OBJECTIVES: The use of breast magnetic resonance imaging (MRI) as screening tool has been stalled by high examination costs. Scan protocols have lengthened to optimize specificity. Modern view-sharing sequences now enable ultrafast dynamic whole-breast MRI, allowing much shorter and more cost-effective procedures. This study evaluates whether dynamic information from ultrafast breast MRI can be used to replace standard dynamic information to preserve accuracy. MATERIALS AND METHODS: We interleaved 20 ultrafast time-resolved angiography with stochastic trajectory (TWIST) acquisitions (0.9 × 1 × 2.5 mm, temporal resolution, 4.3 seconds) during contrast inflow in a regular high-resolution dynamic MRI protocol. A total of 160 consecutive patients with 199 enhancing abnormalities (95 benign and 104 malignant) were included. The maximum slope of the relative enhancement versus time curve (MS) obtained from the TWIST and curve type obtained from the regular dynamic sequence as defined in the breast imaging reporting and data system (BIRADS) lexicon were recorded. Diagnostic performance was compared using receiver operating characteristic analysis. RESULTS: All lesions were visible on both the TWIST and standard series. Maximum slope allows discrimination between benign and malignant disease with high accuracy (area under the curve, 0.829). Types of MS were defined in analogy to BIRADS curve types: MS type 3 implies a high risk of malignancy (MS >13.3%/s; specificity, 85%), MS type 2 yields intermediate risk (MS <13.3%/s and >6.4%/s), and MS type 1 implies a low risk (MS <6.4%/s; sensitivity, 90%). This simplification provides a much higher accuracy than the much lengthier BIRADS curve type analysis does (area under the curve, 0.812 vs 0.692; P = 0.0061). CONCLUSIONS: Ultrafast dynamic breast MRI allows detection of breast lesions and classification with high accuracy using MS. This allows substantial shortening of scan protocols and hence reduces imaging costs, which is beneficial especially for screening.
OBJECTIVES: The use of breast magnetic resonance imaging (MRI) as screening tool has been stalled by high examination costs. Scan protocols have lengthened to optimize specificity. Modern view-sharing sequences now enable ultrafast dynamic whole-breast MRI, allowing much shorter and more cost-effective procedures. This study evaluates whether dynamic information from ultrafast breast MRI can be used to replace standard dynamic information to preserve accuracy. MATERIALS AND METHODS: We interleaved 20 ultrafast time-resolved angiography with stochastic trajectory (TWIST) acquisitions (0.9 × 1 × 2.5 mm, temporal resolution, 4.3 seconds) during contrast inflow in a regular high-resolution dynamic MRI protocol. A total of 160 consecutive patients with 199 enhancing abnormalities (95 benign and 104 malignant) were included. The maximum slope of the relative enhancement versus time curve (MS) obtained from the TWIST and curve type obtained from the regular dynamic sequence as defined in the breast imaging reporting and data system (BIRADS) lexicon were recorded. Diagnostic performance was compared using receiver operating characteristic analysis. RESULTS: All lesions were visible on both the TWIST and standard series. Maximum slope allows discrimination between benign and malignant disease with high accuracy (area under the curve, 0.829). Types of MS were defined in analogy to BIRADS curve types: MS type 3 implies a high risk of malignancy (MS >13.3%/s; specificity, 85%), MS type 2 yields intermediate risk (MS <13.3%/s and >6.4%/s), and MS type 1 implies a low risk (MS <6.4%/s; sensitivity, 90%). This simplification provides a much higher accuracy than the much lengthier BIRADS curve type analysis does (area under the curve, 0.812 vs 0.692; P = 0.0061). CONCLUSIONS: Ultrafast dynamic breast MRI allows detection of breast lesions and classification with high accuracy using MS. This allows substantial shortening of scan protocols and hence reduces imaging costs, which is beneficial especially for screening.
Authors: Laura Heacock; Alana A Lewin; Yiming Gao; James S Babb; Samantha L Heller; Amy N Melsaether; Neeti Bagadiya; Sungheon G Kim; Linda Moy Journal: J Magn Reson Imaging Date: 2017-11-27 Impact factor: 4.813
Authors: Courtney K Morrison; Leah C Henze Bancroft; Wendy B DeMartini; James H Holmes; Kang Wang; Ryan J Bosca; Frank R Korosec; Roberta M Strigel Journal: Invest Radiol Date: 2017-04 Impact factor: 6.016
Authors: Laura Heacock; Yiming Gao; Samantha L Heller; Amy N Melsaether; James S Babb; Tobias K Block; Ricardo Otazo; Sungheon G Kim; Linda Moy Journal: J Magn Reson Imaging Date: 2016-11-17 Impact factor: 4.813