Literature DB >> 18572340

Prognostic value of pre-treatment DCE-MRI parameters in predicting disease free and overall survival for breast cancer patients undergoing neoadjuvant chemotherapy.

Martin D Pickles1, David J Manton, Martin Lowry, Lindsay W Turnbull.   

Abstract

The purpose of this study was to investigate whether dynamic contrast enhanced MRI (DCE-MRI) data, both pharmacokinetic and empirical, can predict, prior to neoadjuvant chemotherapy, which patients are likely to have a shorter disease free survival (DFS) and overall survival (OS) interval following surgery. Traditional prognostic parameters were also included in the survival analysis. Consequently, a comparison of the prognostic value could be made between all the parameters studied. MR examinations were conducted on a 1.5 T system in 68 patients prior to the initiation of neoadjuvant chemotherapy. DCE-MRI consisted of a fast spoiled gradient echo sequence acquired over 35 phases with a mean temporal resolution of 11.3s. Both pharmacokinetic and empirical parameters were derived from the DCE-MRI data. Kaplan-Meier survival plots were generated for each parameter and group comparisons were made utilising logrank tests. The results from the 54 patients entered into the univariate survival analysis demonstrated that traditional prognostic parameters (tumour grade, hormonal status and size), empirical parameters (maximum enhancement index, enhancement index at 30s, area under the curve and initial slope) and adjuvant therapies demonstrated significant differences in survival intervals. Further multivariate Cox regression survival analysis revealed that empirical enhancement parameters contributed the greatest prediction of both DFS and OS in the resulting models. In conclusion, this study has demonstrated that in patients who exhibit high levels of perfusion and vessel permeability pre-treatment, evidenced by elevated empirical DCE-MRI parameters, a significantly lower disease free survival and overall survival can be expected.

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Year:  2008        PMID: 18572340     DOI: 10.1016/j.ejrad.2008.05.007

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  42 in total

1.  The clinical value of dynamic contrast-enhanced MRI in differential diagnosis of malignant and benign ovarian lesions.

Authors:  Xian Li; Jun-Li Hu; Lai-Min Zhu; Xin-Hai Sun; Hua-Qiang Sheng; Ning Zhai; Xi-Bin Hu; Chu-Ran Sun; Bin Zhao
Journal:  Tumour Biol       Date:  2015-02-28

2.  Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer.

Authors:  Daniel I Golden; Jafi A Lipson; Melinda L Telli; James M Ford; Daniel L Rubin
Journal:  J Am Med Inform Assoc       Date:  2013-06-19       Impact factor: 4.497

3.  Using semi-quantitative dynamic contrast-enhanced magnetic resonance imaging parameters to evaluate tumor hypoxia: a preclinical feasibility study in a maxillofacial VX2 rabbit model.

Authors:  Linfeng Zheng; Yujie Li; Feng Geng; Sujuan Zheng; Ruiling Yan; Yuedong Han; Qiben Wang; Zhuoli Zhang; Guixiang Zhang
Journal:  Am J Transl Res       Date:  2015-03-15       Impact factor: 4.060

4.  Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer.

Authors:  Jia Wu; Bailiang Li; Xiaoli Sun; Guohong Cao; Daniel L Rubin; Sandy Napel; Debra M Ikeda; Allison W Kurian; Ruijiang Li
Journal:  Radiology       Date:  2017-07-14       Impact factor: 11.105

Review 5.  Functional MRI and CT biomarkers in oncology.

Authors:  J M Winfield; G S Payne; N M deSouza
Journal:  Eur J Nucl Med Mol Imaging       Date:  2015-01-13       Impact factor: 9.236

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

Authors:  Martin D Pickles; Martin Lowry; David J Manton; Lindsay W Turnbull
Journal:  Eur Radiol       Date:  2014-11-26       Impact factor: 5.315

7.  Predicting breast cancer response to neoadjuvant chemotherapy based on tumor vascular features in needle biopsies.

Authors:  Terisse A Brocato; Ursa Brown-Glaberman; Zhihui Wang; Reed G Selwyn; Colin M Wilson; Edward F Wyckoff; Lesley C Lomo; Jennifer L Saline; Anupama Hooda-Nehra; Renata Pasqualini; Wadih Arap; C Jeffrey Brinker; Vittorio Cristini
Journal:  JCI Insight       Date:  2019-03-05

8.  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
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

Review 9.  Imaging in clinical trials.

Authors:  P Murphy; D-M Koh
Journal:  Cancer Imaging       Date:  2010-10-04       Impact factor: 3.909

10.  Differentiation of tumor vasculature heterogeneity levels in small animals based on total hemoglobin concentration using magnetic resonance-guided diffuse optical tomography in vivo.

Authors:  Tiffany C Kwong; Mitchell Hsing; Yuting Lin; David Thayer; Mehmet Burcin Unlu; Min-Ying Su; Gultekin Gulsen
Journal:  Appl Opt       Date:  2016-07-20       Impact factor: 1.980

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