Literature DB >> 19863408

Magnetic resonance imaging of breast cancer and correlation with prognostic factors.

Yun-Woo Chang1, Kui Hyang Kwon, Deuk Lin Choi, Dong Wha Lee, Min Hyuk Lee, Hye Kyung Lee, Seung Boo Yang, Yongbae Kim, Dae Young Seo.   

Abstract

BACKGROUND: Prognostic factors of breast cancer have been used for the prediction of clinical outcome or selection of patients for complementary treatment. Some of the imaging features of breast cancer, e.g. magnetic resonance imaging (MRI), are associated with these prognostic factors.
PURPOSE: To evaluate the relationship between dynamic enhanced MR features and prognostic factors of clinical outcome of breast cancer.
MATERIAL AND METHODS: A total of 136 patients with 151 breast cancers underwent 1.5T dynamic MR imaging with the use of a dynamic T1-weighted three-dimensional fast low-angle shot (FLASH) subtraction imaging technique. Morphological and kinetic analyses of MR features were evaluated using the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon. Pathological prognostic factors were correlated with MR imaging characteristics, including tumor size, histological grade, lymph node status, expression of estrogen receptor (ER), expression of progesterone receptor (PR), expression of c-erbB2, determination of Ki-67 index, and microvascular density (MVD), using univariate and multivariate statistical analyses.
RESULTS: Based on univariate and multivariate analyses, spiculated tumor margins correlated significantly with lower histological grade (I-II) and positive PR expression. Rim enhancement was significantly correlated with high histological grade, presence of axillary lymph node metastasis, large tumor size, increased Ki-67 index, and increased MVD. Early peak enhancement, as seen on the first scan after contrast medium injection, was correlated with negative ER expression.
CONCLUSION: The presence of a lesion with a spiculated margin may predict a relatively good prognosis, and the presence of a lesion with rim enhancement may predict a relatively poor prognosis.

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Year:  2009        PMID: 19863408     DOI: 10.3109/02841850903225180

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  9 in total

1.  Correlation of contrast-enhanced ultrasound kinetics with prognostic factors in invasive breast cancer.

Authors:  Botond K Szabó; Ariel Saracco; Ervin Tánczos; Peter Aspelin; Karin Leifland; Brigitte Wilczek; Rimma Axelsson
Journal:  Eur Radiol       Date:  2013-07-03       Impact factor: 5.315

2.  Correlations between diffusion-weighted imaging and breast cancer biomarkers.

Authors:  Laura Martincich; Veronica Deantoni; Ilaria Bertotto; Stefania Redana; Franziska Kubatzki; Ivana Sarotto; Valentina Rossi; Michele Liotti; Riccardo Ponzone; Massimo Aglietta; Daniele Regge; Filippo Montemurro
Journal:  Eur Radiol       Date:  2012-03-13       Impact factor: 5.315

3.  Early prediction of response to neoadjuvant chemotherapy using contrast-enhanced ultrasound in breast cancer.

Authors:  Juan Peng; Huan Pu; Yan Jia; Chuang Chen; Xiao-Kang Ke; Qing Zhou
Journal:  Medicine (Baltimore)       Date:  2021-05-14       Impact factor: 1.889

4.  Background Parenchymal Enhancement of the Contralateral Normal Breast: Association with Tumor Response in Breast Cancer Patients Receiving Neoadjuvant Chemotherapy.

Authors:  Jeon Hor Chen; Hon J Yu; Christine Hsu; Rita S Mehta; Philip M Carpenter; Min Ying Su
Journal:  Transl Oncol       Date:  2015-06       Impact factor: 4.243

5.  Correlation between apparent diffusion coefficient values in breast magnetic resonance imaging and prognostic factors of breast invasive ductal carcinoma.

Authors:  Ricardo Moutinho-Guilherme; Janeth Hercilia Oyola; David Sanz-Rosa; Israel Thuissard Vassallo; Raquel Murillo García; Joana Martins Pisco; Vicente Martínez de Vega
Journal:  Porto Biomed J       Date:  2018-08-03

6.  Development and Internal Validation of a Preoperative Prediction Model for Sentinel Lymph Node Status in Breast Cancer: Combining Radiomics Signature and Clinical Factors.

Authors:  Chunhua Wang; Xiaoyu Chen; Hongbing Luo; Yuanyuan Liu; Ruirui Meng; Min Wang; Siyun Liu; Guohui Xu; Jing Ren; Peng Zhou
Journal:  Front Oncol       Date:  2021-11-08       Impact factor: 6.244

Review 7.  Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review.

Authors:  Toshiki Kazama; Taro Takahara; Jun Hashimoto
Journal:  Life (Basel)       Date:  2022-03-28

8.  Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Based on Intratumoral and Peritumoral DCE-MRI Radiomics Nomogram.

Authors:  Ying Liu; Xing Li; Lina Zhu; Zhiwei Zhao; Tuan Wang; Xi Zhang; Bing Cai; Li Li; Mingrui Ma; Xiaojian Ma; Jie Ming
Journal:  Contrast Media Mol Imaging       Date:  2022-08-18       Impact factor: 3.009

9.  A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores.

Authors:  Tao Wan; B Nicolas Bloch; Donna Plecha; CheryI L Thompson; Hannah Gilmore; Carl Jaffe; Lyndsay Harris; Anant Madabhushi
Journal:  Sci Rep       Date:  2016-02-18       Impact factor: 4.379

  9 in total

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