← Back to Search
Heterogeneous Enhancement Patterns Of Tumor-adjacent Parenchyma At MR Imaging Are Associated With Dysregulated Signaling Pathways And Poor Survival In Breast Cancer.
J. Wu, B. Li, Xiaoli Sun, Guohong Cao, D. Rubin, S. Napel, D. Ikeda, A. Kurian, R. Li
Published 2017 · Medicine
Download PDFAnalyze on Scholarcy
Purpose To identify the molecular basis of quantitative imaging characteristics of tumor-adjacent parenchyma at dynamic contrast material-enhanced magnetic resonance (MR) imaging and to evaluate their prognostic value in breast cancer. Materials and Methods In this institutional review board-approved, HIPAA-compliant study, 10 quantitative imaging features depicting tumor-adjacent parenchymal enhancement patterns were extracted and screened for prognostic features in a discovery cohort of 60 patients. By using data from The Cancer Genome Atlas (TCGA), a radiogenomic map for the tumor-adjacent parenchymal tissue was created and molecular pathways associated with prognostic parenchymal imaging features were identified. Furthermore, a multigene signature of the parenchymal imaging feature was built in a training cohort (n = 126), and its prognostic relevance was evaluated in two independent cohorts (n = 879 and 159). Results One image feature measuring heterogeneity (ie, information measure of correlation) was significantly associated with prognosis (false-discovery rate < 0.1), and at a cutoff of 0.57 stratified patients into two groups with different recurrence-free survival rates (log-rank P = .024). The tumor necrosis factor signaling pathway was identified as the top enriched pathway (hypergeometric P < .0001) among genes associated with the image feature. A 73-gene signature based on the tumor profiles in TCGA achieved good association with the tumor-adjacent parenchymal image feature (R2 = 0.873), which stratified patients into groups regarding recurrence-free survival (log-rank P = .029) and overall survival (log-rank P = .042) in an independent TCGA cohort. The prognostic value was confirmed in another independent cohort (Gene Expression Omnibus GSE 1456), with log-rank P = .00058 for recurrence-free survival and log-rank P = .0026 for overall survival. Conclusion Heterogeneous enhancement patterns of tumor-adjacent parenchyma at MR imaging are associated with the tumor necrosis signaling pathway and poor survival in breast cancer. © RSNA, 2017 Online supplemental material is available for this article.
This paper references
Breast cancer molecular subtype classifier that incorporates MRI features
E. Sutton (2016)
MRI Background Parenchymal Enhancement Is Not Associated with Breast Cancer
B. Bennani-Baiti (2016)
Tumour necrosis factor α confers an invasive, transformed phenotype on mammary epithelial cells
R. Montesano (2005)
Cells secreting tumour necrosis factor show enhanced metastasis in nude mice.
S. T. Malik (1990)
Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis.
S. Yamamoto (2015)
TNF‐α promotes invasive growth through the MET signaling pathway
Viola Bigatto (2015)
Topographic enhancement mapping of the cancer-associated breast stroma using breast MRI.
N. Nabavizadeh (2011)
CCR 20th Anniversary Commentary: Gene-Expression Signature in Breast Cancer—Where Did It Start and Where Are We Now?
I. Gingras (2015)
Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study
J. Wang (2015)
Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts
Y. Pawitan (2005)
Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data
Wentian Guo (2015)
Breast MRI background parenchymal enhancement (BPE) correlates with the risk of breast cancer.
M. Telegrafo (2016)
Comprehensive molecular portraits of human breast tumors
D. Koboldt (2012)
MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.
H. Li (2016)
Multigene prognostic tests in breast cancer: past, present, future
B. Győrffy (2015)
Multiparametric Magnetic Resonance Imaging for Predicting Pathological Response After the First Cycle of Neoadjuvant Chemotherapy in Breast Cancer
Xia Li (2015)
Updates and revisions to the BI-RADS magnetic resonance imaging lexicon.
Sonya D Edwards (2013)
Tumor Intrinsic Subtype Is Reflected in Cancer-Adjacent Tissue
Patricia Casbas-Hernández (2014)
Cancer statistics, 2016
Rebecca L. Siegel Mph (2016)
Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.
M. Mazurowski (2014)
Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma
Y. Zhu (2015)
N4ITK: Improved N3 Bias Correction
N. Tustison (2010)
Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape.
S. Yamamoto (2012)
A gene-expression signature as a predictor of survival in breast cancer.
M. J. van de Vijver (2002)
Prospective Validation of a 21-Gene Expression Assay in Breast Cancer.
J. Sparano (2015)
MRI measurements of breast tumor volume predict response to neoadjuvant chemotherapy and recurrence-free survival.
S. Partridge (2005)
Magnetic resonance imaging texture analysis classification of primary breast cancer
S. Waugh (2015)
Invasive breast cancer: predicting disease recurrence by using high-spatial-resolution signal enhancement ratio imaging.
K. Li (2008)
Breast cancer intra-tumor heterogeneity
L. Martelotto (2014)
Survival outcomes of breast cancer patients who receive neoadjuvant chemotherapy: association with dynamic contrast-enhanced MR imaging with computer-aided evaluation.
A. Yi (2013)
Prognostic value of pre-treatment DCE-MRI parameters in predicting disease free and overall survival for breast cancer patients undergoing neoadjuvant chemotherapy.
M. Pickles (2009)
Pretreatment Prognostic Value of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Vascular, Texture, Shape, and Size Parameters Compared With Traditional Survival Indicators Obtained From Locally Advanced Breast Cancer Patients
M. Pickles (2016)
WGCNA: an R package for weighted correlation network analysis
P. Langfelder (2008)
Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage
E. Burnside (2016)
Radiogenomic Analysis Demonstrates Associations between (18)F-Fluoro-2-Deoxyglucose PET, Prognosis, and Epithelial-Mesenchymal Transition in Non-Small Cell Lung Cancer.
S. Yamamoto (2016)
Enhancement of experimental metastasis by tumor necrosis factor
P. Orosz (1993)
Positive predictive value of BI-RADS MR imaging.
M. Mahoney (2012)
Background parenchymal enhancement at breast MR imaging and breast cancer risk.
Valencia King (2011)
Enhancing area surrounding breast carcinoma on MR mammography: comparison with pathological examination
M. van Goethem (2004)
Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles.
A. Ashraf (2014)
Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPY TRIAL.
N. Hylton (2012)
Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?
F. Murtagh (2014)
Adjuvant therapy for breast cancer — results from the USA consensus conference
J. Abrams (2001)
Intratumor heterogeneity and precision of microarray-based predictors of breast cancer biology and clinical outcome.
W. Barry (2010)
Breast stromal enhancement on MRI is associated with response to neoadjuvant chemotherapy.
J. Hattangadi (2008)
Association between Parenchymal Enhancement of the Contralateral Breast in Dynamic Contrast-enhanced MR Imaging and Outcome of Patients with Unilateral Invasive Breast Cancer.
Bas H. M. van der Velden (2015)
Breast cancer subtype intertumor heterogeneity: MRI‐based features predict results of a genomic assay
E. Sutton (2015)
Macrophage-induced angiogenesis is mediated by tumour necrosis factor-α
S. Leibovich (1987)
Textural Features for Image Classification
R. Haralick (1973)
Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation
J. Wu (2017)
Prognostic value of DCE-MRI in breast cancer patients undergoing neoadjuvant chemotherapy: a comparison with traditional survival indicators
M. Pickles (2014)
Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers.
N. Bhooshan (2010)
Unsupervised Clustering of Quantitative Image Phenotypes Reveals Breast Cancer Subtypes with Distinct Prognoses and Molecular Pathways
J. Wu (2017)
Background parenchymal signal enhancement ratio at preoperative MR imaging: association with subsequent local recurrence in patients with ductal carcinoma in situ after breast conservation surgery.
Sun-Ah Kim (2014)
Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer.
W. Berg (2004)
Epithelial-Mesenchymal Transitions in Development and Disease
J. Thiery (2009)
Are Qualitative Assessments of Background Parenchymal Enhancement, Amount of Fibroglandular Tissue on MR Images, and Mammographic Density Associated with Breast Cancer Risk?
Brian N Dontchos (2015)
Glioblastoma multiforme regional genetic and cellular expression patterns: influence on anatomic and physiologic MR imaging.
R. Barajas (2010)
Background parenchymal enhancement in breast MRI before and after neoadjuvant chemotherapy: correlation with tumour response
H. Preibsch (2015)
Cancer statistics, 2016
R. Siegel (2016)
TNF-α/NF-κB/Snail pathway in cancer cell migration and invasion
Y. Wu (2010)
Reply to "Breast MRI background parenchymal enhancement (BPE) correlates with the risk of breast cancer".
B. Bennani-Baiti (2016)
Regulation of in situ to invasive breast carcinoma transition.
M. Hu (2008)
Tumor necrosis factor-alpha stimulates the epithelial-to-mesenchymal transition of human colonic organoids.
R. Bates (2003)
Optimized breast MRI functional tumor volume as a biomarker of recurrence‐free survival following neoadjuvant chemotherapy
N. Jafri (2014)
Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials.
C. Davies (2011)
The Molecular Signatures Database Hallmark Gene Set Collection
Arthur Liberzon (2015)
Neoadjuvant Chemotherapy for Breast Cancer: Functional Tumor Volume by MR Imaging Predicts Recurrence-free Survival-Results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL.
N. Hylton (2016)
TNF-α in promotion and progression of cancer
F. Balkwill (2006)
Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study.
S. Agner (2014)
Estimating the number of clusters in a dataset via the gap statistic
R. Tibshirani (2000)
Changes in primary breast cancer heterogeneity may augment midtreatment MR imaging assessment of response to neoadjuvant chemotherapy.
Jyoti K. Parikh (2014)
Predicting survival and early clinical response to primary chemotherapy for patients with locally advanced breast cancer using DCE‐MRI
Roar Johansen (2009)
This paper is referenced by
Development of MR-based preoperative nomograms predicting DNA copy number subtype in lower grade gliomas with prognostic implication
Si-wei Zhang (2020)
Radiomics and radiogenomics for precision radiotherapy
J. Wu (2018)
Prognostic Significance of CT-Attenuation of Tumor-Adjacent Breast Adipose Tissue in Breast Cancer Patients with Surgical Resection
J. D. Lee (2019)
Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma
Yihuai Hu (2020)
18F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma
Z. Kong (2019)
Machine learning in breast MRI
B. Reig (2019)
Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study
Ahmad Algohary (2020)
Radiogenomics: bridging imaging and genomics
Z. Bodalal (2019)
Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI
J. Zhou (2020)
Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer
M. Fan (2020)
Radiomics-Based Preoperative Prediction of Lymph Node Status Following Neoadjuvant Therapy in Locally Advanced Rectal Cancer
Xuezhi Zhou (2020)
Contralateral parenchymal enhancement on dynamic contrast-enhanced MRI reproduces as a biomarker of survival in ER-positive/HER2-negative breast cancer patients
B. H. M. van der Velden (2018)
Background parenchymal enhancement on breast MRI: A comprehensive review
G. Liao (2019)
Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study
Nathaniel Braman (2020)
Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy.
J. Wu (2018)
Correlation between magnetic resonance imaging and the level of tumor-infiltrating lymphocytes in patients with estrogen receptor-negative HER2-positive breast cancer
W. J. Choi (2019)
Prediction of tumor doubling time of lung adenocarcinoma using radiomic margin characteristics
Hyun Jung Yoon (2020)
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
Ming Fan (2018)
Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)–Positive Breast Cancer
Nathaniel Braman (2019)
Radiomic features analysis by digital breast tomosynthesis and contrast-enhanced dual-energy mammography to detect malignant breast lesions
R. Fusco (2019)
Deep learning analysis of the primary tumour and the prediction of lymph node metastases in gastric cancer.
C. Jin (2020)
Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images
L. Liu (2017)
A multi-organ cancer study of the classification performance using 2D and 3D image features in radiomics analysis.
L. Xu (2019)
TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review
Mario Zanfardino (2019)
Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer
J. Wu (2018)
Emergence of "Big Data" and Its Potential and Current Limitations in Medical Imaging.
M. Yaffe (2019)
Sequential [ 18 F ] FDG-[ 18 F ] FMISO PET and Multiparametric MRI at 3 T for Insights into Breast Cancer Heterogeneity and Correlation with Patient Outcomes : First Clinical Experience
P. Andrzejewski (2019)
Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region
Qiuchang Sun (2020)
Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients
Ming Fan (2019)
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)
The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status
Rossana Castaldo (2020)
Sequential [18F]FDG-[18F]FMISO PET and Multiparametric MRI at 3T for Insights into Breast Cancer Heterogeneity and Correlation with Patient Outcomes: First Clinical Experience
P. Andrzejewski (2019)See more