22 January 2018 Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial
Anna G. Sorace, Savannah C. Partridge, Xia Li, Jack Virostko, Stephanie L. Barnes, Daniel S. Hippe, Wei Huang, Thomas E. Yankeelov
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Abstract
Comparative preliminary analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data collected in the International Breast MR Consortium 6883 multicenter trial was performed to distinguish benign and malignant breast tumors. Prebiopsy DCE-MRI data from 45 patients with suspicious breast lesions were obtained. Semiquantitative mean signal-enhancement ratio ( SERmean) was calculated for all lesions, and quantitative pharmacokinetic, parameters Ktrans, kep, and ve, were calculated for the subset with available T1 maps ( n=35). Diagnostic performance was estimated for DCE-MRI parameters and compared to standard clinical MRI assessment. Quantitative and semiquantitative metrics discriminated benign and malignant lesions, with receiver operating characteristic area under the curve (AUC) values of 0.71, 0.70, and 0.82 for Ktrans, kep, and SERmean, respectively ( p<0.05). At equal 94% sensitivity, the specificity and positive predictive value of SERmean (53% and 63%, respectively) and Ktrans (42% and 58%) were higher than clinical MRI assessment (32% and 54%).
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2018/$25.00 © 2018 SPIE
Anna G. Sorace, Savannah C. Partridge, Xia Li, Jack Virostko, Stephanie L. Barnes, Daniel S. Hippe, Wei Huang, and Thomas E. Yankeelov "Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial," Journal of Medical Imaging 5(1), 011019 (22 January 2018). https://doi.org/10.1117/1.JMI.5.1.011019
Received: 19 June 2017; Accepted: 18 December 2017; Published: 22 January 2018
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Cited by 24 scholarly publications.
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KEYWORDS
Magnetic resonance imaging

Breast

Diagnostics

Data modeling

Tumors

Tumor growth modeling

Temporal resolution

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