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Relationship of temporal resolution to diagnostic performance for dynamic contrast enhanced MRI of the breast
Open accessAbstract
Purpose: To investigate the relationship between temporal resolution of dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) and classification of breast lesions as benign versus malignant. Materials and Methods: Patients underwent T 1‐weighted DCE MRI with 15 s/acquisition temporal resolution using 1.5 Tesla (n = 48) and 3.0T (n = 33) MRI scanners. Seventy‐nine patients had pathologically proven diagnosis and 2 had 2 years follow‐up showing no change in lesion size. The temporal resolution of DCE MRI was systematically reduced as a postprocessing step from 15 to 30, 45, and 60 s/acquisition by eliminating intermediate time points. Average wash‐in and wash‐out slopes, wash‐out percentage changes, and kinetic curve shape (persistently enhancing, plateau, or wash‐out) were compared for each temporal resolution. Logistic regression and receiver operating characteristic (ROC) curve analysis were used to compare kinetic parameters and diagnostic accuracy. Results: Sixty patients (74%) had malignant lesions and 21 patients (26%) had benign lesions. All temporal‐resolution parameters significantly predicted benign versus malignant diagnosis (P < 0.05). However, 45 s/acquisition and higher temporal‐resolution datasets showed higher accuracy than the 60 s/acquisition dataset by ROC curve analysis (0.72 versus 0.69 for average wash‐in slope; 0.85 versus 0.82, for average wash‐out slope; and 0.88 versus 0.80 for kinetic curve shape assessment, for 45 s/acquisition versus 60 s/acquisition temporal‐resolution datasets, respectively (P = 0.027). Conclusion: DCE MRI data with at least 45‐s temporal resolution maximized the agreement between the kinetic parameters and correct classification of benign versus malignant diagnosis. J. Magn. Reson. Imaging 2009;30:999–1004. © 2009 Wiley‐Liss, Inc.
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- 10.1148/radiol.2322030547 · DOI
- 10.1007/BF02678476 · DOI
- 10.1007/s003300000745 · DOI
- 10.1055/s-2007-1000448 · DOI
- 10.1016/j.breast.2005.05.002 · DOI
- 10.1148/radiology.211.1.r99ap38101 · DOI
- 10.1001/jama.292.22.2735 · DOI
- 10.1148/radiology.193.3.7972823 · DOI
- 10.1016/0730-725X(93)90002-U · DOI
- 10.1148/radiol.2363040811 · DOI
- 10.1097/01.rli.0000163741.16718.3e · DOI
- 10.1002/mrm.1910330416 · DOI
- 10.1080/02841850500539033 · DOI
- 10.1148/radiol.2291010898 · DOI
- 10.1016/S0001-2998(78)80014-2 · DOI
- Cabin RJ, 2000, To Bonferroni or not to Bonferroni: when and how are the questions, ESA Bull, 81, 246
- 10.2214/ajr.175.1.1750035 · DOI
- 10.1148/radiol.2381042117 · DOI
- 10.1002/jmri.10380 · DOI
- 10.1016/S1076-6332(03)80683-9 · DOI
- 10.1148/radiology.219.2.r01ma44484 · DOI
- 10.1148/radiology.202.3.9051042 · DOI
- 10.2214/ajr.169.2.9242744 · DOI
- 10.1002/jmri.1880070113 · DOI
- 10.1109/MEMB.2004.1360410 · DOI
- 10.1002/(SICI)1522-2586(199909)10:3<223::AID-JMRI2>3.0.CO;2-S · DOI
- 10.1002/jmri.10399 · DOI
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Metadata
- Authors
- El Khouli, R. H., Macura, K. J., Barker, P. B., Habba, M. R., Jacobs, M. A., & Bluemke, D. A.
- Affiliations
- Johns Hopkins University (US)
- Suez Canal University (EG)
- National Institutes of Health (US)
- Corresponding author
- Bluemke, David A.
- Venue
- Journal of Magnetic Resonance Imaging
- Publisher
- Wiley
- Published
- 2009-10-23
- Volume
- 30
- Issue
- 5
- Pages
- 999-1004
- ISSN
- 1053-1807 (ISSN-L)
- 1522-2586
- PMC
- PMC2935260
- OpenAlex
- W1984236853
- Cited by
- 213
- References
- 0
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 27 · 2023: 33 · 2022: 21 · 2021: 14 · 2020: 11 · 2019: 13 · 2018: 19 · 2017: 10 · 2016: 13 · 2015: 7 · 2014: 4 · 2013: 6 · 2012: 4
- FWCI
- 3.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (1ziaeb000065-01)
- National Cancer Institute (R01CA125258, R01CA100184, P50CA103175)
- NIH Clinical Center
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Health Sciences › Medicine › Pathology and Forensic Medicine › Breast Lesions and Carcinomas
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Digital Radiography and Breast Imaging
- Keywords
- dynamic contrast-enhanced MRI, breast MRI, temporal resolution, breast lesion classification, benign versus malignant breast lesions, diagnostic accuracy, T1-weighted imaging
- MeSH terms
- Adult, Aged, Breast, Contrast Media, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Kinetics, Magnetic Resonance Imaging, Middle Aged, Retrospective Studies, Time Factors, Reproducibility of Results, Area Under Curve
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