Literature DB >> 26001248

Breast cancer molecular subtype as a predictor of the utility of preoperative MRI.

Richard Ha1, Brian Jin, Victoria Mango, Lauren Friedlander, Vesco Miloshev, Sharp Malak, Ralph Wynn.   

Abstract

OBJECTIVE: The purpose of this study was to discern whether breast cancer molecular subtype, a known prognostic indicator, can be used to select patients with the highest likelihood of having clinically significant additional findings on breast MRI.
MATERIALS AND METHODS: A database review from January 2010 through December 2013 identified 299 patients who underwent preoperative breast MRI with tumors classifiable into molecular subtypes. Subtypes were classified on the basis of immunohistochemical staining surrogates as luminal A (hormone receptor [ER or PR] positive, ERBB2 [formerly HER2 or HER2/neu] negative, luminal B (hormone receptor positive, ERBB2 positive), ERBB2 (hormone receptor negative, ERBB2 positive), or basal (hormone receptor and ERBB2 negative). Univariate and multivariate logistic regression analyses were used to determine the association between subtype and additional breast MRI findings, including multicentric or multifocal disease, contralateral disease, chest wall involvement, skin and nipple involvement, and internal mammary and axillary lymphadenopathy.
RESULTS: The subtype distribution was luminal A, 70.6% (211/299); luminal B, 14.1% (42/299); ERBB2, 5.4% (16/299); and basal, 10.0% (30/299). ERBB2 and luminal B sub-types were more often associated with multicentric disease (25.0% and 26.2%), multifocal disease (37.5% and 35.7%), and axillary disease (50.0% and 45.2%) than were luminal A cancers (multicentric disease, 10.9%; multifocal disease 20.4%; axillary disease, 22.7%) (p < 0.001). In multivariate analysis, after control for patient age, tumor size, and nuclear grade, patients with ERBB2-overexpressing tumors were 2.4 times as likely as patients with luminal A tumors to have multicentric disease (p = 0.016), 2.0 times as likely to have multifocal disease (p = 0.024), 1.7 times as likely to have skin and nipple involvement (p = 0.013), and 1.9 times as likely to have axillary disease (p = 0.011).
CONCLUSION: Preoperative MRI may most benefit patients with tumors with ERBB2 overexpression because of the increased likelihood of the presence of additional disease.

Entities:  

Keywords:  ERBB2; breast MRI; breast cancer; immunohistochemical staining; molecular subtype

Mesh:

Substances:

Year:  2015        PMID: 26001248     DOI: 10.2214/AJR.14.13666

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  7 in total

1.  Incremental cancer detection using breast ultrasonography versus breast magnetic resonance imaging in the evaluation of newly diagnosed breast cancer patients.

Authors:  Hongying He; Jeri S Plaxco; Wei Wei; Lei Huo; Rosalind P Candelaria; Henry M Kuerer; Wei T Yang
Journal:  Br J Radiol       Date:  2016-07-07       Impact factor: 3.039

2.  Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm.

Authors:  Richard Ha; Simukayi Mutasa; Jenika Karcich; Nishant Gupta; Eduardo Pascual Van Sant; John Nemer; Mary Sun; Peter Chang; Michael Z Liu; Sachin Jambawalikar
Journal:  J Digit Imaging       Date:  2019-04       Impact factor: 4.056

3.  The Values of Combined and Sub-Stratified Imaging Scores with Ultrasonography and Mammography in Breast Cancer Subtypes.

Authors:  Tsun-Hou Chang; Hsian-He Hsu; Yu-Ching Chou; Jyh-Cherng Yu; Giu-Cheng Hsu; Guo-Shu Huang; Guo-Shiou Liao
Journal:  PLoS One       Date:  2015-12-21       Impact factor: 3.240

4.  MRI features predictive of negative surgical margins in patients with HER2 overexpressing breast cancer undergoing breast conservation.

Authors:  Brittany Z Dashevsky; Jung Hun Oh; Aditya P Apte; Blanca Bernard-Davila; Elizabeth A Morris; Joseph O Deasy; Elizabeth J Sutton
Journal:  Sci Rep       Date:  2018-01-10       Impact factor: 4.379

5.  MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer.

Authors:  Almir G V Bitencourt; Peter Gibbs; Carolina Rossi Saccarelli; Isaac Daimiel; Roberto Lo Gullo; Michael J Fox; Sunitha Thakur; Katja Pinker; Elizabeth A Morris; Monica Morrow; Maxine S Jochelson
Journal:  EBioMedicine       Date:  2020-10-08       Impact factor: 8.143

Review 6.  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

7.  Computer-aided evaluation of the correlation between MRI morphology and immunohistochemical biomarkers or molecular subtypes in breast cancer.

Authors:  Sen Jiang; You-Jia Hong; Fan Zhang; Yang-Kang Li
Journal:  Sci Rep       Date:  2017-10-23       Impact factor: 4.379

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.