Literature DB >> 21502383

Use of dynamic contrast-enhanced MR imaging to predict survival in patients with primary breast cancer undergoing neoadjuvant chemotherapy.

Sonia P Li1, Andreas Makris, Mark J Beresford, N Jane Taylor, Mei-Lin W Ah-See, J James Stirling, James A d'Arcy, David J Collins, Robert Kozarski, Anwar R Padhani.   

Abstract

PURPOSE: To investigate whether early changes in vascular parameters determined with dynamic contrast material-enhanced magnetic resonance (MR) imaging after two cycles of neoadjuvant chemotherapy (NAC) are predictive of disease-free and overall survival in primary breast cancer.
MATERIALS AND METHODS: Institutional ethics approval and informed consent were obtained. Patients with primary breast cancer (median age, 45 years; age range, 22-70 years) recruited from January 2001 to September 2008 underwent dynamic contrast-enhanced MR imaging before and after two cycles of NAC. Quantitative and semiquantitative kinetic parameters were calculated, including the volume transfer constant (K(trans)) and the initial area under the gadolinium concentration-time curve over 60 seconds (IAUGC(60)). Cut points optimized to the receiver operating characteristic curve were used to dichotomize MR imaging data for Kaplan-Meier survival analysis. MR imaging parameters and known prognostic indicators in primary breast cancer were correlated with disease-free and overall survival by using the Cox proportional hazards model for univariate and multivariate analyses.
RESULTS: MR imaging was performed before (n = 62) and after (n = 58) two cycles of NAC. The median follow-up time was 43.9 months for disease-free survival and 60.3 months for overall survival. There were 28 recurrences; 26 patients had distant metastases (two had additional local recurrence) and two had local recurrence only. There were 20 deaths, all of which were related to breast cancer. At univariate analysis, progesterone receptor status, the type of surgery performed, higher posttreatment K(trans) (P = .048), and larger posttreatment IAUGC(60) (P = .035) were significant predictors of worse disease-free survival. At multivariate analysis, progesterone receptor status (P = .002) and mean transit time (P = .025) were significant predictors of disease-free survival. Univariate analysis showed that clinical tumor stage (P = .005), progesterone receptor status (P = .025), and type of surgery performed (P = .017) were significant predictors of overall survival. Higher posttreatment K(trans) (P = .043), larger IAUGC(60) (P = .029), and larger tumor size at posttreatment MR imaging were predictive of worse overall survival (P = .018). Of these variables, K(trans) remained an independent indicator of overall survival (P = .038).
CONCLUSION: Higher posttreatment tumor vascularization as depicted with dynamic contrast-enhanced MR imaging may be associated with higher recurrence and lower survival rates. Dynamic contrast-enhanced MR imaging parameters, in conjunction with traditional prognostic factors, have the potential to be prognostic biomarkers for disease-free and overall survival in primary breast cancer.

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Year:  2011        PMID: 21502383     DOI: 10.1148/radiol.11102493

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  36 in total

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2.  Prognostic value of DCE-MRI in breast cancer patients undergoing neoadjuvant chemotherapy: a comparison with traditional survival indicators.

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4.  Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results.

Authors:  Hakmook Kang; Allison Hainline; Lori R Arlinghaus; Stephanie Elderidge; Xia Li; Vandana G Abramson; Anuradha Bapsi Chakravarthy; Richard G Abramson; Brian Bingham; Kareem Fakhoury; Thomas E Yankeelov
Journal:  J Med Imaging (Bellingham)       Date:  2017-12-29

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6.  The combination of FDG PET and dynamic contrast-enhanced MRI improves the prediction of disease-free survival in patients with advanced breast cancer after the first cycle of neoadjuvant chemotherapy.

Authors:  Ilhan Lim; Woo Chul Noh; Jihyun Park; Ji Ae Park; Hyun-Ah Kim; Eun-Kyu Kim; Ko Woon Park; Seung Sook Lee; Eun Young You; Kyeong Min Kim; Byung Hyun Byun; Byung Ii Kim; Chang Woon Choi; Sang Moo Lim
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7.  Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer.

Authors:  Xia Li; Hakmook Kang; Lori R Arlinghaus; Richard G Abramson; A Bapsi Chakravarthy; Vandana G Abramson; Jaime Farley; Melinda Sanders; Thomas E Yankeelov
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9.  Prevalence of extramammary findings on breast MRI: a large retrospective single-centre study.

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10.  Arterial input functions in dynamic contrast-enhanced magnetic resonance imaging: which model performs best when assessing breast cancer response?

Authors:  David K Woolf; N Jane Taylor; Andreas Makris; Nina Tunariu; David J Collins; Sonia P Li; Mei-Lin Ah-See; Mark Beresford; Anwar R Padhani
Journal:  Br J Radiol       Date:  2016-05-17       Impact factor: 3.039

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