Jie Ding1, Alison T Stopeck2,3, Yi Gao4,5,6, Marilyn T Marron7, Betsy C Wertheim7, Maria I Altbach7,8, Jean-Philippe Galons7,8, Denise J Roe7,9, Fang Wang3, Gertraud Maskarinec10, Cynthia A Thomson7,11, Patricia A Thompson3,12, Chuan Huang1,3,13,14,15. 1. Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA. 2. Department of Hematology and Oncology, Stony Brook Medicine, Stony Brook, New York, USA. 3. Stony Brook University Cancer Center, Stony Brook, New York, USA. 4. School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China. 5. Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China. 6. Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA. 7. University of Arizona Cancer Center, Tucson, Arizona, USA. 8. Department of Medical Imaging, University of Arizona, Tucson, Arizona, USA. 9. Department of Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona, USA. 10. University of Hawaii Cancer Center, Honolulu, Hawaii, USA. 11. Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona, USA. 12. Department of Pathology, Stony Brook Medicine, Stony Brook, New York, USA. 13. Department of Radiology, Stony Brook Medicine, Stony Brook, New York, USA. 14. Department of Psychiatry, Stony Brook Medicine, Stony Brook, New York, USA. 15. Department of Computer Science, Stony Brook University, Stony Brook, New York, USA.
Abstract
BACKGROUND: Increased breast density is a significant independent risk factor for breast cancer, and recent studies show that this risk is modifiable. Hence, breast density measures sensitive to small changes are desired. PURPOSE: Utilizing fat-water decomposition MRI, we propose an automated, reproducible breast density measurement, which is nonionizing and directly comparable to mammographic density (MD). STUDY TYPE: Retrospective study. POPULATION: The study included two sample sets of breast cancer patients enrolled in a clinical trial, for concordance analysis with MD (40 patients) and reproducibility analysis (10 patients). FIELD STRENGTH/SEQUENCE: The majority of MRI scans (59 scans) were performed with a 1.5T GE Signa scanner using radial IDEAL-GRASE sequence, while the remaining (seven scans) were performed with a 3T Siemens Skyra using 3D Cartesian 6-echo GRE sequence with a similar fat-water separation technique. ASSESSMENT: After automated breast segmentation, breast density was calculated using FraGW, a new measure developed to reliably reflect the amount of fibroglandular tissue and total water content in the entire breast. Based on its concordance with MD, FraGW was calibrated to MR-based breast density (MRD) to be comparable to MD. A previous breast density measurement, Fra80-the ratio of breast voxels with <80% fat fraction-was also calculated for comparison with FraGW. STATISTICAL TESTS: Pearson correlation was performed between MD (reference standard) and FraGW (and Fra80). Test-retest reproducibility of MRD was evaluated using the difference between test-retest measures (Δ1-2 ) and intraclass correlation coefficient (ICC). RESULTS: Both FraGW and Fra80 were strongly correlated with MD (Pearson ρ: 0.96 vs. 0.90, both P < 0.0001). MRD converted from FraGW showed higher test-retest reproducibility (Δ1-2 variation: 1.1% ± 1.2%; ICC: 0.99) compared to MD itself (literature intrareader ICC ≤0.96) and Fra80. DATA CONCLUSION: The proposed MRD is directly comparable with MD and highly reproducible, which enables the early detection of small breast density changes and treatment response. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;48:971-981.
RCT Entities:
BACKGROUND: Increased breast density is a significant independent risk factor for breast cancer, and recent studies show that this risk is modifiable. Hence, breast density measures sensitive to small changes are desired. PURPOSE: Utilizing fat-water decomposition MRI, we propose an automated, reproducible breast density measurement, which is nonionizing and directly comparable to mammographic density (MD). STUDY TYPE: Retrospective study. POPULATION: The study included two sample sets of breast cancerpatients enrolled in a clinical trial, for concordance analysis with MD (40 patients) and reproducibility analysis (10 patients). FIELD STRENGTH/SEQUENCE: The majority of MRI scans (59 scans) were performed with a 1.5T GE Signa scanner using radial IDEAL-GRASE sequence, while the remaining (seven scans) were performed with a 3T Siemens Skyra using 3D Cartesian 6-echo GRE sequence with a similar fat-water separation technique. ASSESSMENT: After automated breast segmentation, breast density was calculated using FraGW, a new measure developed to reliably reflect the amount of fibroglandular tissue and total water content in the entire breast. Based on its concordance with MD, FraGW was calibrated to MR-based breast density (MRD) to be comparable to MD. A previous breast density measurement, Fra80-the ratio of breast voxels with <80% fat fraction-was also calculated for comparison with FraGW. STATISTICAL TESTS: Pearson correlation was performed between MD (reference standard) and FraGW (and Fra80). Test-retest reproducibility of MRD was evaluated using the difference between test-retest measures (Δ1-2 ) and intraclass correlation coefficient (ICC). RESULTS: Both FraGW and Fra80 were strongly correlated with MD (Pearson ρ: 0.96 vs. 0.90, both P < 0.0001). MRD converted from FraGW showed higher test-retest reproducibility (Δ1-2 variation: 1.1% ± 1.2%; ICC: 0.99) compared to MD itself (literature intrareader ICC ≤0.96) and Fra80. DATA CONCLUSION: The proposed MRD is directly comparable with MD and highly reproducible, which enables the early detection of small breast density changes and treatment response. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;48:971-981.
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