Literature DB >> 17729334

MRI measurements of tumor size and pharmacokinetic parameters as early predictors of response in breast cancer patients undergoing neoadjuvant anthracycline chemotherapy.

Hon J Yu1, Jeon-Hor Chen, Rita S Mehta, Orhan Nalcioglu, Min-Ying Su.   

Abstract

PURPOSE: To investigate the value of using changes in three parameters (tumor size, transfer constant (K(trans)), and rate constant (k(ep))) obtained after the first treatment-cycle in predicting the final clinical response after two to four cycles of neoadjuvant anthracycline and cyclophosphamide (AC) chemotherapy.
MATERIALS AND METHODS: Early changes in the three parameters were measured in 29 patients with invasive breast cancer by MRI after one cycle of treatment. Changes were then assessed for their predictive value of final clinical response and compared among patients with four different response patterns, Group 1 = responder (R) after one cycle and also R after four cycles, Group 2 = nonresponder (NR) after one cycle, but eventual R after four cycles, Group 3 = NR after one cycle and still NR after four cycles, and Group 4 = NR after one cycle and determined as NR after two cycles, being switched to the taxane regimen.
RESULTS: Pearson's correlation analysis revealed significant correlation between early changes in tumor size and both pharmacokinetic parameters (r = 0.49 and P < 0.01 for K(trans), r = 0.66 and P < 0.001 for k(ep)). The areas under the receiver operating characteristic (ROC) curve differentiating between R (Groups 1+2) and NR (Groups 3+4) groups using changes in tumor size, K(trans), and k(ep) were 0.88 (standard error [SE] = 0.06, P < 0.0001), 0.63 (SE = 0.11, P = 0.11), and 0.77 (SE = 0.09, P = 0.001), respectively.
CONCLUSION: Early tumor size change in MRI after one cycle is better response predictor than that of either K(trans) or k(ep) in breast cancer undergoing neoadjuvant chemotherapy using an AC regimen. (c) 2007 Wiley-Liss, Inc.

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Year:  2007        PMID: 17729334     DOI: 10.1002/jmri.21060

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


  39 in total

1.  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
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2.  Diffusion-weighted MRI in pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer.

Authors:  Raphael Richard; Isabelle Thomassin; Marion Chapellier; Aurélie Scemama; Patricia de Cremoux; Mariana Varna; Sylvie Giacchetti; Marc Espié; Eric de Kerviler; Cedric de Bazelaire
Journal:  Eur Radiol       Date:  2013-05-08       Impact factor: 5.315

Review 3.  Pre-treatment differences and early response monitoring of neoadjuvant chemotherapy in breast cancer patients using magnetic resonance imaging: a systematic review.

Authors:  R Prevos; M L Smidt; V C G Tjan-Heijnen; M van Goethem; R G Beets-Tan; J E Wildberger; M B I Lobbes
Journal:  Eur Radiol       Date:  2012-09-16       Impact factor: 5.315

4.  Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging.

Authors:  Imon Banerjee; Sadhika Malladi; Daniela Lee; Adrien Depeursinge; Melinda Telli; Jafi Lipson; Daniel Golden; Daniel L Rubin
Journal:  J Med Imaging (Bellingham)       Date:  2017-11-02

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

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6.  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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7.  Is there any correlation between model-based perfusion parameters and model-free parameters of time-signal intensity curve on dynamic contrast enhanced MRI in breast cancer patients?

Authors:  Boram Yi; Doo Kyoung Kang; Dukyong Yoon; Yong Sik Jung; Ku Sang Kim; Hyunee Yim; Tae Hee Kim
Journal:  Eur Radiol       Date:  2014-02-21       Impact factor: 5.315

8.  Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.

Authors:  Amirhessam Tahmassebi; Georg J Wengert; Thomas H Helbich; Zsuzsanna Bago-Horvath; Sousan Alaei; Rupert Bartsch; Peter Dubsky; Pascal Baltzer; Paola Clauser; Panagiotis Kapetas; Elizabeth A Morris; Anke Meyer-Baese; Katja Pinker
Journal:  Invest Radiol       Date:  2019-02       Impact factor: 6.016

9.  Current and future trends in magnetic resonance imaging assessments of the response of breast tumors to neoadjuvant chemotherapy.

Authors:  Lori R Arlinghaus; Xia Li; Mia Levy; David Smith; E Brian Welch; John C Gore; Thomas E Yankeelov
Journal:  J Oncol       Date:  2010-09-29       Impact factor: 4.375

10.  Predicting pathologic response to neoadjuvant chemotherapy in breast cancer by using MR imaging and quantitative 1H MR spectroscopy.

Authors:  Hyeon-Man Baek; Jeon-Hor Chen; Ke Nie; Hon J Yu; Shadfar Bahri; Rita S Mehta; Orhan Nalcioglu; Min-Ying Su
Journal:  Radiology       Date:  2009-03-10       Impact factor: 11.105

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