Relationship of temporal resolution to diagnostic performance for dynamic contrast enhanced MRI of the breast

Abstract

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 ¼ 48) and 3.0T (n ¼ 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 ¼ 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. CONTRAST-ENHANCED (CE) MRI has proven to be an important tool in detecting and characterizing breast lesions with a sensitivity greater than 90% but widely varying specificity ranging from 30 to 84% (1-4). Dynamic contrast-enhanced (DCE) MRI has been shown to improve the specificity of MRI in the diagnosis of breast cancer (5). The most widely described features of the DCE MRI analysis is the uptake and wash-out patterns of the gadolinium contrast agent in regions of suspected abnormalities. These patterns are typically qualitatively classified as: (i) persistently enhancing type suggestive of benignity, (ii) wash-out type suggestive of malignancy, and (iii) plateau type representing an intermediate probability. Wash-in characteristics have also been related to malignant versus benign histology, depending on the slope of the uptake curve (6). However, DCE MRI breast protocols can vary in temporal resolution, and there is a need to define the optimal limit that is required for correct classification and diagnosis of lesions.Current MRI scanners and newest pulse sequences can rapidly image the entire breast in 15 s or less. However, this high temporal resolution is at the expense of reduced spatial resolution and/or signalto-noise (SNR) ratio that are important for morphologic assessment of the s...

Gender Differences in Regional Brain Activity in Patients with Chronic Primary Insomnia: Evidence from a Resting-State fMRI Study

“…31 In the present study, the ROC analysis revealed that the different ALFF areas of both the PCPI-Fs and the PCPI-Ms showed high AUC values and high degree of sensitivities and specificities. However, the different ALFF areas of total PCPIs showed one poor, two fair, and one good AUC value, with neither high degree of sensitivity nor specificity.…”

The effect of temporal sampling on quantitative pharmacokinetic and three-time-point analysis of breast DCE-MRI

“…As seen in Fig. 6, the literature classifier used in conjunction with the 3TP method yields good results (AUCN0.8) regardless of the temporal resolution and is comparable to similar studies with other semiquantitative analysis methods [34]. As mentioned in Ref.…”

“…As with previous analyses of various diagnostic techniques [18,34], the pathology-proven diagnoses allowed the selection of a retrospectively determined classifier for optimally distinguishing benign and malignant lesions. This was especially useful for the ETK analysis, where no consensus has been reached on how to classify lesions based on the estimated parameters.…”

Automated Assessment of Children’s Postoperative Pain Using Computer Vision

“…AUC scores demonstrated good-to-excellent 41 Inclusion of demographic data as independent variables did not alter binary pain classification model performance.…”