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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

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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)
Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.
M. Mazurowski (2014)
Can contrast-enhanced MR imaging predict survival in breast cancer?
B. Bóné (2003)
Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics.
W. Chen (2004)
Preoperative Magnetic Resonance Imaging in Breast Cancer: Meta-Analysis of Surgical Outcomes
N. Houssami (2013)
On cluster validity for the fuzzy c-means model
N. Pal (1995)
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)
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)
The case against routine preoperative breast MRI.
I. Jatoi (2013)
Breast cancer: a review for the general surgeon.
C. Matsen (2013)
Use of dynamic contrast-enhanced MR imaging to predict survival in patients with primary breast cancer undergoing neoadjuvant chemotherapy.
S. Li (2011)
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)
Survival outcomes of breast cancer patients who receive neoadjuvant chemotherapy: association with dynamic contrast-enhanced MR imaging with computer-aided evaluation.
A. Yi (2013)
Association between survival in patients with primary invasive breast cancer and computer aided MRI
M. Dietzel (2013)
Computer Assisted Analysis of MR-Mammography Reveals Association between Contrast Enhancement and Occurrence of Distant Metastasis
P. Baltzer (2012)
Predicting survival and early clinical response to primary chemotherapy for patients with locally advanced breast cancer using DCE‐MRI
Roar Johansen (2009)
Prognostic value DCE-MRI parameters in predicting factor disease free survival and overall survival for breast cancer patients.
N. Tuncbilek (2012)
Breast Cancer Survival
R. Voelker (1998)

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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
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Heterogeneity of enhancement kinetics in dynamic contrast-enhanced MRI and implication of distant metastasis in invasive breast cancer.
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Background parenchymal enhancement on breast MRI: A comprehensive review
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A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features
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Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics.
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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.
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Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
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A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models
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Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: a large scale evaluation
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Machine learning in breast MRI
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Breast MRI radiogenomics: Current status and research implications
L. Grimm (2016)
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