Literature DB >> 23400545

Diffusion-weighted imaging reflects pathological therapeutic response and relapse in breast cancer.

Hiroshi Fujimoto1, Toshiki Kazama, Takeshi Nagashima, Masahiro Sakakibara, Tiberiu Hiroshi Suzuki, Yoshiyuki Okubo, Nobumitsu Shiina, Kaoru Fujisaki, Satoshi Ota, Masaru Miyazaki.   

Abstract

BACKGROUND: Conventional imaging does not always accurately depict the pathological response to neoadjuvant chemotherapy (NAC). Diffusion-weighted imaging (DWI) may provide additional insight into the chemotherapeutic effect. This study assessed whether the apparent diffusion coefficient (ADC) correlated with pathological outcome and prognosis in breast cancer patients receiving NAC.
METHODS: Fifty-six patients with locally advanced breast cancer received surgery after NAC. Dynamic contrast-enhanced (DCE) and DWI were performed before and after NAC. The pathological response was classified into five categories from no response to complete response according to amount of residual cancer. The correlation between ADC and postoperative pathologic and prognostic outcome was assessed.
RESULTS: The distribution of the pathological response classification was as follows: no response, 3 cases; mild response, 22 cases; moderate response, 12 cases; marked response, 11 cases; complete response, 8 cases. ADC after NAC correlated with pathological response, but ADC before NAC did not. The change in ADC after chemotherapy had better correlation coefficient (r = 0.67) than change in size (r = 0.58) and ADC after NAC (r = 0.64). Although the group with larger change of tumor size showed only marginal significance compared with the smaller change group (p = 0.089), the group with higher change of ADC showed significantly better prognosis than the lower one (p = 0.038).
CONCLUSIONS: Change in ADC after chemotherapy better correlated with pathological outcome and prognosis than change in tumor size. DWI has potential in evaluating the pathological outcome of NAC in breast cancer patients.

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Year:  2013        PMID: 23400545     DOI: 10.1007/s12282-013-0449-3

Source DB:  PubMed          Journal:  Breast Cancer        ISSN: 1340-6868            Impact factor:   4.239


  15 in total

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Authors:  W-Y Huang; J-B Wen; G Wu; B Yin; J-J Li; D-Y Geng
Journal:  AJNR Am J Neuroradiol       Date:  2016-07-07       Impact factor: 3.825

2.  Histogram analysis of apparent diffusion coefficients after neoadjuvant chemotherapy in breast cancer.

Authors:  Yun Ju Kim; Sung Hun Kim; Ah Won Lee; Min-Sun Jin; Bong Joo Kang; Byung Joo Song
Journal:  Jpn J Radiol       Date:  2016-08-12       Impact factor: 2.374

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Journal:  Eur J Nucl Med Mol Imaging       Date:  2015-07-22       Impact factor: 9.236

4.  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

5.  Diffusion MRI and novel texture analysis in osteosarcoma xenotransplants predicts response to anti-checkpoint therapy.

Authors:  Parastou Foroutan; Jenny M Kreahling; David L Morse; Olya Grove; Mark C Lloyd; Damon Reed; Meera Raghavan; Soner Altiok; Gary V Martinez; Robert J Gillies
Journal:  PLoS One       Date:  2013-12-16       Impact factor: 3.240

6.  Diffusion-weighted imaging in monitoring the pathological response to neoadjuvant chemotherapy in patients with breast cancer: a meta-analysis.

Authors:  Wen Gao; Ning Guo; Ting Dong
Journal:  World J Surg Oncol       Date:  2018-07-18       Impact factor: 2.754

7.  Can Multi-Parametric MR Based Approach Improve the Predictive Value of Pathological and Clinical Therapeutic Response in Breast Cancer Patients?

Authors:  Uma Sharma; Khushbu Agarwal; Rani G Sah; Rajinder Parshad; Vurthaluru Seenu; Sandeep Mathur; Siddhartha D Gupta; Naranamangalam R Jagannathan
Journal:  Front Oncol       Date:  2018-08-15       Impact factor: 6.244

8.  Diffusion-Weighted Magnetic Resonance Imaging of Patients with Breast Cancer Following Neoadjuvant Chemotherapy Provides Early Prediction of Pathological Response - A Prospective Study.

Authors:  Nara P Pereira; Carla Curi; Cynthia A B T Osório; Elvira F Marques; Fabiana B Makdissi; Katja Pinker; Almir G V Bitencourt
Journal:  Sci Rep       Date:  2019-11-08       Impact factor: 4.379

9.  Role of the Intravoxel Incoherent Motion Diffusion Weighted Imaging in the Pre-treatment Prediction and Early Response Monitoring to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer.

Authors:  Shunan Che; Xinming Zhao; Yanghan Ou; Jing Li; Meng Wang; Bing Wu; Chunwu Zhou
Journal:  Medicine (Baltimore)       Date:  2016-01       Impact factor: 1.889

Review 10.  Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy.

Authors:  Roberto Lo Gullo; Sarah Eskreis-Winkler; Elizabeth A Morris; Katja Pinker
Journal:  Breast       Date:  2019-11-23       Impact factor: 4.380

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