Online citations, reference lists, and bibliographies.

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.

L. Grimm, J. Zhang, M. Mazurowski
Published 2015 · Medicine

Cite This
Download PDF
Analyze on Scholarcy
Share
PURPOSE To identify associations between semiautomatically extracted MRI features and breast cancer molecular subtypes. METHODS We analyzed routine clinical pre-operative breast MRIs from 275 breast cancer patients at a single institution in this retrospective, Institutional Review Board-approved study. Six fellowship-trained breast imagers reviewed the MRIs and annotated the cancers. Computer vision algorithms were then used to extract 56 imaging features from the cancers including morphologic, texture, and dynamic features. Surrogate markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor-2 [HER2]) were used to categorize tumors by molecular subtype: ER/PR+, HER2- (luminal A); ER/PR+, HER2+ (luminal B); ER/PR-, HER2+ (HER2); ER/PR/HER2- (basal). A multivariate analysis was used to determine associations between the imaging features and molecular subtype. RESULTS The imaging features were associated with both luminal A (P = 0.0007) and luminal B (P = 0.0063) molecular subtypes. No association was found for either HER2 (P = 0.2465) or basal (P = 0.1014) molecular subtype and the imaging features. A P-value of 0.0125 (0.05/4) was considered significant. CONCLUSION Luminal A and luminal B molecular subtype breast cancer are associated with semiautomatically extracted features from routine contrast enhanced breast MRI.
This paper references
10.1016/j.ctrv.2013.06.006
Breast cancer classification by proteomic technologies: current state of knowledge.
S. W. Lam (2014)
10.1200/JCO.2005.02.6914
Prognostic value of pathologic complete response after primary chemotherapy in relation to hormone receptor status and other factors.
V. Guarneri (2006)
10.1245/s10434-009-0606-2
Presenting Features of Breast Cancer Differ by Molecular Subtype
L. Wiechmann (2009)
10.1016/J.BREAST.2007.02.006
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)
10.1109/TSMC.1973.4309314
Textural Features for Image Classification
R. Haralick (1973)
10.1158/1078-0432.CCR-04-2421
Breast Cancer Molecular Subtypes Respond Differently to Preoperative Chemotherapy
R. Rouzier (2005)
10.1007/S12094-008-0265-Y
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)
10.1002/cncr.22981
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)
10.1002/jmri.24863
Differentiating benign from malignant vertebral fractures using T1 -weighted dynamic contrast-enhanced MRI.
J. Arevalo-Perez (2015)
10.1186/bcr2904
Luminal-B breast cancer and novel therapeutic targets
B. Tran (2011)
10.1158/0008-5472.CAN-04-4115
Gene expression profiling identifies molecular subtypes of inflammatory breast cancer.
F. Bertucci (2005)
10.1158/1078-0432.CCR-06-1109
The Triple Negative Paradox: Primary Tumor Chemosensitivity of Breast Cancer Subtypes
L. Carey (2007)
Radiogenomic analysis of breast cancer: Luminal B molecular subtype is associated with enhancement dynamics in MRI. Radiology 2014;273(2):365-372
MA Mazurowski (2014)
10.1118/1.1695652
Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics.
W. Chen (2004)
10.1093/annonc/mdt303
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
A. Goldhirsch (2013)
10.1007/s12282-013-0512-0
Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer: comparison based on the molecular subtypes of invasive breast cancer
K. Yamaguchi (2014)
10.5858/arpa.2013-0953-SA
Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update.
A. Wolff (2014)
10.3892/OL.2012.1004
Analysis of complete response by MRI following neoadjuvant chemotherapy predicts pathological tumor responses differently for molecular subtypes of breast cancer.
Y. Hayashi (2013)
10.1016/j.semradonc.2009.05.004
Breast cancer molecular subtypes in patients with locally advanced disease: impact on prognosis, patterns of recurrence, and response to therapy.
K. Huber (2009)
10.2214/AJR.11.7824
Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape.
S. Yamamoto (2012)
10.1007/s10549-011-1709-6
A retrospective study of breast cancer subtypes: the risk of relapse and the relations with treatments
Y. Wang (2011)
10.1016/j.breast.2012.07.004
Predictive value of breast cancer molecular subtypes in Chinese patients with four or more positive nodes after postmastectomy radiotherapy.
San-Gang Wu (2012)
10.1177/0284185114524197
Quantitative MRI morphology of invasive breast cancer: correlation with immunohistochemical biomarkers and subtypes
Min Sun Bae (2015)
10.1109/42.668698
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
J. Sled (1998)
10.1200/JCO.2007.14.4287
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)
10.1148/radiol.14132641
Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.
M. Mazurowski (2014)
10.1002/jmri.24894
Diagnostic accuracy of diffusion-weighted MRI for differentiation of cervical cancer and benign cervical lesions at 3.0T: Comparison with routine MRI and dynamic contrast-enhanced MRI.
Fei Li Kuang (2015)
10.4065/77.2.148
HER2 testing in patients with breast cancer: poor correlation between weak positivity by immunohistochemistry and gene amplification by fluorescence in situ hybridization.
E. Perez (2002)
10.3816/CBC.2007.N.012
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)
10.1158/0008-5472.CAN-07-5644
Subtypes of breast cancer show preferential site of relapse.
M. Smid (2008)
10.1200/JCO.2007.14.5565
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)
10.1038/35021093
Molecular portraits of human breast tumours
C. Perou (2000)
10.1148/radiol.09090838
Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers.
N. Bhooshan (2010)
10.1109/91.413225
On cluster validity for the fuzzy c-means model
N. Pal (1995)
10.1073/pnas.191367098
Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications
T. Sørlie (2001)
10.3892/OR_00000752
Molecular subtype can predict the response and outcome of Chinese locally advanced breast cancer patients treated with preoperative therapy.
X. S. Chen (2010)



This paper is referenced by
10.1016/j.acra.2016.11.021
Role of Imaging in the Era of Precision Medicine.
A. Giardino (2017)
10.1117/12.2217658
Radiogenomic analysis of breast cancer: dynamic contrast enhanced - magnetic resonance imaging based features are associated with molecular subtypes
Shijian Wang (2016)
10.1016/j.crad.2017.10.021
Clinical role of breast MRI now and going forward.
D. Leithner (2018)
10.1016/j.yacr.2019.04.012
Radiogenomics of Oncology: Current Trends and Future Directions
Jason Chiang (2019)
10.18632/oncotarget.20643
Tumor image-derived texture features are associated with CD3 T-cell infiltration status in glioblastoma
S. Narang (2017)
10.1007/s00261-019-02028-w
Radiogenomics: bridging imaging and genomics
Z. Bodalal (2019)
10.11606/D.45.2019.tde-25092019-133337
A new calibration approach to graph-based semantic segmentation
Mateus Riva (2018)
10.1007/s00259-017-3770-9
[18F]FDG PET/CT features for the molecular characterization of primary breast tumors
L. Antunovic (2017)
10.1007/s00117-018-0409-1
Imaging and the completion of the omics paradigm in breast cancer
Doris Leithner (2018)
10.1259/bjr.20160140
Intravoxel incoherent motion diffusion-weighted MR imaging of breast cancer: association with histopathological features and subtypes.
Yunju Kim (2016)
10.3390/cancers12020518
The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status
Rossana Castaldo (2020)
10.3390/ijms18040805
Radiogenomic Analysis of Oncological Data: A Technical Survey
M. Incoronato (2017)
10.1001/jamanetworkopen.2019.2561
Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)–Positive Breast Cancer
Nathaniel Braman (2019)
10.1158/1078-0432.CCR-17-3783
Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer
Hyunjin Park (2018)
Computer Vision Tracking of sUAS From a Pan/Tilt Platform
Jeremy Patrick Ogorzalek (2019)
10.18632/aging.101769
IDH mutation-specific radiomic signature in lower-grade gliomas
X. Liu (2019)
10.1007/s00330-018-5891-3
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)
10.1016/j.cdtm.2018.01.002
Quantitative image analysis for evaluation of tumor response in clinical oncology
Wenli Cai (2018)
10.1186/s40644-017-0126-4
Proceedings of the International Cancer Imaging Society (ICIS) 17th Annual Teaching Course
Elena Tagliabue (2017)
10.1259/bjr.20151030
Imaging genomics in cancer research: limitations and promises.
H. Bai (2016)
10.1371/journal.pone.0171683
Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer
Ming Fan (2017)
10.1097/RTI.0000000000000375
Detection of Calcified Aortic Plaques in an Apolipoprotein E Animal Model Using a Human Computed Tomography System for Ultra–High-resolution Imaging: A Feasibility Study
Calin Manta (2019)
PREDICTION OF 1 P / 19 Q CODELETION STATUS IN DIFFUSE GLIOMA PATIENTS USING PREOPERATIVE MULTIPARAMETRIC MAGNETIC RESONANCE IMAGING
Donnie Kim (2018)
10.1067/j.cpradiol.2018.08.003
Relationships Between Human-Extracted MRI Tumor Phenotypes of Breast Cancer and Clinical Prognostic Indicators Including Receptor Status and Molecular Subtype.
J. Net (2018)
10.1117/12.2255866
Can BI-RADS features on mammography be used as a surrogate for expensive genomic testing in breast cancer patients?
Michael R. Harowicz (2017)
10.1007/s13167-018-0128-8
The crucial role of multiomic approach in cancer research and clinically relevant outcomes
M. Lu (2018)
10.1177/1533033820916191
The Application of Radiomics in Breast MRI: A Review
Dong-Man Ye (2020)
Clinical characteristics of Lower Grade Glioma patients in training and validation set
Xupeng Liu (2019)
10.1007/978-3-319-68873-2_6
Introduction to Radiogenomics
Vassilios D Raptopoulos (2018)
10.1118/1.4955435
Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics.
Ashirbani Saha (2016)
10.1002/jmri.25870
Background, current role, and potential applications of radiogenomics
K. Pinker (2018)
PREDICTION OF 1P/19Q CODELETION STATUS IN DIFFUSE GLIOMA PATIENTS USING PREOPERATIVE MULTIPARAMETRIC MAGNETIC RESONANCE IMAGING
Donnie Kim (2020)
See more
Semantic Scholar Logo Some data provided by SemanticScholar