Literature DB >> 15446790

Angiogenic response of locally advanced breast cancer to neoadjuvant chemotherapy evaluated with parametric histogram from dynamic contrast-enhanced MRI.

Yeun-Chung Chang1, Chiun-Sheng Huang, Yi-Jui Liu, Jyh-Horng Chen, Yen-Shen Lu, Wen-Yih I Tseng.   

Abstract

The aim of this study was to evaluate angiogenic compositions and tumour response in the course of neoadjuvant chemotherapy in patients with locally advanced breast cancer (LABC) using dynamic contrast-enhanced (DCE) MRI. Thirteen patients with LABC underwent serial DCE MRI during the course of chemotherapy. DCE MRI was quantified using a two-compartment model on a pixel-by-pixel basis. Analysis of parametric histograms of amplitude, exchange rate k(out) and peak enhancement over the whole tumour was performed. The distribution patterns of histograms were correlated with the tumour response. Initial kurtosis and standard deviation of amplitude before chemotherapy correlated with tumour response, r = 0.63 and r = 0.61, respectively. Comparing the initial values with the values after the first course of chemotherapy, tumour response was associated with a decrease in standard deviation of amplitude (r = 0.79), and an increase in kurtosis and a decrease in standard deviation of k(out) (r = 0.57 and 0.57, respectively). Comparing the initial values with the values after completing the chemotherapy, tumours with better response were associated with an increase in kurtosis (r = 0.62), a decrease in mean (r = 0.84) and standard deviation (r = 0.77) of amplitude, and a decrease in mean of peak enhancement (r = 0.71). Our results suggested that tumours with better response tended to alter their internal compositions from heterogeneous to homogeneous distributions and a decrease in peak enhancement after chemotherapy. Serial analyses of parametric histograms of DCE MRI-derived angiogenic parameters are potentially useful to monitor the response of angiogenic compositions of a tumour throughout the course of chemotherapy, and might predict tumour response early in the course.

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Year:  2004        PMID: 15446790     DOI: 10.1088/0031-9155/49/16/007

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  24 in total

1.  Quantitative DCE-MRI for prediction of pathological complete response following neoadjuvant treatment for locally advanced breast cancer: the impact of breast cancer subtypes on the diagnostic accuracy.

Authors:  Stylianos Drisis; Thierry Metens; Michael Ignatiadis; Konstantinos Stathopoulos; Shih-Li Chao; Marc Lemort
Journal:  Eur Radiol       Date:  2015-08-27       Impact factor: 5.315

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.  Radiomics: a new application from established techniques.

Authors:  Vishwa Parekh; Michael A Jacobs
Journal:  Expert Rev Precis Med Drug Dev       Date:  2016-03-31

4.  Prognostic imaging in neoadjuvant chemotherapy of locally-advanced breast cancer should be cost-effective.

Authors:  Marc Schegerin; Anna N A Tosteson; Peter A Kaufman; Keith D Paulsen; Brian W Pogue
Journal:  Breast Cancer Res Treat       Date:  2008-04-25       Impact factor: 4.872

5.  Evaluation of the effect of transcytolemmal water exchange analysis for therapeutic response assessment using DCE-MRI: a comparison study.

Authors:  Chunhao Wang; Ergys Subashi; Xiao Liang; Fang-Fang Yin; Zheng Chang
Journal:  Phys Med Biol       Date:  2016-06-08       Impact factor: 3.609

6.  Radiomic analysis of HTR-DCE MR sequences improves diagnostic performance compared to BI-RADS analysis of breast MR lesions.

Authors:  Saskia Vande Perre; Loïc Duron; Audrey Milon; Asma Bekhouche; Daniel Balvay; Francois H Cornelis; Laure Fournier; Isabelle Thomassin-Naggara
Journal:  Eur Radiol       Date:  2021-01-06       Impact factor: 5.315

7.  Dynamic fractal signature dissimilarity analysis for therapeutic response assessment using dynamic contrast-enhanced MRI.

Authors:  Chunhao Wang; Ergys Subashi; Fang-Fang Yin; Zheng Chang
Journal:  Med Phys       Date:  2016-03       Impact factor: 4.071

8.  Histogram analysis of DCE-MRI for chemoradiotherapy response evaluation in locally advanced esophageal squamous cell carcinoma.

Authors:  Na-Na Sun; Xiao-Lin Ge; Xi-Sheng Liu; Lu-Lu Xu
Journal:  Radiol Med       Date:  2019-10-11       Impact factor: 3.469

9.  Statistical comparison of dynamic contrast-enhanced MRI pharmacokinetic models in human breast cancer.

Authors:  Xia Li; E Brian Welch; A Bapsi Chakravarthy; Lei Xu; Lori R Arlinghaus; Jaime Farley; Ingrid A Mayer; Mark C Kelley; Ingrid M Meszoely; Julie Means-Powell; Vandana G Abramson; Ana M Grau; John C Gore; Thomas E Yankeelov
Journal:  Magn Reson Med       Date:  2011-11-29       Impact factor: 4.668

10.  DCE-MRI parameters have potential to predict response of locally advanced breast cancer patients to neoadjuvant chemotherapy and hyperthermia: a pilot study.

Authors:  Oana I Craciunescu; Kimberly L Blackwell; Ellen L Jones; James R Macfall; Daohai Yu; Zeljko Vujaskovic; Terence Z Wong; Vlayka Liotcheva; Eric L Rosen; Leonard R Prosnitz; Thaddeus V Samulski; Mark W Dewhirst
Journal:  Int J Hyperthermia       Date:  2009       Impact factor: 3.914

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