Maciej A Mazurowski1, Lars J Grimm2, Jing Zhang2, P Kelly Marcom3, Sora C Yoon2, Connie Kim2, Sujata V Ghate2, Karen S Johnson2. 1. Department of Radiology, Duke University Medical Center, Durham, NC, USA. Electronic address: maciej.mazurowski@duke.edu. 2. Department of Radiology, Duke University Medical Center, Durham, NC, USA. 3. Department of Medicine, Division of Medical Oncology, Duke University Medical Center, Durham, NC, USA.
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.
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 cancerpatients 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 cancerpatients.
Authors: Maciej A Mazurowski; Ashirbani Saha; Michael R Harowicz; Elizabeth Hope Cain; Jeffrey R Marks; P Kelly Marcom Journal: J Magn Reson Imaging Date: 2019-01-22 Impact factor: 4.813
Authors: Geraldine J Liao; Leah C Henze Bancroft; Roberta M Strigel; Rhea D Chitalia; Despina Kontos; Linda Moy; Savannah C Partridge; Habib Rahbar Journal: J Magn Reson Imaging Date: 2019-04-19 Impact factor: 4.813
Authors: Aydin Demircioglu; Johannes Grueneisen; Marc Ingenwerth; Oliver Hoffmann; Katja Pinker-Domenig; Elizabeth Morris; Johannes Haubold; Michael Forsting; Felix Nensa; Lale Umutlu Journal: PLoS One Date: 2020-06-26 Impact factor: 3.240