Literature DB >> 22002757

Diffusion-weighted and dynamic contrast-enhanced MRI in evaluation of early treatment effects during neoadjuvant chemotherapy in breast cancer patients.

Line R Jensen1, Benjamin Garzon, Mariann G Heldahl, Tone F Bathen, Steinar Lundgren, Ingrid S Gribbestad.   

Abstract

PURPOSE: To use dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) MRI at 3 Tesla (T) for early evaluation of treatment effects in breast cancer patients undergoing neoadjuvant chemotherapy (NAC), and assess the reliability of DW-MRI.
MATERIALS AND METHODS: DW- and DCE-MRI acquisitions of 15 breast cancer patients were performed before and after one cycle of NAC. MRI tumor diameter and volume, apparent diffusion coefficient (ADC) and kinetic parameters (K(trans), v(e)) were derived. The reliability of ADC before NAC was assessed. Changes in MRI parameters after NAC were analyzed, and logistic regression analysis was used to find the best predictors for pathologic response.
RESULTS: The reliability for ADC values was high, with intraclass correlation coefficient of 0.84 (P = 0.001). After one cycle of NAC, MRI tumor diameter (8%, P = 0.005) and tumor volume (30%, P = 0.008) was reduced for all patients, while ADC mean values increased (0.12 mm(2)/s, P = 0.008). The best predictor for treatment response was a change in MRI tumor diameter with mean error rate of 0.167 (13% for responders, 5% for nonresponders, P = 0.291).
CONCLUSION: Changes in MRI derived tumor diameter and ADC after only one cycle of NAC could provide a valuable tool for early evaluation of treatment effects in breast cancer patients.
Copyright © 2011 Wiley Periodicals, Inc.

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Year:  2011        PMID: 22002757     DOI: 10.1002/jmri.22726

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  33 in total

1.  Rapid dramatic alterations to the tumor microstructure in pancreatic cancer following irreversible electroporation ablation.

Authors:  Zhuoli Zhang; Weiguo Li; Daniel Procissi; Patrick Tyler; Reed A Omary; Andrew C Larson
Journal:  Nanomedicine (Lond)       Date:  2013-09-11       Impact factor: 5.307

2.  Longitudinal, intermodality registration of quantitative breast PET and MRI data acquired before and during neoadjuvant chemotherapy: preliminary results.

Authors:  Nkiruka C Atuegwu; Xia Li; Lori R Arlinghaus; Richard G Abramson; Jason M Williams; A Bapsi Chakravarthy; Vandana G Abramson; Thomas E Yankeelov
Journal:  Med Phys       Date:  2014-05       Impact factor: 4.071

3.  Multicenter Repeatability Study of a Novel Quantitative Diffusion Kurtosis Imaging Phantom.

Authors:  Dariya I Malyarenko; Scott D Swanson; Amaresha S Konar; Eve LoCastro; Ramesh Paudyal; Michael Z Liu; Sachin R Jambawalikar; Lawrence H Schwartz; Amita Shukla-Dave; Thomas L Chenevert
Journal:  Tomography       Date:  2019-03

4.  [Therapy monitoring of neoadjuvant therapy with MRI. RECIST and functional imaging].

Authors:  S Grandl; M Ingrisch; K Hellerhoff
Journal:  Radiologe       Date:  2014-03       Impact factor: 0.635

5.  Assessing reproducibility of diffusion-weighted magnetic resonance imaging studies in a murine model of HER2+ breast cancer.

Authors:  Jennifer G Whisenant; Gregory D Ayers; Mary E Loveless; Stephanie L Barnes; Daniel C Colvin; Thomas E Yankeelov
Journal:  Magn Reson Imaging       Date:  2013-12-14       Impact factor: 2.546

Review 6.  Functional MR Imaging Techniques in Oncology in the Era of Personalized Medicine.

Authors:  Matthias R Benz; Hebert Alberto Vargas; Evis Sala
Journal:  Magn Reson Imaging Clin N Am       Date:  2015-09-26       Impact factor: 2.266

Review 7.  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

8.  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

Review 9.  Diffusion MRI in early cancer therapeutic response assessment.

Authors:  C J Galbán; B A Hoff; T L Chenevert; B D Ross
Journal:  NMR Biomed       Date:  2016-01-15       Impact factor: 4.044

10.  Parameterizing the Logistic Model of Tumor Growth by DW-MRI and DCE-MRI Data to Predict Treatment Response and Changes in Breast Cancer Cellularity during Neoadjuvant Chemotherapy.

Authors:  Nkiruka C Atuegwu; Lori R Arlinghaus; Xia Li; A Bapsi Chakravarthy; Vandana G Abramson; Melinda E Sanders; Thomas E Yankeelov
Journal:  Transl Oncol       Date:  2013-06-01       Impact factor: 4.243

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