Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas

Abstract

Purpose

Tumor grading is very important both in treatment decision and evaluation of prognosis. While tissue samples are obtained as part of most therapeutic approaches, factors that may result in inaccurate grading due to sampling error (namely, heterogeneity in tissue sampling, as well as tumor-grade heterogeneity within the same tumor specimen), have led to a desire to use imaging better to ascertain tumor grade. The purpose in our study was to evaluate the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC), and accuracy of diffusion-weighted MR imaging (DWI), proton MR spectroscopic imaging (MRSI) or both in grading primary cerebral gliomas.

Materials and methods

We performed conventional MR imaging (MR), DWI, and MRSI in 74 patients with newly diagnosed brain gliomas: 59 patients had histologically verified high-grade gliomas: 37 glioblastomas multiform (GBM) and 22 anaplastic astrocytomas (AA), and 15 patients had low-grade gliomas. Apparent diffusion coefficient (ADC) values of tumor and peritumoral edema, and ADC ratios (ADC in tumor or peritumoral edema to ADC of contralateral white matter, as well as ADC in tumor to ADC in peritumoral edema) were determined from three regions of interest. The average of the mean, maximum, and minimum for ADC variables was calculated for each patient. The metabolite ratios of Cho/Cr and Cho/NAA at intermediate TE were assessed from spectral maps in the solid portion of tumor, peritumoral edema and contralateral normal-appearing white matter. Tumor grade determined with the two methods was then compared with that from histopathologic grading. Logistic regression and receiver operating characteristic (ROC) curve analysis were performed to determine optimum thresholds for tumor grading. Measures of diagnostic examination performance, such as sensitivity, specificity, PPV, NPV, AUC, and accuracy for identifying high-grade gliomas were also calculated.

Results

Statistical analysis demonstrated a threshold minimum ADC tumor value of 1.07 to provide sensitivity, specificity, PPV, and NPV of 79.7%, 60.0%, 88.7%, and 42.9% respectively, in determining high-grade gliomas. Threshold values of 1.35 and 1.78 for peritumoral Cho/Cr and Cho/NAA metabolite ratios resulted in sensitivity, specificity, PPV, and NPV of 83.3%, 85.1%, 41.7%, 97.6%, and 100%, 57.4%, 23.1% and 100% respectively for determining high-grade gliomas. Significant differences were noted in the ADC tumor values and ratios, peritumoral Cho/Cr and Cho/NAA metabolite ratios, and tumoral Cho/NAA ratio between low- and high-grade gliomas. The combination of mean ADC tumor value, maximum ADC tumor ratio, peritumoral Cho/Cr and Cho/NAA metabolite ratios resulted in sensitivity, specificity, PPV, and NPV of 91.5%, 100%, 100% and 60% respectively.

Conclusion

Combining DWI and MRSI increases the accuracy of preoperative imaging in the determination of glioma grade. MRSI had superior diagnostic performance in predicting glioma grade compared with DWI alone. The predictive values are helpful in the clinical decision-making process to evaluate the histologic grade of tumors, and provide a means of guiding treatment.

Introduction

Gliomas are the most common primary neoplasms of the central nervous system [1]. Grading of gliomas is important for the determination of appropriate treatment strategies [1], [2] and in the assessment of prognosis [3], because high-grade gliomas (HGGs) are usually treated with tumor resection and additional radiation and chemotherapy, whereas in low-grade gliomas (LGGs), only surgical treatment for histologic confirmation or tumor resection is performed in most patients [4], [5], [6]. The current criterion standard for tumor grading is histopathologic assessment, but this has limitations, such as inherent sampling error associated with the limited number of biopsy samples [2]. Advanced MR imaging techniques, such as diffusion-weighted MR imaging (DWI) and proton MR spectroscopic imaging (MRSI), can provide information not available from conventional MR imaging that may complement the histopathologic grade.

There are few reports in the literature describing the false-positive and false-negative ratios for glioma grading using apparent diffusion coefficient (ADC) values or ratios, MRSI, and in particular using both methods in conjunction. The aims of this study were (1) to define the role of these advanced MR imaging techniques in clinical practice, in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC), and accuracy, and determining whether DWI, MRSI or the combination of the two techniques is more superior need further investigation, and (2) to determine whether DWI or MRSI and in combination can be used in the grading of gliomas on the basis of differences in cell density and metabolite levels in the tumor and in the peritumoral edema.