Literature DB >> 27955963

Association Between Imaging Characteristics and Different Molecular Subtypes of Breast Cancer.

Mingxiang Wu1, Jie Ma2.   

Abstract

RATIONALE AND
OBJECTIVE: Breast cancer can be divided into four major molecular subtypes based on the expression of hormone receptor (estrogen receptor and progesterone receptor), human epidermal growth factor receptor 2, HER2 status, and molecular proliferation rate (Ki67). In this study, we sought to investigate the association between breast cancer subtype and radiological findings in the Chinese population.
MATERIALS AND METHODS: Medical records of 300 consecutive invasive breast cancer patients were reviewed from the database: the Breast Imaging Reporting and Data System. The imaging characteristics of the lesions were evaluated. The molecular subtypes of breast cancer were classified into four types: luminal A, luminal B, HER2 overexpressed (HER2), and basal-like breast cancer (BLBC). Univariate and multivariate logistic regression analyses were performed to assess the association between the subtype (dependent variable) and mammography or 15 magnetic resonance imaging (MRI) indicators (independent variables).
RESULTS: Luminal A and B subtypes were commonly associated with "clustered calcification distribution," "nipple invasion," or "skin invasion" (P <0.05). The BLBC subtype was more commonly associated with "rim enhancement" and persistent inflow type enhancement in delayed phase (P <0.05). HER2 overexpressed cancers showed association with persistent enhancement in the delayed phase on MRI and "clustered calcification distribution" on mammography (P <0.05).
CONCLUSION: Certain radiological features are strongly associated with the molecular subtype and hormone receptor status of breast tumor, which are potentially useful tools in the diagnosis and subtyping of breast cancer.
Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  MRI; Subtype; multivariate logistic regression analysis

Mesh:

Substances:

Year:  2016        PMID: 27955963     DOI: 10.1016/j.acra.2016.11.012

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


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