RATIONALE AND OBJECTIVES: Neoadjuvant systemic therapy (NST) is the standard treatment for locally advanced breast cancer and a common option for primary operable disease. It is important to develop standardized imaging techniques that can monitor and quantify response to NST enabling treatment tailored to each individual patient, and facilitating surgical planning. Here we present a high spatial resolution, parametric method based on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), which evaluates breast cancer response to NST. MATERIALS AND METHODS: DCE-MRI examinations were performed twice on 17 breast cancer patients, before and after treatment. Seven sets of axial breast images were sequentially recorded at 1.5 Tesla applying a three-dimensional, gradient echo at a spatial resolution approximately 2 x 1.2 x 0.6 mm(3) and temporal resolution approximately 2 minutes, using gadopentate dimeglumine (0.1 mmol/kg wt). Image analysis was based on a color-coded scheme related to physiologic perfusion parameters. RESULTS: A high Pearson correlation coefficient of 0.96 (P < .0001) was found between the histopathologic estimation of viable neoplastic tissue volume and the segmented volume of all the pixels demonstrating fast and steady state washout after NST (colored in light red and green). Segmentation of these pixels before and after NST indicated response in terms of reduced tumor volume and a parallel decrease in enhancement rate which reflects diminished transcapillary transfer of the contrast agent. CONCLUSIONS: The use of a parametric MRI technique provided a means to standardize segmentation and quantify changes in the perfusion of breast neoplastic tissue in response to NST. Whether this technique can serve to predict breast cancer recurrence and survival rates requires further clinical testing.
RATIONALE AND OBJECTIVES: Neoadjuvant systemic therapy (NST) is the standard treatment for locally advanced breast cancer and a common option for primary operable disease. It is important to develop standardized imaging techniques that can monitor and quantify response to NST enabling treatment tailored to each individual patient, and facilitating surgical planning. Here we present a high spatial resolution, parametric method based on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), which evaluates breast cancer response to NST. MATERIALS AND METHODS: DCE-MRI examinations were performed twice on 17 breast cancerpatients, before and after treatment. Seven sets of axial breast images were sequentially recorded at 1.5 Tesla applying a three-dimensional, gradient echo at a spatial resolution approximately 2 x 1.2 x 0.6 mm(3) and temporal resolution approximately 2 minutes, using gadopentate dimeglumine (0.1 mmol/kg wt). Image analysis was based on a color-coded scheme related to physiologic perfusion parameters. RESULTS: A high Pearson correlation coefficient of 0.96 (P < .0001) was found between the histopathologic estimation of viable neoplastic tissue volume and the segmented volume of all the pixels demonstrating fast and steady state washout after NST (colored in light red and green). Segmentation of these pixels before and after NST indicated response in terms of reduced tumor volume and a parallel decrease in enhancement rate which reflects diminished transcapillary transfer of the contrast agent. CONCLUSIONS: The use of a parametric MRI technique provided a means to standardize segmentation and quantify changes in the perfusion of breast neoplastic tissue in response to NST. Whether this technique can serve to predict breast cancer recurrence and survival rates requires further clinical testing.
Authors: Xia Li; Richard G Abramson; Lori R Arlinghaus; Hakmook Kang; Anuradha Bapsi Chakravarthy; Vandana G Abramson; Jaime Farley; Ingrid A Mayer; Mark C Kelley; Ingrid M Meszoely; Julie Means-Powell; Ana M Grau; Melinda Sanders; Thomas E Yankeelov Journal: Invest Radiol Date: 2015-04 Impact factor: 6.016
Authors: Stefan Zwick; Gunnar Brix; Paul S Tofts; Ralph Strecker; Annette Kopp-Schneider; Hendrik Laue; Wolfhard Semmler; Fabian Kiessling Journal: Eur Radiol Date: 2009-09-01 Impact factor: 5.315
Authors: Robert E Lee; E Brian Welch; Jared G Cobb; Tuhin Sinha; John C Gore; Thomas E Yankeelov Journal: J Digit Imaging Date: 2008-04-30 Impact factor: 4.056
Authors: Hakmook Kang; Allison Hainline; Lori R Arlinghaus; Stephanie Elderidge; Xia Li; Vandana G Abramson; Anuradha Bapsi Chakravarthy; Richard G Abramson; Brian Bingham; Kareem Fakhoury; Thomas E Yankeelov Journal: J Med Imaging (Bellingham) Date: 2017-12-29
Authors: Xia Li; E Brian Welch; A Bapsi Chakravarthy; Lei Xu; Lori R Arlinghaus; Jaime Farley; Ingrid A Mayer; Mark C Kelley; Ingrid M Meszoely; Julie Means-Powell; Vandana G Abramson; Ana M Grau; John C Gore; Thomas E Yankeelov Journal: Magn Reson Med Date: 2011-11-29 Impact factor: 4.668
Authors: Lori R Arlinghaus; Xia Li; A Ridwan Rahman; E Brian Welch; Lei Xu; John C Gore; Thomas E Yankeelov Journal: Magn Reson Imaging Date: 2011-04-29 Impact factor: 2.546
Authors: Katherine D Watson; Xiaowen Hu; Chun-Yen Lai; Heather A Lindfors; Dana D Hu-Lowe; Theresa A Tuthill; David R Shalinsky; Katherine W Ferrara Journal: Ultrasound Med Biol Date: 2011-04-30 Impact factor: 2.998
Authors: Xia Li; Hakmook Kang; Lori R Arlinghaus; Richard G Abramson; A Bapsi Chakravarthy; Vandana G Abramson; Jaime Farley; Melinda Sanders; Thomas E Yankeelov Journal: Transl Oncol Date: 2014-02-01 Impact factor: 4.243
Authors: Richard G Abramson; Lori R Arlinghaus; Jared A Weis; Xia Li; Adrienne N Dula; Eduard Y Chekmenev; Seth A Smith; Michael I Miga; Vandana G Abramson; Thomas E Yankeelov Journal: Breast Cancer (Dove Med Press) Date: 2012-10