title: Recurrence-free survival in breast cancer is associated with MRI tumor enhancement dynamics quantified using computer algorithms.
abstract: OBJECTIVE
  The purpose of this study is to investigate the association between breast cancer recurrence-free survival and breast magnetic resonance imaging (MRI) tumor enhancement dynamics which are quantified semi-automatically using computer algorithms.
  
  
  METHODS
  In this retrospective IRB-approved study, we analyzed data from 275 breast cancer patients at a single institution. Recurrence-free survival data were obtained from the medical record. Routine clinical pre-operative breast MRIs were performed in all patients. The tumors were marked on the MRIs by fellowship-trained breast radiologists. A previously developed computer algorithm was applied to the marked tumors to quantify the enhancement dynamics relative to the automatically assessed background parenchymal enhancement. To establish whether the contrast enhancement feature quantified by the algorithm was associated with recurrence-free survival, we constructed a Cox proportional hazards regression model with the computer-extracted feature as a covariate. We controlled for tumor grade and size (major axis length), patient age, patient race/ethnicity, and menopausal status.
  
  
  RESULTS
  The analysis showed that the semi-automatically obtained feature quantifying MRI tumor enhancement dynamics was independently predictive of recurrence-free survival (p=0.024).
  
  
  CONCLUSIONS
  Semi-automatically quantified tumor enhancement dynamics on MRI are predictive of recurrence-free survival in breast cancer patients.
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, NC, USA. Electronic address: maciej.mazurowski@duke.edu.
authors.forename: Maciej A
authors.initials: MA
authors.lastname: Mazurowski
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, NC, USA.
authors.forename: Lars J
authors.initials: LJ
authors.lastname: Grimm
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, NC, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Medicine, Division of Medical Oncology, Duke University Medical Center, Durham, NC, USA.
authors.forename: P Kelly
authors.initials: PK
authors.lastname: Marcom
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, NC, USA.
authors.forename: Sora C
authors.initials: SC
authors.lastname: Yoon
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, NC, USA.
authors.forename: Connie
authors.initials: C
authors.lastname: Kim
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, NC, USA.
authors.forename: Sujata V
authors.initials: SV
authors.lastname: Ghate
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, NC, USA.
authors.forename: Karen S
authors.initials: KS
authors.lastname: Johnson
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000401:mortality / Q000473:pathology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local / Q000473:pathology*; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D012189:Retrospective Studies
keywords: Breast cancer; Computer-based analysis; Magnetic resonance imaging; Recurrence-free survival