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Published on: December 15, 2014
Relationship of temporal resolution to diagnostic performance for dynamic contrast enhanced MRI of the breast
Riham H El Khouli1, Katarzyna J Macura, Peter B Barker
1National Institutes of Health, Department of Radiology and Imaging Sciences, Bethesda, Maryland, USA.
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.
Insights
Dynamic contrast-enhanced MRI (DCE-MRI) with a temporal resolution of at least 45 seconds improves the accuracy of classifying breast lesions as benign or malignant. This enhanced temporal resolution maximizes agreement between kinetic parameters and diagnosis.
Area of Science:
- Radiology and Medical Imaging
- Oncology
- Biomedical Engineering
Background:
- Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is crucial for breast lesion characterization.
- The temporal resolution of DCE-MRI acquisition impacts the reliability of kinetic parameter analysis.
- Optimizing temporal resolution is key to improving diagnostic accuracy in breast MRI.
Purpose of the Study:
- To evaluate the impact of varying temporal resolutions in DCE-MRI on the classification accuracy of breast lesions.
- To determine the optimal temporal resolution for distinguishing benign from malignant breast lesions using kinetic analysis.
Main Methods:
- T(1)-weighted DCE-MRI was performed on 81 patients (1.5T and 3.0T scanners) with 15 s/acquisition temporal resolution.
- Temporal resolution was post-processed to 30, 45, and 60 s/acquisition by data reduction.
- Kinetic parameters (wash-in, wash-out, curve shape) were analyzed using logistic regression and ROC analysis for diagnostic accuracy.
Main Results:
- All analyzed temporal resolutions significantly predicted lesion malignancy (P < 0.05).
- DCE-MRI datasets with 45 s/acquisition temporal resolution demonstrated higher diagnostic accuracy compared to 60 s/acquisition.
- ROC analysis showed improved accuracy for wash-in slope (0.72 vs. 0.69), wash-out slope (0.85 vs. 0.82), and kinetic curve shape (0.88 vs. 0.80) at 45 s/acquisition versus 60 s/acquisition.
Conclusions:
- A temporal resolution of at least 45 seconds in DCE-MRI maximizes the agreement between kinetic parameters and the accurate classification of breast lesions.
- Slower temporal resolutions (e.g., 60 s/acquisition) may reduce diagnostic performance in differentiating benign from malignant breast lesions.
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