Dynamic contrast-enhanced breast magnetic resonance imaging for the prediction of early and late recurrences in breast cancer

Research output: Contribution to journalJournal articlepeer-review

Abstract

The aim of the study was to evaluate dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) features for the prediction of early and late recurrences in patients with breast cancer. Of 1030 breast cancer patients who underwent surgery at our hospital from January 2007 to July 2011, 83 recurrent breast cancer patients were enrolled in this study. We compared MRI features (background parenchymal enhancement [BPE], internal enhancement, adjacent vessel sign, whole-breast vascularity, initial enhancement pattern, kinetic curve types, and quantitative kinetic parameters) and clinico-pathologic variables (age, stage, histologic grade, nuclear grade, existence of lymphovascular invasion and extensive intraductal carcinoma component, and immunohistochemical profiles) between patients with early (≤2.5 years after surgery) and late recurrence (>2.5 years after surgery). Cox proportional hazard regression analysis was performed to evaluate independent risk factors for early and late recurrence. On breast MRI, prominent ipsilateral whole-breast vascularity was independently associated with early recurrence (hazard ratio [HR], 2.86; 95% confidence intervals [CI], 1.39-5.88) and moderate or marked BPE (HR, 2.08; 95% CI, 1.04-4.18) and rim enhancement (HR, 2.14; 95% CI, 1.00-4.59) were independently associated with late recurrence. Clinico-pathologic variables independently associated with early recurrence included negative estrogen receptor (HR, 0.53; 95% CI, 0.29-0.96), whereas T2 stage (HR, 2.08; 95% CI, 1.04-4.16) and nuclear grade III (HR, 2.54; 95% CI, 1.29-4.98) were associated with late recurrence. In DCE-MRI, prominent ipsilateral whole-breast vascularity, moderate or marked BPE, and rim enhancement could be useful for predicting recurrence timing in patients with breast cancer.

Original languageEnglish
Pages (from-to)e5330
JournalMedicine (United States)
Volume95
Issue number48
DOIs
StatePublished - 2016

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Breast cancer
  • Image enhancement
  • Magnetic resonance imaging
  • Neoplasm recurrence

Quacquarelli Symonds(QS) Subject Topics

  • Medicine

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