Please confirm you are human (Sign Up for free to never see this)
← Back to Search
Prognostic Value DCE-MRI Parameters In Predicting Factor Disease Free Survival And Overall Survival For Breast Cancer Patients.
N. Tuncbilek, F. Tokatli, S. Altaner, Atakan Sezer, M. Türe, I. Omurlu, O. Temizoz
Published 2012 · Medicine
Save to my Library
Download PDFAnalyze on Scholarcy
PURPOSE The aim of the study is to assess the predictive power of DCE-MRI semi-quantitative parameters during treatment of breast cancer, for disease-free (DFS) and overall survival (OS). MATERIALS AND METHODS Forty-nine women (age range, 28-84 years; mean, 50.6 years) with breast cancer underwent dynamic contrast enhancement MRI at 1.0T imaging, using 2D FLASH sequences. Time intensity curves (TICs) were obtained from the regions showing maximal enhancement in subtraction images. Semi-quantitative parameters (TICs; maximal relative enhancement within the first minute, E (max/1); maximal relative enhancement of the entire study, E(max); steepest slope of the contrast enhancement curve; and time to peak enhancement) derived from the DCE-MRI data. These parameters were then compared with presence of recurrence or metastasis, DFS and OS by using Cox regression (proportional hazards model) analysis, linear discriminant analysis. RESULTS The results from of the 49 patients enrolled into the survival analysis demonstrated that traditional prognostic parameters (tumor size and nodal metastasis) and semi-quantitative parameters (E(max/1), and steepest slope) demonstrated significant differences in survival intervals (p<0.05). Further Cox regression (proportional hazards model) survival analysis revealed that semi-quantitative parameters contributed the greatest prediction of both DFS, OS in the resulting models (for E(max/1): p=0.013, hazard ratio 1.022; for stepest slope: p=0.004, hazard ratio 1.584). CONCLUSION This study shows that DCE-MRI has utility predicting survival analysis with breast cancer patients.
This paper references
MR imaging of the breast for the detection, diagnosis, and staging of breast cancer.
S. Orel (2001)
Bladder tumor staging: comparison of contrast-enhanced CT, T1- and T2-weighted MR imaging, dynamic gadolinium-enhanced imaging, and late gadolinium-enhanced imaging.
B. Kim (1994)
Dynamic MR imaging of invasive breast cancer: correlation with tumour grade and other histological factors.
S. Mussurakis (1997)
Can contrast-enhanced MR imaging predict survival in breast cancer?
B. Bóné (2003)
Dynamic magnetic resonance imaging in determining histopathological prognostic factors of invasive breast cancers.
N. Tuncbilek (2005)
Seminars in Medicine of the Beth Israel Hospital, Boston. Clinical applications of research on angiogenesis.
J. Folkman (1995)
Breast lesions: correlation of contrast medium enhancement patterns on MR images with histopathologic findings and tumor angiogenesis.
L. Buadu (1996)
Angiogenesis of uterine cervical carcinoma: characterization by pharmacokinetic magnetic resonance parameters and histological microvessel density with correlation to lymphatic involvement.
H. Hawighorst (1997)
Update on locally advanced breast cancer.
S. Giordano (2003)
Adjuvant radiotherapy and chemotherapy in breast cancer: 30 year follow-up of survival
C. Mcardle (2010)
Adjuvant radiotherapy and chemotherapy in breast cancer
C. Mcardle (1986)
Angiogenic activity of cervical carcinoma: assessment by functional magnetic resonance imaging-based parameters and a histomorphological approach in correlation with disease outcome.
H. Hawighorst (1998)
Contrast-enhanced MR imaging as a prognostic indicator of breast cancer
B. Bóné (1998)
Can dynamic contrast-enhanced MRI (DCE-MRI) predict tumor recurrence and lymph node status in patients with breast cancer?
S. Bahri (2008)
Predicting Control of Primary Tumor and Survival by DCE MRI During Early Therapy in Cervical Cancer
W. Yuh (2009)
High grade and non-high grade ductal carcinoma in situ on dynamic MR mammography: characteristic findings for signal increase and morphological pattern of enhancement.
H. Neubauer (2003)
Contrast-enhanced high-resolution MRI of invasive breast cancer: correlation with histopathologic subtypes.
K. Kitagawa (2004)
Dynamic magnetic resonance imaging of regional contrast access as an additional prognostic factor in pediatric osteosarcoma
W. Reddick (2001)
Relationship among outcome, stage of disease, and histologic grade for 22,616 cases of breast cancer. The basis for a prognostic index
D. Henson (1991)
MR imaging characterization of microvessels in experimental breast tumors by using a particulate contrast agent with histopathologic correlation.
K. Turetschek (2001)
Clinical Applications of Research on Angiogenesis
J. Folkman (1995)
Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases
C. Carter (1989)
R. Rosenfeld (2010)
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)
Tumor angiogenesis and metastasis--correlation in invasive breast carcinoma.
N. Weidner (1991)
Dynamic-enhanced MRI predicts metastatic potential of invasive ductal breast cancer
T. Nagashima (2002)
Correlation between numeric gadolinium-enhanced dynamic MRI ratios and prognostic factors and histologic type of breast carcinoma
成定 宏之 (2006)
Local recurrences and distant metastases after breast-conserving surgery and radiation therapy for early breast cancer.
E. Touboul (1999)
New horizons in the diagnosis and treatment of breast cancer using magnetic resonance imaging.
M. Cross (1993)
Invasive breast cancer: correlation of dynamic MR features with prognostic factors
B. Szabó (2003)
Dynamic breast MR imaging: are signal time course data useful for differential diagnosis of enhancing lesions? Radiology 1999;211:101–10
CK Kuhl (1999)
Bone marrow angiogenesis magnetic resonance imaging in patients with acute myeloid leukemia: peak enhancement ratio is an independent predictor for overall survival.
T. T. Shih (2009)
Long-term follow-up of the Stockholm randomized trials of postoperative radiation therapy versus adjuvant chemotherapy among ‘high risk’ pre- and postmenopausal breast cancer patients
L. Rutqvist (2006)
Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions?
C. Kuhl (1999)
Correlation Between Contrast Enhancement in Dynamic Magnetic Resonance Imaging of the Breast and Tumor Angiogenesis
C. Frouge (1994)
This paper is referenced by
The value of DCE-MRI in assessing histopathological and molecular biological features in induced rat epithelial ovarian carcinomas
Su Juan Yuan (2017)
Spatiotemporal features of DCE-MRI for breast cancer diagnosis
Masood Banaie (2018)
Dynamic contrast-enhanced magnetic resonance imaging for characterising nasopharyngeal carcinoma: comparison of semiquantitative and quantitative parameters and correlation with tumour stage
Bingsheng Huang (2012)
Prognostic value DCE-MRI parameters in predicting factor disease free survival and overall survival for breast cancer patients
M. Su (2013)
Recurrence-free survival in breast cancer is associated with MRI tumor enhancement dynamics quantified using computer algorithms.
M. Mazurowski (2015)
Intravoxel incoherent motion diffusion‐weighted MRI of invasive breast cancer: Correlation with prognostic factors and kinetic features acquired with computer‐aided diagnosis
S. Song (2019)
DCE-MRI in assessment of tumor hypoxia, radiation response, and metastatic potential
Kirsti Marie Øvrebø (2013)
MR-Derived Biomarkers for Cancer Characterization
Eugene Kim (2017)
Comparing the Performances of Magnetic Resonance Imaging Size vs Pharmacokinetic Parameters to Predict Response to Neoadjuvant Chemotherapy and Survival in Patients With Breast Cancer.
B. Dogan (2019)
Statistical Learning Algorithm for in situ and invasive breast carcinoma segmentation
J. Jayender (2013)
Feasibility of contrast-enhanced MRI derived textural features to predict overall survival in locally advanced breast cancer
Ioanna Chronaiou (2019)
Prognostic value of DCE-MRI in breast cancer patients undergoing neoadjuvant chemotherapy: a comparison with traditional survival indicators
M. Pickles (2014)
Nonmodel-based Dynamic Contrast-enhanced Magnetic Resonance Imaging for the Assessment of High versus Low Risk Carotid Atherosclerosis
David B. Maclean (2011)
Abbreviated breast MRI combining FAST protocol and high temporal resolution (HTR) dynamic contrast enhanced (DCE) sequence.
Audrey Milon (2019)
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)
Research and applications: Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer
D. I. Golden (2013)
A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection.
R. Vairavan (2019)
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)
Kinetic volume analysis on dynamic Contrast-enhanced MRI of Triple-Negative breast cancer: Associations with survival outcomes.
Yoko Hayashi (2019)
Heterogeneity in intratumoral regions with rapid gadolinium washout correlates with estrogen receptor status and nodal metastasis
B. Chaudhury (2015)
Incorporating prognostic imaging biomarkers into clinical practice
W. P. Law (2013)
Preoperative dynamic breast magnetic resonance imaging kinetic features using computer-aided diagnosis: Association with survival outcome and tumor aggressiveness in patients with invasive breast cancer
S. Y. Nam (2018)
Models and methods for analyzing DCE-MRI: a review.
F. Khalifa (2014)