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

Recurrence-free Survival In Breast Cancer Is Associated With MRI Tumor Enhancement Dynamics Quantified Using Computer Algorithms.

M. Mazurowski, L. Grimm, J. Zhang, P. Marcom, S. Yoon, C. Kim, S. Ghate, K. Johnson
Published 2015 · Medicine

Cite This
Download PDF
Analyze on Scholarcy
Share
PURPOSE The purpose of this study is to investigate the association between breast cancer recurrence-free survival and breast magnetic resonance imaging (MRI) tumor enhancement dynamics which are quantified semi-automatically using computer algorithms. METHODS In this retrospective IRB-approved study, we analyzed data from 275 breast cancer patients at a single institution. Recurrence-free survival data were obtained from the medical record. Routine clinical pre-operative breast MRIs were performed in all patients. The tumors were marked on the MRIs by fellowship-trained breast radiologists. A previously developed computer algorithm was applied to the marked tumors to quantify the enhancement dynamics relative to the automatically assessed background parenchymal enhancement. To establish whether the contrast enhancement feature quantified by the algorithm was associated with recurrence-free survival, we constructed a Cox proportional hazards regression model with the computer-extracted feature as a covariate. We controlled for tumor grade and size (major axis length), patient age, patient race/ethnicity, and menopausal status. RESULTS The analysis showed that the semi-automatically obtained feature quantifying MRI tumor enhancement dynamics was independently predictive of recurrence-free survival (p=0.024). CONCLUSION Semi-automatically quantified tumor enhancement dynamics on MRI are predictive of recurrence-free survival in breast cancer patients.
This paper references
Prognostic value of pre - treatment DCEMRI parameters in predicting disease free and overall survival for breast cancer patients undergoing neoadjuvant chemotherapy
M. D. Pickles (2011)
10.1148/radiol.14132641
Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.
M. Mazurowski (2014)
10.1034/J.1600-0455.2003.00080.X
Can contrast-enhanced MR imaging predict survival in breast cancer?
B. Bóné (2003)
10.1118/1.1695652
Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics.
W. Chen (2004)
10.1097/SLA.0b013e31827a8d17
Preoperative Magnetic Resonance Imaging in Breast Cancer: Meta-Analysis of Surgical Outcomes
N. Houssami (2013)
10.1109/91.413225
On cluster validity for the fuzzy c-means model
N. Pal (1995)
10.1016/j.ejrad.2008.05.007
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)
10.1148/radiol.09090838
Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers.
N. Bhooshan (2010)
Computer assisted analysis of MR-mammography reveals association between contrast enhancement and occurrence of distant metastasis, Technol
P. A. Baltzer (2012)
10.2217/fon.12.186
The case against routine preoperative breast MRI.
I. Jatoi (2013)
10.1001/jamasurg.2013.3393
Breast cancer: a review for the general surgeon.
C. Matsen (2013)
10.1148/radiol.11102493
Use of dynamic contrast-enhanced MR imaging to predict survival in patients with primary breast cancer undergoing neoadjuvant chemotherapy.
S. Li (2011)
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)
10.1148/radiol.13121801
Survival outcomes of breast cancer patients who receive neoadjuvant chemotherapy: association with dynamic contrast-enhanced MR imaging with computer-aided evaluation.
A. Yi (2013)
10.1002/jmri.23812
Association between survival in patients with primary invasive breast cancer and computer aided MRI
M. Dietzel (2013)
10.7785/tcrt.2012.500266
Computer Assisted Analysis of MR-Mammography Reveals Association between Contrast Enhancement and Occurrence of Distant Metastasis
P. Baltzer (2012)
10.1002/jmri.21778
Predicting survival and early clinical response to primary chemotherapy for patients with locally advanced breast cancer using DCE‐MRI
Roar Johansen (2009)
10.1016/j.ejrad.2011.02.021
Prognostic value DCE-MRI parameters in predicting factor disease free survival and overall survival for breast cancer patients.
N. Tuncbilek (2012)
10.1001/JAMA.280.14.1216
Breast Cancer Survival
R. Voelker (1998)



This paper is referenced by
10.1007/S12530-019-09297-2
Survey of deep learning in breast cancer image analysis
Taye Girma Debelee (2020)
Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images
J. Zhang (2018)
10.1109/TMI.2018.2865671
Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics
J. Zhang (2019)
10.1186/s40644-019-0233-5
Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling
A. C. Yeh (2019)
10.1111/tbj.12799
Relationships Between MRI Breast Imaging‐Reporting and Data System (BI‐RADS) Lexicon Descriptors and Breast Cancer Molecular Subtypes: Internal Enhancement is Associated with Luminal B Subtype
L. Grimm (2017)
10.1016/j.crad.2020.07.030
Heterogeneity of enhancement kinetics in dynamic contrast-enhanced MRI and implication of distant metastasis in invasive breast cancer.
R. Zhao (2020)
10.1002/jmri.26762
Background parenchymal enhancement on breast MRI: A comprehensive review
G. Liao (2019)
10.1038/s41416-018-0185-8
A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features
Ashirbani Saha (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)
10.1148/radiol.2017162079
Computer-aided Diagnosis-generated Kinetic Features of Breast Cancer at Preoperative MR Imaging: Association with Disease-free Survival of Patients with Primary Operable Invasive Breast Cancer.
J. Kim (2017)
10.1002/jmri.26534
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)
10.1007/s00432-018-2595-7
A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models
Ashirbani Saha (2018)
10.1002/mp.12925
Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter‐reader variability in annotating tumors
Ashirbani Saha (2018)
10.3233/JIFS-179212
Research on the improvement of the diagnostic effect of machine learning on nuclear magnetic resonance of brain tumors
Shuyan Gao (2019)
10.1371/journal.pone.0234871
A rapid volume of interest-based approach of radiomics analysis of breast MRI for tumor decoding and phenotyping of breast cancer
Aydin Demircioglu (2020)
10.1016/j.eswa.2017.06.029
Effects of MRI scanner parameters on breast cancer radiomics
Ashirbani Saha (2017)
10.1002/jmri.26648
Association of distant recurrence‐free survival with algorithmically extracted MRI characteristics in breast cancer
M. Mazurowski (2019)
10.1002/jmri.26636
Machine learning‐based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high‐risk screening MRI
Ashirbani Saha (2019)
10.1117/12.2293207
Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: a large scale evaluation
Ashirbani Saha (2018)
10.1002/jmri.26852
Machine learning in breast MRI
B. Reig (2019)
10.1002/jmri.25116
Breast MRI radiogenomics: Current status and research implications
L. Grimm (2016)
Semantic Scholar Logo Some data provided by SemanticScholar