Literature DB >> 24738612

Breast cancer: early prediction of response to neoadjuvant chemotherapy using parametric response maps for MR imaging.

Nariya Cho1, Seock-Ah Im, In-Ae Park, Kyung-Hun Lee, Mulan Li, Wonshik Han, Dong-Young Noh, Woo Kyung Moon.   

Abstract

PURPOSE: To prospectively compare the performance of dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging using parametric response map (PRM) analysis with that using pharmacokinetic parameters (transfer constant [K(trans)], rate constant [kep ], and relative extravascular extracellular space [ve]) in the early prediction of pathologic responses to neoadjuvant chemotherapy (NAC) in breast cancer patients.
MATERIALS AND METHODS: The institutional review board approved this study; informed consent was obtained. Between August 2010 and December 2012, 48 women (mean age, 46.4 years; range, 29-65 years) with breast cancer were enrolled and treated with an anthracycline-taxane regimen. DCE MR imaging was performed before and after the first cycle of chemotherapy, and the pathologic response was assessed after surgery. Tumor size and volume, PRM characteristics, and pharmacokinetic parameters (K(trans), kep, and ve) on MR images were assessed and compared according to the pathologic responses by using the Fisher exact test or the independent-sample t test.
RESULTS: Six of 48 (12%) patients showed pathologic complete response (CR) (pCR) and 42 (88%) showed nonpathologic CR (npCR). Thirty-eight (79%) patients showed a good response (Miller-Payne score of 3, 4, or 5), and 10 (21%) showed a minor response (Miller-Payne score of 1 or 2). The mean proportion of voxels with increased signal intensity (PRMSI+) in the pCR or good response group was significantly lower than that in the npCR or minor response group (14.0% ± 6.5 vs 40.7% ± 27.2, P < .001; 34.3% ± 26.4 vs 52.8% ± 24.9, P = .041). Area under the receiver operating characteristic curve for PRMSI+ in the pCR group was 0.770 (95% confidence interval: 0.626, 0.879), and that for the good response group was 0.716 (95% confidence interval: 0.567, 0.837). No difference in tumor size, tumor volume, or pharmacokinetic parameters was found between groups.
CONCLUSION: PRM analysis of DCE MR images may enable the early identification of the pathologic response to NAC after the first cycle of chemotherapy, whereas pharmacokinetic parameters (K(trans), kep, and ve) do not.

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Year:  2014        PMID: 24738612     DOI: 10.1148/radiol.14131332

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


  29 in total

1.  MR spectroscopy of breast cancer for assessing early treatment response: Results from the ACRIN 6657 MRS trial.

Authors:  Patrick J Bolan; Eunhee Kim; Benjamin A Herman; Gillian M Newstead; Mark A Rosen; Mitchell D Schnall; Etta D Pisano; Paul T Weatherall; Elizabeth A Morris; Constance D Lehman; Michael Garwood; Michael T Nelson; Douglas Yee; Sandra M Polin; Laura J Esserman; Constantine A Gatsonis; Gregory J Metzger; David C Newitt; Savannah C Partridge; Nola M Hylton
Journal:  J Magn Reson Imaging       Date:  2016-12-16       Impact factor: 4.813

2.  Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy.

Authors:  Jia Wu; Guanghua Gong; Yi Cui; Ruijiang Li
Journal:  J Magn Reson Imaging       Date:  2016-04-15       Impact factor: 4.813

3.  Early prediction of response to neoadjuvant chemotherapy in breast cancer patients: comparison of single-voxel (1)H-magnetic resonance spectroscopy and (18)F-fluorodeoxyglucose positron emission tomography.

Authors:  Nariya Cho; Seock-Ah Im; Keon Wook Kang; In-Ae Park; In Chan Song; Kyung-Hun Lee; Tae-Yong Kim; Hyunjong Lee; In Kook Chun; Hai-Jeon Yoon; Woo Kyung Moon
Journal:  Eur Radiol       Date:  2015-09-17       Impact factor: 5.315

4.  Whole-lesion histogram and texture analyses of breast lesions on inline quantitative DCE mapping with CAIPIRINHA-Dixon-TWIST-VIBE.

Authors:  Kun Sun; Hong Zhu; Weimin Chai; Ying Zhan; Dominik Nickel; Robert Grimm; Caixia Fu; Fuhua Yan
Journal:  Eur Radiol       Date:  2019-08-01       Impact factor: 5.315

5.  Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model.

Authors:  Jared A Weis; Michael I Miga; Lori R Arlinghaus; Xia Li; Vandana Abramson; A Bapsi Chakravarthy; Praveen Pendyala; Thomas E Yankeelov
Journal:  Cancer Res       Date:  2015-09-02       Impact factor: 12.701

6.  Integrated 18F-FDG PET/MRI in breast cancer: early prediction of response to neoadjuvant chemotherapy.

Authors:  Nariya Cho; Seock-Ah Im; Gi Jeong Cheon; In-Ae Park; Kyung-Hun Lee; Tae-Yong Kim; Young Seon Kim; Bo Ra Kwon; Jung Min Lee; Hoon Young Suh; Koung Jin Suh
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-11-04       Impact factor: 9.236

7.  Effects of MRI scanner parameters on breast cancer radiomics.

Authors:  Ashirbani Saha; Xiaozhi Yu; Dushyant Sahoo; Maciej A Mazurowski
Journal:  Expert Syst Appl       Date:  2017-06-20       Impact factor: 6.954

8.  A Novel Marker, Based on Ultrasound Tomography, for Monitoring Early Response to Neoadjuvant Chemotherapy.

Authors:  Neb Duric; Peter Littrup; Mark Sak; Cuiping Li; Di Chen; Olivier Roy; Lisa Bey-Knight; Rachel Brem
Journal:  J Breast Imaging       Date:  2020-10-27

9.  Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI.

Authors:  Ji Young Lee; Kwang-Sig Lee; Bo Kyoung Seo; Kyu Ran Cho; Ok Hee Woo; Sung Eun Song; Eun-Kyung Kim; Hye Yoon Lee; Jung Sun Kim; Jaehyung Cha
Journal:  Eur Radiol       Date:  2021-07-05       Impact factor: 5.315

10.  Breast tumour volume and blood flow measured by MRI after one cycle of epirubicin and cyclophosphamide-based neoadjuvant chemotherapy as predictors of pathological response.

Authors:  William Stevens; Isabelle M Farrow; Leonidas Georgiou; Andrew M Hanby; Timothy J Perren; Laura M Windel; Daniel J Wilson; Nisha Sharma; David Dodwell; Thomas A Hughes; Barbara Jg Dall; David L Buckley
Journal:  Br J Radiol       Date:  2021-06-09       Impact factor: 3.039

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