Literature DB >> 21929557

Diffusion-weighted imaging in evaluating the response to neoadjuvant breast cancer treatment.

Paolo Belli1, Melania Costantini, Carmine Ierardi, Enida Bufi, Daniele Amato, Antonino Mule', Luigia Nardone, Daniela Terribile, Lorenzo Bonomo.   

Abstract

The aim of this study was to investigate the role of diffusion imaging in the evaluation of response to neoadjuvant breast cancer treatment by correlating apparent diffusion coefficient (ADC) value changes with pathological response. From June 2007 to June 2009, all consecutive patients with histopathologically confirmed breast cancer undergoing neoadjuvant chemotherapy were enrolled. All patients underwent magnetic resonance imaging (MRI) (including diffusion sequence) before and after neoadjuvant treatment. The ADC values obtained using two different methods of region of interest (ROI) placement before and after treatment were compared with MRI response (assessed using RECIST 1.1 criteria) and pathological response (assessed using Mandard's classification). Fifty-one women (mean age 48.41 years) were included in this study. Morphological MRI (RECIST classification) well evaluated the responder status after chemotherapy (TRG class; area-under-the-curve 0.865). Mean pretreatment ADC values obtained with the two different methods of ROI placement were 1.11 and 1.02 × 10(-3) mm(2) /seconds. Mean post-treatment ADC values were 1.40 and 1.35 × 10(-3) mm(2) /seconds, respectively. A significant inverse correlation between mean ADC increase and Mandard's classifications was observed for both the methods of ADC measurements. Diagnostic performance analysis revealed that the single ROI method has a superior diagnostic accuracy compared with the multiple ROIs method (accuracy: 82% versus 74%). The coupling of the diffusion imaging with the established morphological MRI provides superior evaluation of response to neoadjuvant chemotherapy treatment in breast cancer patients compared with morphological MRI alone. There is a potential in the future to optimize patient therapy on the basis of ADC value changes. Additional works are needed to determine whether these preliminary observed changes in tumor diffusion are a universal response to tumor cell death, and to more fully delineate the ability of ADC value changes in early recognizing responder from nonresponder patients.
© 2011 Wiley Periodicals, Inc.

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Year:  2011        PMID: 21929557     DOI: 10.1111/j.1524-4741.2011.01160.x

Source DB:  PubMed          Journal:  Breast J        ISSN: 1075-122X            Impact factor:   2.431


  27 in total

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Authors:  Xia Li; Richard G Abramson; Lori R Arlinghaus; Hakmook Kang; Anuradha Bapsi Chakravarthy; Vandana G Abramson; Jaime Farley; Ingrid A Mayer; Mark C Kelley; Ingrid M Meszoely; Julie Means-Powell; Ana M Grau; Melinda Sanders; Thomas E Yankeelov
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3.  Sequence design and evaluation of the reproducibility of water-selective diffusion-weighted imaging of the breast at 3 T.

Authors:  He Zhu; Lori R Arlinghaus; Jennifer G Whisenant; Ming Li; John C Gore; Thomas E Yankeelov
Journal:  NMR Biomed       Date:  2014-07-01       Impact factor: 4.044

4.  Comparison of two radiation techniques for the breast boost in patients undergoing neoadjuvant treatment for breast cancer.

Authors:  Maria C De Santis; Luigia Nardone; Barbara Diletto; Roberta Canna; Michela Dispinzieri; Lorenza Marino; Laura Lozza; Vincenzo Valentini
Journal:  Br J Radiol       Date:  2016-07-25       Impact factor: 3.039

5.  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

6.  Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features.

Authors:  Lakshmanan Sannachi; Mehrdad Gangeh; Hadi Tadayyon; Ali Sadeghi-Naini; Sonal Gandhi; Frances C Wright; Elzbieta Slodkowska; Belinda Curpen; William Tran; Gregory J Czarnota
Journal:  PLoS One       Date:  2018-01-03       Impact factor: 3.240

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

8.  Diffusion magnetic resonance imaging in breast cancer characterisation: correlations between the apparent diffusion coefficient and major prognostic factors.

Authors:  Paolo Belli; Melania Costantini; Enida Bufi; Giuseppe Giovanni Giardina; Pierluigi Rinaldi; Gianluca Franceschini; Lorenzo Bonomo
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Review 9.  MR Imaging Biomarkers in Oncology Clinical Trials.

Authors:  Richard G Abramson; Lori R Arlinghaus; Adrienne N Dula; C Chad Quarles; Ashley M Stokes; Jared A Weis; Jennifer G Whisenant; Eduard Y Chekmenev; Igor Zhukov; Jason M Williams; Thomas E Yankeelov
Journal:  Magn Reson Imaging Clin N Am       Date:  2016-02       Impact factor: 2.266

10.  Current and emerging quantitative magnetic resonance imaging methods for assessing and predicting the response of breast cancer to neoadjuvant therapy.

Authors:  Richard G Abramson; Lori R Arlinghaus; Jared A Weis; Xia Li; Adrienne N Dula; Eduard Y Chekmenev; Seth A Smith; Michael I Miga; Vandana G Abramson; Thomas E Yankeelov
Journal:  Breast Cancer (Dove Med Press)       Date:  2012-10
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