Differentiation between glioblastomas and solitary brain metastases using diffusion tensor imaging

Abstract

The purpose of this study is to determine whether diffusion tensor imaging (DTI) metrics including tensor shape measures such as linear and planar anisotropy coefficients (CL and CP) can help differentiate glioblastomas from solitary brain metastases. Sixty-three patients with histopathologic diagnosis of glioblastomas (22 men, 16 women, mean age 58.4 years) and brain metastases (13 men, 12 women, mean age 56.3 years) were included in this study. Contrast-enhanced T1-weighted, fluid-attenuated inversion recovery (FLAIR) images, fractional anisotropy (FA), apparent diffusion coefficient (ADC), CL and CP maps were co-registered and each lesion was semi-automatically subdivided into four regions: central, enhancing, immediate peritumoral and distant peritumoral. DTI metrics as well as the normalized signal intensity from the contrast-enhanced T1-weighted images were measured from each region. Univariate and multivariate logistic regression analyses were employed to determine the best model for classification. The results demonstrated that FA, CL and CP from glioblastomas were significantly higher than those of brain metastases from all segmented regions (p < 0.05), and the differences from the enhancing regions were most significant (p < 0.001). FA and CL from the enhancing region had the highest prediction accuracy when used alone with an area under the curve of 0.90. The best logistic regression model included three parameters (ADC, FA and CP) from the enhancing part, resulting in 92% sensitivity, 100% specificity and area under the curve of 0.98. We conclude that DTI metrics, used individually or combined, have a potential as a non-invasive measure to differentiate glioblastomas from metastases.

Introduction

Glioblastomas and brain metastases (according to the WHO 2007 classification) are the two most common brain neoplasms in adults (Louis et al., 2007). The management of these two neoplasms is vastly different and can potentially affect the clinical outcome (Giese and Westphal, 2001, Soffietti et al., 2002). In general, differentiation of these two neoplasms is possible based on the clinical history or presence of multiple enhancing lesions (Tang et al., 2006, Zhang and Olsson, 1997). However, distinction remains challenging when the patient presents with a solitary enhancing mass as both glioblastomas and metastases may exhibit ring-enhancement and extensive edema on magnetic resonance imaging (MRI) (Schiff, 2001). In addition, a solitary brain mass may be the first manifestation of disease in about 30% of patients with systemic cancer (Schiff, 2001).

Diffusion tensor imaging (DTI) has been used to study pathologic changes in brain tumors (Field et al., 2004, Rumboldt et al., 2006, Stadlbauer et al., 2006, Yamasaki et al., 2005). It has also been applied in differentiating glioblastomas from metastases, however, with mixed results (Calli et al., 2006, Lu et al., 2003, Lu et al., 2004, Morita et al., 2005, Oh et al., 2005, Tsuchiya et al., 2005, Yamasaki et al., 2005). Some reports have suggested that apparent diffusion coefficient (ADC) (Lu et al., 2003, Lu et al., 2004, Morita et al., 2005) and fractional anisotropy (FA) (Lu et al., 2004) are helpful for the differentiation, while others indicated the limited use of ADC (Calli et al., 2006, Oh et al., 2005, Yamasaki et al., 2005) and FA (Lu et al., 2003, Tsuchiya et al., 2005) for the differentiation. These conflicting results may be due to the differences in acquisition and analytical methods employed, achievable signal to noise ratio (SNR), gradient directions used, motion and eddy current artifacts that are typically observed on DTI images as well as the heterogeneous nature of brain neoplasm. It can also be noted that ADC and FA constitute only a fraction of the information available from DTI measurements. More detailed features of the tensor shape, such as linear and planar anisotropy coefficients (CL and CP) (Alexander et al., 2000, Westin et al., 1997, Westin et al., 2002) may further elucidate tissue characterization as previously reported for brain tumors (Kim et al., 2007).

Most earlier DTI studies have focused on the peritumoral region that lies just outside the contrast-enhancing region for detection of differences in tumor infiltration between glioblastomas and metastases (Cha, 2006, Lu et al., 2003, Lu et al., 2004, Morita et al., 2005). However, to date, there has been no report on Systematic measurements of DTI metrics from different regions of the tumor, which might be a more robust way of characterizing brain neoplasms. The enhancing neoplastic mass can be generally categorized into two sub-regions; the contrast-enhancing region representing the solid part of the tumor, and the central area with no or slight enhancement representing necrotic or cystic part of the tumor. Similarly, the edematous region can also be separated into another two sub-regions; the regions surrounding the enhancing part of the tumor potentially including infiltrative tumor cells, and the more distal regions mainly comprised of vasogenic edema.

Therefore, in this study, we hypothesized that diffusion tensor characteristics of glioblastomas are different from brain metastases in different regions of the tumor. We also hypothesized that these two tumor types can be differentiated based on the DTI metrics measured from one or more of these sub-regions of the tumor. In order to achieve this goal, we developed a semi-automatic segmentation method to delineate different regions of the tumor based on conventional MRI. DTI metrics from segmented regions were combined to generate an optimal regression model to differentiate these two tumors.