Literature DB >> 24938669

Effect of breast cancer phenotype on diagnostic performance of MRI in the prediction to response to neoadjuvant treatment.

Enida Bufi1, Paolo Belli2, Marialuisa Di Matteo2, Daniela Terribile3, Gianluca Franceschini3, Luigia Nardone4, Gianluigi Petrone5, Lorenzo Bonomo2.   

Abstract

AIM: The estimation of response to neoadjuvant chemotherapy (NAC) is useful in the surgical decision in breast cancer. We addressed the diagnostic reliability of conventional MRI, of diffusion weighted imaging (DWI) and of a merged criterion coupling morphological MRI and DWI. Diagnostic performance was analysed separately in different tumor subtypes, including HER2+ (human epidermal growth factor receptor 2)/HR+ (hormone receptor) (hybrid phenotype).
MATERIALS AND METHODS: Two-hundred and twenty-five patients underwent MRI before and after NAC. The response to treatment was defined according to the RECIST classification and the evaluation of DWI with apparent diffusion coefficient (ADC). The complete pathological response - pCR was assessed (Mandard classification).
RESULTS: Tumor phenotypes were Luminal (63.6%), Triple Negative (16.4%), HER2+ (7.6%) or Hybrid (12.4%). After NAC, pCR was observed in 17.3% of cases. Average ADC was statistically higher after NAC (p<0.001) among patients showing pCR vs. those who had not pCR. The RECIST classification showed adequate performance in predicting the pCR in Triple Negative (area under the receiver operating characteristic curve, ROC AUC=0.9) and in the HER2+ subgroup (AUC=0.826). Lower performance was found in the Luminal and Hybrid subgroups (AUC 0.693 and 0.611, respectively), where the ADC criterion yielded an improved performance (AUC=0.787 and 0.722). The coupling of morphological and DWI criteria yielded maximally improved performance in the Luminal and Hybrid subgroups (AUC=0.797 and 0.761).
CONCLUSION: The diagnostic reliability of MRI in predicting the pCR to NAC depends on the tumor phenotype, particularly in the Luminal and Hybrid subgroups. In these cases, the coupling of morphological MRI evaluation and DWI assessment may facilitate the diagnosis.
Copyright © 2014. Published by Elsevier Ireland Ltd.

Entities:  

Keywords:  Breast cancer; Complete Pathological response; Diffusion weighted imaging; Neoadjuvant chemotherapy; RECIST classification; Tumor subtype

Mesh:

Year:  2014        PMID: 24938669     DOI: 10.1016/j.ejrad.2014.05.002

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  14 in total

1.  Magnetic resonance imaging texture analysis classification of primary breast cancer.

Authors:  S A Waugh; C A Purdie; L B Jordan; S Vinnicombe; R A Lerski; P Martin; A M Thompson
Journal:  Eur Radiol       Date:  2015-06-12       Impact factor: 5.315

2.  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

3.  Prediction of pathologic complete response on MRI in patients with breast cancer receiving neoadjuvant chemotherapy according to molecular subtypes.

Authors:  Jieun Kim; Boo-Kyung Han; Eun Young Ko; Eun Sook Ko; Ji Soo Choi; Ko Woon Park
Journal:  Eur Radiol       Date:  2022-01-06       Impact factor: 5.315

4.  Usefulness of new shear wave elastography in early predicting the efficacy of neoadjuvant chemotherapy for patients with breast cancer: where and when to measure is optimal?

Authors:  Jiong-Hui Gu; Chang He; Qi-Yu Zhao; Tian-An Jiang
Journal:  Breast Cancer       Date:  2022-01-17       Impact factor: 3.307

Review 5.  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

Review 6.  How to use magnetic resonance imaging following neoadjuvant chemotherapy in locally advanced breast cancer.

Authors:  Elissa R Price; Jasmine Wong; Rita Mukhtar; Nola Hylton; Laura J Esserman
Journal:  World J Clin Cases       Date:  2015-07-16       Impact factor: 1.337

7.  Background parenchymal enhancement and breast cancer: a review of the emerging evidences about its potential use as imaging biomarker.

Authors:  Rossella Rella; Andrea Contegiacomo; Enida Bufi; Sara Mercogliano; Paolo Belli; Riccardo Manfredi
Journal:  Br J Radiol       Date:  2020-10-15       Impact factor: 3.039

8.  Kinetic information from dynamic contrast-enhanced MRI enables prediction of residual cancer burden and prognosis in triple-negative breast cancer: a retrospective study.

Authors:  Ayane Yamaguchi; Maya Honda; Hiroshi Ishiguro; Masako Kataoka; Tatsuki R Kataoka; Hanako Shimizu; Masae Torii; Yukiko Mori; Nobuko Kawaguchi-Sakita; Kentaro Ueno; Masahiro Kawashima; Masahiro Takada; Eiji Suzuki; Yuji Nakamoto; Kosuke Kawaguchi; Masakazu Toi
Journal:  Sci Rep       Date:  2021-05-12       Impact factor: 4.379

9.  Diffusion-weighted imaging in identifying breast cancer pathological response to neoadjuvant chemotherapy: A meta-analysis.

Authors:  Wei Chu; Weiwei Jin; Daihong Liu; Jian Wang; Chengjun Geng; Lihua Chen; Xuequan Huang
Journal:  Oncotarget       Date:  2017-12-11

10.  The diagnostic accuracy of magnetic resonance imaging in predicting pathologic complete response after neoadjuvant chemotherapy in patients with different molecular subtypes of breast cancer.

Authors:  Xinfeng Zhang; Dandan Wang; Zhuangkai Liu; Zheng Wang; Qiang Li; Hong Xu; Bin Zhang; Ting Liu; Feng Jin
Journal:  Quant Imaging Med Surg       Date:  2020-01
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