Recurrence-free survival in breast cancer is associated with MRI tumor enhancement dynamics quantified using computer algorithms. Issue 11 (November 2015)

• Record Type: Journal Article
• Authors: Mazurowski, Maciej A.; Grimm, Lars J.; Zhang, Jing; Marcom, P. Kelly; Yoon, Sora C.; Kim, Connie; Ghate, Sujata V.; Johnson, Karen S.
• Is Part Of: European journal of radiology. Volume 84:Issue 11(2015)
• Page Start: 2117
• Page End: 2122
• Publication Date: 2015-11
• Subjects: Breast cancer -- Recurrence-free survival -- Magnetic resonance imaging -- Computer-based analysis; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2015.07.012
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Recurrence-free survival in breast cancer are associated with MRI enhancement. We controlled for age, race/ethnicity, menopausal status, tumor grade, and size. The enhancement was quantified by a computer algorithm. Abstract: Purpose: 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). Conclusion: Semi-automatically quantified tumor enhancement dynamics on MRI are predictive of recurrence-free survival in breast cancer patients.