Application of whole‐lesion histogram analysis of pharmacokinetic parameters in dynamic contrast‐enhanced MRI of breast lesions with the CAIPIRINHA‐Dixon‐TWIST‐VIBE technique

Abstract

Purpose: To investigate the application of whole-lesion histogram analysis of pharmacokinetic parameters for differentiating malignant from benign breast lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Materials and Methods: In all, 92 women with 97 breast lesions (26 benign and 71 malignant lesions) were enrolled in this study. Patients underwent dynamic breast MRI at 3T using a prototypical CAIPIRINHA-Dixon-TWIST-VIBE (CDT-VIBE) sequence and a subsequent surgery or biopsy. Inflow rate of the agent between plasma and interstitium (K trans ), outflow rate of agent between interstitium and plasma (K ep ), extravascular space volume per unit volume of tissue (v e ) including mean value, 25th/50th/75th/90th percentiles, skewness, and kurtosis were then calculated based on the whole lesion. A single-sample Kolmogorov-Smirnov test, paired t-test, and receiver operating characteristic curve (ROC) analysis were used for statistical analysis. Results: Malignant breast lesions had significantly higher K trans , K ep , and lower v e in mean values, 25th/50th/75th/90th percentiles, and significantly higher skewness of v e than benign breast lesions (all P < 0.05). There was no significant difference in kurtosis values between malignant and benign breast lesions (all P > 0.05). The 90th percentile of K trans , the 90th percentile of K ep , and the 50th percentile of v e showed the greatest areas under the ROC curve (AUC) for each pharmacokinetic parameter derived from DCE-MRI. The 90th percentile of K ep achieved the highest AUC value (0.927) among all histogram-derived values. Conclusion: The whole-lesion histogram analysis of pharmacokinetic parameters can improve the diagnostic accuracy of breast DCE-MRI with the CDT-VIBE technique. The 90th percentile of K ep may be the best indicator in differentiation between malignant and benign breast lesions. Level of Evidence: 4 Technical Efficacy Stage: 2 J. MAGN. RESON. IMAGING 2018;47:91-96. B reast cancer is the most common malignant tumor in women worldwide. [1][2][3] The incidence of breast cancer in young women is increasingly high and has become the leading cause of cancer death in women younger than 40 years. 4,5 Therefore, it is of great importance to detect early and differentiate breast cancer, and to evaluate the therapeutic effect in the ensuing treatment cycle.Angiogenesis is a crucial biological factor involved in tumor growth, progression, and metastatic spread. 6 Newly developed vessels of tumor show immaturity due to an View this article online at wileyonlinelibrary.com.

Feasibility study of dual parametric 2D histogram analysis of breast lesions with dynamic contrast-enhanced and diffusion-weighted MRI

“…In this study, with a fairly high temporal resolution of 5.6 s, the 90th percentile of Ktrans had the highest AUC, which was also statistically higher than the 10th percentile, probably because voxels with higher Ktrans values represent the most aggressive regions of tumors with abundant blood perfusion. This is consistent with the results of a similar Ktrans histogram analysis from another study [ 12 ]. Of the Tofts model parameters, Ktrans and the extravascular extracellular space (EES) fractional volume (Ve) are directly related to fundamental physiology and require access to the absolute values of tracer concentration, whereas Kep is the ratio of Ktrans to Ve (Kep = Ktrans/Ve), which can be derived from data regarding the shape of the tracer concentration vs time.…”

“…As the results revealed, the Ktrans skewness of benign lesions was higher than that of malignant tumors, probably because the Ktrans values of benign lesions were lower than those of malignant lesions, and most voxels lie to the left, making the curve lean more to the left. Such differences might not be statistically significant in 1D histograms, as the kurtosis of ADC and the skewness of Ktrans in this study and other studies [ 12 , 20 , 32 ], but these differences are amplified in the 2D histogram.…”

“…Though not directly comparable, studies have verified that high temporal resolution sequences have better diagnostic performances than low temporal resolution sequences [ 5 , 10 ]. Recently, high temporal resolution can be achieved while spatial resolution is preserved with controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA)-Dixon-time-resolved angiography with stochastic trajectories (TWIST)-volumetric interpolated breath-hold examination (VIBE) DCE and this sequence has demonstrated promising results [ 11 , 12 ].…”

“…One possible approach is to combine DCE and DWI to obtain better performance in terms of differential diagnosis. Histogram analysis is a mathematical method [ 12 , 20 – 22 ] that excels in describing tumor heterogeneity by providing quantitative metrics. With the two state of the art acquisition sequences, CAIPIRINHA-Dixon-TWIST (CDT)-VIBE DCE and readout-segmented diffusion-weighted (RS-DWI) MRI, we proposed a dual parametric 2D histogram to combine Ktrans and ADC in this study and hypothesized that this histogram would perform better than single parametric histogram for differentiating breast lesions.…”

DCE-MRI Pharmacokinetic-Based Phenotyping of Invasive Ductal Carcinoma: A Radiomic Study for Prediction of Histological Outcomes

“…Although all of them are based on the extraction of morphological features and enhancement features from DCE-MRI, no one takes into account the radiomic evaluation of the quantitative DCE pharmacokinetic parameters ( k trans , k ep, iAUC, and ve ) that, in standard correlation analysis with mean values, have shown a good agreement with prognostic factors and TN subtypes [ 59 ]. To overcome this approach and take into account lesion heterogeneity, Li et al [ 60 ] performed a histogram-based analysis for differentiating benign and malignant tumors.…”