Quantitative analysis of vascular properties derived from ultrafast DCE‐MRI to discriminate malignant and benign breast tumors

Abstract

Purpose We propose a novel methodology to integrate morphological and functional information of tumor‐associated vessels to assist in the diagnosis of suspicious breast lesions. Theory and Methods Ultrafast, fast, and high spatial resolution DCE‐MRI data were acquired on 15 patients with suspicious breast lesions. Segmentation of the vasculature from the surrounding tissue was performed by applying a Hessian filter to the enhanced image to generate a map of the probability for each voxel to belong to a vessel. Summary measures were generated for vascular morphology, as well as the inputs and outputs of vessels physically connected to the tumor. The ultrafast DCE‐MRI data was analyzed by a modified Tofts model to estimate the bolus arrival time, Ktrans (volume transfer coefficient), and vp (plasma volume fraction). The measures were compared between malignant and benign lesions via the Wilcoxon test, and then incorporated into a logistic ridge regression model to assess their combined diagnostic ability. Results A total of 24 lesions were included in the study (13 malignant and 11 benign). The vessel count, Ktrans, and vp showed significant difference between malignant and benign lesions (P = 0.009, 0.034, and 0.010, area under curve [AUC] = 0.76, 0.63, and 0.70, respectively). The best multivariate logistic regression model for differentiation included the vessel count and bolus arrival time (AUC = 0.91). Conclusion This study provides preliminary evidence that combining quantitative characterization of morphological and functional features of breast vasculature may provide an accurate means to diagnose breast cancer.

Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization

“…At a lower field strength, one has to compromise on using either a high temporal resolution suitable for PK analysis or a high spatial resolution for detailed morphologic assessment. Our findings are in good agreement with prior studies at 1.5 and 3T, which demonstrated the potential of PK parameters to aid in the differentiation of benign from malignant tumors [ 14 , 18 , 32 , 44 , 45 , 46 , 47 ]. We integrated the information from quantitative PK analysis to established reporting guidelines, i.e., BI-RADS, showing that PK-enhanced BI-RADS improved diagnostic accuracy from 88.4% to 97.7% by reducing false-positive findings.…”

New parameters of ultrafast dynamic contrast‐enhanced breast MRI using compressed sensing

“…In the earlier studies, the presence of vessels adjacent to breast cancer or an increase in the number of vessels in the cancer‐bearing breast are said to be associated with a poor prognosis . A recent study suggested that the diagnostic ability of breast MRI could be improved by knowing the number of vessels associated with lesions using both UF‐DCE MRI and high‐resolution CE MRI . Combined evaluation of the number of vessels and AVI may further increase the diagnostic accuracy of breast MRI.…”

Multiparametric Analysis of Longitudinal Quantitative MRI Data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer

“…To reduce systematic errors in the model fits, the AIF was calibrated for each DCE-MRI scan to segmented skeletal muscle tissue [ 76 ] prior to fitting for pharmacokinetic parameters. Additionally, each tumor voxel time-course was fit to a half-logistic function to determine the bolus arrival-time, which was used to shift the calibrated AIF on a voxel-wise basis to align the enhancement time of C p ( t ) with the bolus arrival-time [ 77 ]. Voxel-wise fits to the standard Kety-Tofts model were bound within the physiological range for each parameter (e.g., 0 to 5 min −1 for K trans , 0 to 1 for v e ).…”