Sungheon Gene Kim1,2, Melanie Freed1,2, Ana Paula Klautau Leite1,2, Jin Zhang1,2, Claudia Seuss1,2, Linda Moy1,2. 1. Center for Advanced Imaging Innovation and Research (CAIR), New York, New York, USA. 2. Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.
Abstract
PURPOSE: To assess the diagnostic utility of contrast kinetic analysis for breast lesions and background parenchyma of women undergoing MRI-guided biopsies, for whom standard clinical analysis had failed to separate benign and malignant lesions. MATERIALS AND METHODS: This study included 115 women who had indeterminate lesions based on routine diagnostic breast MRI exams and underwent an MRI (3 Tesla) -guided biopsy of one or more lesions suspicious for breast cancer. Breast dynamic contrast-enhanced (DCE)-MRI was performed using a radial stack-of-stars three-dimensional spoiled gradient echo pulse sequence and modified k-space weighted image contrast image reconstruction. Contrast kinetic model analysis was conducted to characterize the contrast enhancement patterns measured in lesions and background parenchyma (BP). The transfer rate (Ktrans ), interstitial volume fraction (ve ), and vascular volume fraction (vp ) estimated from the lesion and BP were used to separate malignant from benign lesions. RESULTS: The patients with malignant lesions had significantly (P < 0.05) higher median lesion-Ktrans (0.081 min-1 ), higher median BP-Ktrans (0.032 min-1 ), and BP-vp (0.020) than those without malignant lesions (0.056 min-1 , 0.017 min-1 and 0.012, respectively). The area under the receiver operating characteristic curve (AUC) of the BP-Ktrans (0.687) was highest among the single parameters and higher than that of the lesion-Ktrans (0.664). The combined logistic regression model of lesion-Ktrans , lesion-ve , BP-Ktrans , BP-ve , and BP-vp had the highest AUC of 0.730. CONCLUSION: Our results suggest that the contrast kinetic analysis of DCE-MRI data can be used to differentiate the malignant lesions from the benign and high-risk lesions among the indeterminate breast lesions recommended for MRI-guided biopsy exams. LEVEL OF EVIDENCE: 3 J. MAGN. RESON. IMAGING 2017;45:1385-1393.
PURPOSE: To assess the diagnostic utility of contrast kinetic analysis for breast lesions and background parenchyma of women undergoing MRI-guided biopsies, for whom standard clinical analysis had failed to separate benign and malignant lesions. MATERIALS AND METHODS: This study included 115 women who had indeterminate lesions based on routine diagnostic breast MRI exams and underwent an MRI (3 Tesla) -guided biopsy of one or more lesions suspicious for breast cancer. Breast dynamic contrast-enhanced (DCE)-MRI was performed using a radial stack-of-stars three-dimensional spoiled gradient echo pulse sequence and modified k-space weighted image contrast image reconstruction. Contrast kinetic model analysis was conducted to characterize the contrast enhancement patterns measured in lesions and background parenchyma (BP). The transfer rate (Ktrans ), interstitial volume fraction (ve ), and vascular volume fraction (vp ) estimated from the lesion and BP were used to separate malignant from benign lesions. RESULTS: The patients with malignant lesions had significantly (P < 0.05) higher median lesion-Ktrans (0.081 min-1 ), higher median BP-Ktrans (0.032 min-1 ), and BP-vp (0.020) than those without malignant lesions (0.056 min-1 , 0.017 min-1 and 0.012, respectively). The area under the receiver operating characteristic curve (AUC) of the BP-Ktrans (0.687) was highest among the single parameters and higher than that of the lesion-Ktrans (0.664). The combined logistic regression model of lesion-Ktrans , lesion-ve , BP-Ktrans , BP-ve , and BP-vp had the highest AUC of 0.730. CONCLUSION: Our results suggest that the contrast kinetic analysis of DCE-MRI data can be used to differentiate the malignant lesions from the benign and high-risk lesions among the indeterminate breast lesions recommended for MRI-guided biopsy exams. LEVEL OF EVIDENCE: 3 J. MAGN. RESON. IMAGING 2017;45:1385-1393.
Authors: Thomas E Yankeelov; William D Rooney; Wei Huang; Jonathan P Dyke; Xin Li; Alina Tudorica; Jing-Huei Lee; Jason A Koutcher; Charles S Springer Journal: NMR Biomed Date: 2005-05 Impact factor: 4.044
Authors: Alana R Amarosa; Jason McKellop; Ana Paula Klautau Leite; Melanie Moccaldi; Tess V Clendenen; James S Babb; Anne Zeleniuch-Jacquotte; Linda Moy; Sungheon Kim Journal: Radiology Date: 2013-05-08 Impact factor: 11.105
Authors: Ellen Warner; Hans Messersmith; Petrina Causer; Andrea Eisen; Rene Shumak; Donald Plewes Journal: Ann Intern Med Date: 2008-05-06 Impact factor: 25.391
Authors: Jonghyun Bae; Zhengnan Huang; Florian Knoll; Krzysztof Geras; Terlika Pandit Sood; Li Feng; Laura Heacock; Linda Moy; Sungheon Gene Kim Journal: Magn Reson Med Date: 2022-01-09 Impact factor: 4.668
Authors: Qihao Zhang; Pascal Spincemaille; Michele Drotman; Christine Chen; Sarah Eskreis-Winkler; Weiyuan Huang; Liangdong Zhou; John Morgan; Thanh D Nguyen; Martin R Prince; Yi Wang Journal: Magn Reson Imaging Date: 2021-11-06 Impact factor: 2.546
Authors: SoHyun Han; Radka Stoyanova; Hansol Lee; Sean D Carlin; Jason A Koutcher; HyungJoon Cho; Ellen Ackerstaff Journal: Magn Reson Med Date: 2017-07-20 Impact factor: 4.668
Authors: Nicole Wake; Hersh Chandarana; Henry Rusinek; Koji Fujimoto; Linda Moy; Daniel K Sodickson; Sungheon Gene Kim Journal: Magn Reson Imaging Date: 2018-05-16 Impact factor: 2.546
Authors: Corrado Tagliati; Paola Piccinni; Paola Ercolani; Elisabetta Marconi; Barbara Franca Simonetti; Gian Marco Giuseppetti; Andrea Giovagnoni Journal: Pol J Radiol Date: 2021-04-30