Literature DB >> 30695671

Prediction and prognosis of biologically aggressive breast cancers by the combination of DWI/DCE-MRI and immunohistochemical tumor markers.

Atiya Allarakha1, Yan Gao1, Hong Jiang1, Pei-Jun Wang1.   

Abstract

Breast cancer (BC) research has been evolving tremendously on all fronts, whether it being for imaging, pathology, oncology, pharmacology, or genetics. Regarding medical imaging, dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted imaging (DWI) are now both universally recognized and widely used modalities in multiparametric MRI (mp-MRI) to diagnose and stage BC, to assess post-chemotherapy response, and to differentiate between scar tissue and recurrent tumor. Meanwhile, pathologists have provided evidence of BC being heterogeneous and having several subtypes, which in turn might affect its prognostic and therapeutic outcomes. Immunohistochemical testing for estrogen receptor (ER), progesterone receptor (PR), human epidermal receptor factor-2 (HER-2), and Ki-67 proliferation index is performed daily to categorize breast tumors into different molecular subtypes. Since then, a number of studies have evaluated whether there is any inter-relationship between them and mp-MRI parameters, the nature of their relationship if any, and the predictive ability of mp-MRI to diagnose biologically aggressive tumors. This review aims to summarize published literature where the data of DCE-MRI/DWI and immunohistochemical markers have been combined for BC studies in order to observe what conclusions have been reached so far, how our understanding of BC has changed because of them, and what are the future implications of these for the diagnosis of breast tumors. We also give our suggestions on what other relevant areas should be investigated.

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Year:  2019        PMID: 30695671

Source DB:  PubMed          Journal:  Discov Med        ISSN: 1539-6509            Impact factor:   2.970


  5 in total

1.  Dual time point 18F-fluorodeoxyglucose positron emission tomography/computed tomography fusion imaging (18F-FDG PET/CT) in primary breast cancer.

Authors:  Yoji Yamagishi; Tomomi Koiwai; Tamio Yamasaki; Takahiro Einama; Makiko Fukumura; Miyuki Hiratsuka; Takako Kono; Katsumi Hayashi; Jiro Ishida; Hideki Ueno; Hitoshi Tsuda
Journal:  BMC Cancer       Date:  2019-11-27       Impact factor: 4.430

2.  Changes in Tumor Stem Cell Markers and Epithelial-Mesenchymal Transition Markers in Nonluminal Breast Cancer after Neoadjuvant Chemotherapy and Their Correlation with Contrast-Enhanced Ultrasound.

Authors:  Xiaoling Leng; Guofu Huang; Lianhua Zhang; Jianbing Ding; Fucheng Ma
Journal:  Biomed Res Int       Date:  2020-11-17       Impact factor: 3.411

3.  Application of MRI Image Based on Computer Semiautomatic Segmentation Algorithm in the Classification Prediction of Breast Cancer Histology.

Authors:  Aizhu Sheng; Aijing Li; Jianbi Xia; Yizhai Ye
Journal:  J Healthc Eng       Date:  2021-11-24       Impact factor: 2.682

4.  Readout-Segmented Echo-Planar Diffusion-Weighted MR Imaging Improves the Differentiation of Breast Cancer Receptor Statuses Compared With Conventional Diffusion-Weighted Imaging.

Authors:  Minghao Zhong; Zhiqi Yang; Xiaofeng Chen; Ruibin Huang; Mengzhu Wang; Weixiong Fan; Zhuozhi Dai; Xiangguang Chen
Journal:  J Magn Reson Imaging       Date:  2022-01-17       Impact factor: 5.119

5.  Correlation Analysis of Pathological Features and Axillary Lymph Node Metastasis in Patients with Invasive Breast Cancer.

Authors:  Hongye Chen; Xiangchao Meng; Xiaopeng Hao; Qiao Li; Lin Tian; Yue Qiu; Yuhui Chen
Journal:  J Immunol Res       Date:  2022-09-19       Impact factor: 4.493

  5 in total

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