Mapping pathophysiological features of breast tumors by MRI at high spatial resolution

Abstract

Magnetic resonance imaging (MRI) is a noninvasive method that reveals anatomical details in vivo and detects lesions for diagnosis. Although standard breast MRI cannot clearly delineate breast cancer, contrast-enhanced MRI enables the detection of breast masses with high sensitivity. Dynamic studies demonstrated that malignant lesions were characterized by a faster signal enhancement rate than benign ones. Dynamic MRI of human breast cancer in mice revealed high heterogeneity in the distribution of contrast-enhanced curves and derived pathophysiological features, indicating the importance of high spatial resolution. With clinical MRI, it is difficult to achieve simultaneously high spatial and temporal resolution. In previous dynamic studies, the emphasis was on high temporal resolution and mainly empiric analyses. We describe here a new model-based method that optimizes spatial resolution by using only three time points, and yet characterizes tumor heterogeneity in terms of microvascular permeability and extracellular fraction. Mapping these pathophysiological features may aid diagnosis and prognosis assessment, while the high spatial resolution may improve the capacity to detect smaller lesions. The method was tested in human breast tumors implanted in mice and in a limited number of benign and malignant breast lesions of patients.

Method for quantitative mapping of dynamic MRI contrast agent uptake in human tumors

“…The result of the fit according to Eq. [1], using the AIF for C p (t), is indicated by the solid line through the tumor data points. The value of k ep derived from this fit was 1.52 minute -1 .…”

“…However, due to the scaling factor f (see Eq. [1]) these parameters could not be expressed in absolute quantitated values. To do so, knowledge would be required not only about the T1 before Gd injection, but also about the tissue relaxivity, which generally can only be approximated (33).…”

“…Physiological specificity may be increased by including a plasma volume term (v p C p ) into the data analysis (Eq. [1]), but this will only make a valuable contribution to the analysis when this term can be determined with good precision (33). Su et al (29) proposed a method to separate contributions from blood vessels and extravascular components to the Gd uptake rate in rats, assuming a generalized AIF.…”

“…Its diagnostic value in tumor detection, identification, and staging has been shown in breast tumors (1)(2)(3), cervix tumors (4), and prostate carcinoma (5)(6)(7). Also, tumor treatment response has been monitored by DCE-MRI (8 -10), and comparison with histology showed a relationship of DCE-MRI data with angiogenic characteristics such as microvessel density (6,7,11).…”

DCE‐MRI pixel‐by‐pixel quantitative curve pattern analysis and its application to osteosarcoma

“…Many researchers have reported that the pattern of the signal intensity curve can be characteristic of the type and degree of breast tumors (14,31), bone sarcomas (32), and myocardial infarctions (33). The parameters used in those studies may lack repeatability or stability for different acquisition protocols or scanners, which could prevent further wide application.…”