Literature DB >> 16543585

Prediction of clinicopathologic response of breast cancer to primary chemotherapy at contrast-enhanced MR imaging: initial clinical results.

Anwar R Padhani1, Carmel Hayes, Laura Assersohn, Trevor Powles, Andreas Makris, John Suckling, Martin O Leach, Janet E Husband.   

Abstract

PURPOSE: To prospectively document changes in contrast agent kinetics in patients with primary breast cancer treated with systemic chemotherapy after one or two cycles and to determine whether kinetic measures can be used to predict final clinicopathologic response.
MATERIALS AND METHODS: Institutional committees on clinical research and ethics approval and patient consent were obtained. Dynamic magnetic resonance (MR) examinations were performed in 25 women with primary breast cancer before treatment and after the first (n = 21) and second (n = 15) cycle of neoadjuvant chemotherapy. Kinetic parameters (transfer constant, leakage space, and rate constant) were derived for whole tumor regions of interest. Changes in histogram distributions of pixel data (median value and range) and MR imaging-derived size were correlated with final clinical and histologic response by using nonparametric methods. Receiver operating characteristic (ROC) analysis of tumor size and transfer constant changes were used to identify patients in whom no benefit was gained from chemotherapy.
RESULTS: After the first cycle of treatment, 12 of 14 clinical responders showed decreases in tumor size, and six of seven nonresponders showed increases or no change in tumor size (P < .001). Transfer constant changes did not differ between responders and nonresponders for either clinical or pathologic assessments. After two cycles of treatment, there were tumor size increases in five of six nonresponders compared with decreases in eight of nine responders (P < .001). Reductions in transfer constant range were also observed in responders for both clinical and pathologic assessments (P = .008 and .02, respectively). No other kinetic parameter change predicted response. Size and transfer constant range were equally accurate for predicting the absence of pathologic response after two cycles of treatment (sensitivity, specificity, and area under ROC curve were 100%, 90%, and 0.93, respectively, for size and 100%, 75%, and 0.94, respectively, for transfer constant range).
CONCLUSION: Reductions in MR imaging-determined size of the primary tumor best predict clinicopathologic response of breast cancer after one cycle of neoadjuvant chemotherapy. Transfer constant and size changes are equally sensitive in the identification of patients who would gain no clinical or pathologic benefit after two cycles of treatment. (c) RSNA, 2006.

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Year:  2006        PMID: 16543585     DOI: 10.1148/radiol.2392021099

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  79 in total

1.  Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer.

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
Journal:  Invest Radiol       Date:  2015-04       Impact factor: 6.016

Review 2.  Diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for monitoring anticancer therapy.

Authors:  Anwar R Padhani; Aftab Alam Khan
Journal:  Target Oncol       Date:  2010-04-11       Impact factor: 4.493

Review 3.  Imaging-based tumor treatment response evaluation: review of conventional, new, and emerging concepts.

Authors:  Hee Kang; Ho Yun Lee; Kyung Soo Lee; Jae-Hun Kim
Journal:  Korean J Radiol       Date:  2012-06-18       Impact factor: 3.500

4.  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

5.  Diffuse optical spectroscopic imaging correlates with final pathological response in breast cancer neoadjuvant chemotherapy.

Authors:  Albert E Cerussi; Vaya W Tanamai; David Hsiang; John Butler; Rita S Mehta; Bruce J Tromberg
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2011-11-28       Impact factor: 4.226

6.  Association between serial dynamic contrast-enhanced MRI and dynamic 18F-FDG PET measures in patients undergoing neoadjuvant chemotherapy for locally advanced breast cancer.

Authors:  Savannah C Partridge; Risa K Vanantwerp; Robert K Doot; Xiaoyu Chai; Brenda F Kurland; Peter R Eby; Jennifer M Specht; Lisa K Dunnwald; Erin K Schubert; Constance D Lehman; David A Mankoff
Journal:  J Magn Reson Imaging       Date:  2010-11       Impact factor: 4.813

7.  [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

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

9.  Noninvasive monitoring of breast cancer during neoadjuvant chemotherapy using optical tomography with ultrasound localization.

Authors:  Quing Zhu; Susan Tannenbaum; Poornima Hegde; Mark Kane; Chen Xu; Scott H Kurtzman
Journal:  Neoplasia       Date:  2008-10       Impact factor: 5.715

10.  Invasive breast cancer: predicting disease recurrence by using high-spatial-resolution signal enhancement ratio imaging.

Authors:  Ka-Loh Li; Savannah C Partridge; Bonnie N Joe; Jessica E Gibbs; Ying Lu; Laura J Esserman; Nola M Hylton
Journal:  Radiology       Date:  2008-07       Impact factor: 11.105

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