancer
Ken Yamaguchi (2014)
Estrogen receptor, progesterone receptor, HER-2, and response to postmastectomy radiotherapy in high-risk breast cancer: the Danish Breast Cancer Cooperative Group.
Marianne Kyndi (2008)
Cost-effectiveness analysis of Mammostrat® compared with Oncotype DX® to inform the treatment of breast cancer
Kimberly Mislick (2014)
2000 update of recommendations for the use of tumor markers in breast and colorectal cancer: clinical practice guidelines of the American Society of Clinical Oncology.
Robert C. Bast (2001)
The current status of breast MR imaging. Part I. Choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice.
Christiane K. Kuhl (2007)
Diffusion-weighted magnetic resonance imaging and its application to cancer
Elizabeth M. Charles-Edwards (2006)
Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.
Maciej A. Mazurowski (2014)
'Omic approaches to preventing or managing metastatic breast cancer
Obi L. Griffith (2011)
Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape.
Shota Yamamoto (2012)
Parameters of Dynamic Contrast-Enhanced MRI as Imaging Markers for Angiogenesis and Proliferation in Human Breast Cancer
Lin Li (2015)
Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms.
Lars J. Grimm (2015)
This paper is referenced by
Differentiation between idiopathic granulomatous mastitis and invasive breast carcinoma, both presenting with non-mass enhancement without rim-enhanced masses: The value of whole-lesion histogram and texture analysis using apparent diffusion coefficient.
Qiufeng Zhao (2019)
Personalized Medicine, Biomarkers of Risk and Breast MRI
Elizabeth J. Sutton (2017)
Imaging and the completion of the omics paradigm in breast cancer
Doris Leithner (2018)
Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis
Robin W Jansen (2018)
A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI
Kavya Ravichandran (2018)
Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes
Elizabeth J. Sutton (2017)
MR imaging features associated with distant metastasis-free survival of patients with invasive breast cancer: a case–control study
Sung Eun Song (2017)
Lymph Node Imaging in Patients with Primary Breast Cancer: Concurrent Diagnostic Tools
Maria Adele Marino (2020)
Integration of dynamic contrast-enhanced magnetic resonance imaging and T2-weighted imaging radiomic features by a canonical correlation analysis-based feature fusion method to predict histological grade in ductal breast carcinoma.
Ming Fan (2019)
Biomarkers and Imaging of Breast Cancer.
Olena Weaver (2018)
MR Imaging–Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma
Michael Iv (2019)
Correlation between NF1 genotype and imaging phenotype on whole-body MRI
Yunpeng Liu (2020)
Mammography-based radiomic analysis for predicting benign BI-RADS category 4 calcifications.
Chuqian Lei (2019)
Apparent Diffusion Coefficient Value to Evaluate Tumor Response After Neoadjuvant Chemotherapy in Patients with Breast Cancer.
Yazmín Aseret Ramírez-Galván (2018)
Contrast-Enhanced Mammography and Radiomics Analysis for Noninvasive Breast Cancer Characterization: Initial Results
Maria Adele Marino (2019)
Background, current role, and potential applications of radiogenomics
Katja Pinker (2018)
Intravoxel Incoherent Motion and Quantitative Non-Gaussian Diffusion MR Imaging: Evaluation of the Diagnostic and Prognostic Value of Several Markers of Malignant and Benign Breast Lesions.
Mami Iima (2018)
Evaluation of human epidermal growth factor receptor 2 status of breast cancer using preoperative multidetector computed tomography with deep learning and handcrafted radiomics features
Xiaojun Yang (2020)
Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer
Tianwen Xie (2019)
Radiogenomics of rectal adenocarcinoma in the era of precision medicine: A pilot study of associations between qualitative and quantitative MRI imaging features and genetic mutations.
Natally Horvat (2019)
Synchronous Breast Cancer: Phenotypic Similarities on MRI
Hui Wang (2019)
Deep Learning in Breast Cancer Screening
Hugh Harvey (2019)
Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer
Ming Fan (2017)
Imaging biomarkers in oncology: Basics and application to MRI
Isabel Dregely (2018)
Interim heterogeneity changes measured using entropy texture features on T2-weighted MRI at 3.0 T are associated with pathological response to neoadjuvant chemotherapy in primary breast cancer
Shelley A. Henderson (2017)