Literature DB >> 25580585

Diffusion-weighted imaging in assessing pathological response of tumor in breast cancer subtype to neoadjuvant chemotherapy.

Shangang Liu1, Ruimei Ren1, Zhaoqiu Chen2, Yongsheng Wang3, Tingyong Fan2, Chengli Li4, Pinliang Zhang5.   

Abstract

PURPOSE: To investigate the efficacy of diffusion-weighted imaging (DWI) for reflecting and predicting pathological tumor response in breast cancer subtype to neoadjuvant chemotherapy (NAC).
MATERIALS AND METHODS: The retrospective study included 176 patients with breast cancer who underwent magnetic resonance imaging (MRI) examinations before and after NAC prior to surgery. The pre- and post-NAC apparent diffusion coefficient (ADC) values of tumor were measured respectively on DWI. The pathological response was classified into either a complete response (pCR) or as a noncomplete response (pNCR) to NAC with the Miller & Payne system. The relationship between the ADC value and the pathological response was assessed according to intrinsic subtypes (Luminal A, Luminal B, HER2-enriched, and triple negative) defined by immunohistochemical features.
RESULTS: Multiple comparisons respectively showed that pre-NAC and post-NAC ADC were significantly different among four subtypes (P < 0.001). After the comparison between two different subtypes, the pre-NAC ADC value of the triple-negative and HER2-enriched subtypes were significantly higher than Luminal A (P < 0.001 and P < 0.001) and Luminal B subtype (P < 0.001 and P = 0.009), and the post-NAC ADC of triple-negative subtype was significantly higher than the others (P < 0.001). The pre-NAC ADC of pCRs was significantly lower than that of pNCRs only in the triple-negative subtype among four subtypes (P < 0.001), and the post-NAC ADC of pCRs was significantly higher than that of pNCRs in each subtype (P < 0.001).
CONCLUSION: DWI appears to be a promising tool to determine the association of pathological response to NAC in breast cancer subtypes.
© 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  breast cancer subtype; diffusion-weighted imaging; magnetic resonance imaging; neoadjuvant chemotherapy; pathological response

Mesh:

Substances:

Year:  2015        PMID: 25580585     DOI: 10.1002/jmri.24843

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


  26 in total

1.  Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging.

Authors:  Tianwen Xie; Qiufeng Zhao; Caixia Fu; Qianming Bai; Xiaoyan Zhou; Lihua Li; Robert Grimm; Li Liu; Yajia Gu; Weijun Peng
Journal:  Eur Radiol       Date:  2018-11-06       Impact factor: 5.315

2.  Distortion correction in diffusion-weighted imaging of the breast: Performance assessment of prospective, retrospective, and combined (prospective + retrospective) approaches.

Authors:  Ileana Hancu; Seung-Kyun Lee; Keith Hulsey; Robert Lenkinski; Dominic Holland; Jonathan I Sperl; Ek T Tan
Journal:  Magn Reson Med       Date:  2016-07-12       Impact factor: 4.668

Review 3.  Imaging Considerations and Interprofessional Opportunities in the Care of Breast Cancer Patients in the Neoadjuvant Setting.

Authors:  Anna G Sorace; Sara Harvey; Anum Syed; Thomas E Yankeelov
Journal:  Semin Oncol Nurs       Date:  2017-09-15       Impact factor: 2.315

4.  Diffusion-weighted imaging of breast invasive lobular carcinoma: comparison with invasive carcinoma of no special type using a histogram analysis.

Authors:  Seongkyun Jeong; Tae Hee Kim
Journal:  Quant Imaging Med Surg       Date:  2022-01

5.  Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis.

Authors:  Hans-Jonas Meyer; Andreas Wienke; Alexey Surov
Journal:  Breast Care (Basel)       Date:  2021-02-23       Impact factor: 2.860

Review 6.  Diffusion-weighted breast MRI: Clinical applications and emerging techniques.

Authors:  Savannah C Partridge; Noam Nissan; Habib Rahbar; Averi E Kitsch; Eric E Sigmund
Journal:  J Magn Reson Imaging       Date:  2016-09-30       Impact factor: 4.813

7.  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

8.  Test-retest repeatability and reproducibility of ADC measures by breast DWI: Results from the ACRIN 6698 trial.

Authors:  David C Newitt; Zheng Zhang; Jessica E Gibbs; Savannah C Partridge; Thomas L Chenevert; Mark A Rosen; Patrick J Bolan; Helga S Marques; Sheye Aliu; Wen Li; Lisa Cimino; Bonnie N Joe; Heidi Umphrey; Haydee Ojeda-Fournier; Basak Dogan; Karen Oh; Hiroyuki Abe; Jennifer Drukteinis; Laura J Esserman; Nola M Hylton
Journal:  J Magn Reson Imaging       Date:  2018-10-22       Impact factor: 4.813

9.  DWI in the Assessment of Breast Lesions.

Authors:  Savannah C Partridge; Nita Amornsiripanitch
Journal:  Top Magn Reson Imaging       Date:  2017-10

10.  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
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