Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival

As drug discovery programs search for new treatment strategies to improve the survival of patients with glioblastoma multiforme (GBM), the need for a timely and accurate end point to judge treatment efficacy has never been greater.1 Overall survival (OS) represents the benchmark measure of outcome but has clear disadvantages for clinical trial assessment. Clinical trials that use OS are lengthened because of the time needed to observe mortality,1,2 and correlations between OS and initial treatment are modified by subsequent salvage therapies.2,3 These limitations have led to the use of progression-free survival (PFS) as a surrogate marker for OS. However, PFS requires accurate estimates of tumor growth based on MRI, and such estimates are not always readily made, nor does PFS correlate well with OS.2,3 Contrast-enhanced MRI (CE-MRI) represents the best available method for measuring treatment response and predicting survival after standard first-line therapy and is used to define PFS.2–4 Currently, decisions about treatment are guided by criteria (Macdonald, RANO) that equate increasing size of CE-MRI enhancement with progressive tumor burden, treatment failure, and poor prognosis.5,6 Despite its widespread use, this approach has distinct limitations. First, CE-MRI cannot distinguish tumor growth from treatment-induced parenchymal injury, so called post-treatment radiation effect (PTRE), which exactly mimics tumor on CE-MRI. Two well documented forms of PTRE are pseudoprogression (pP) and radiation necrosis (RN). Unlike tumor, PTRE represents a positive response to treatment and, therefore, a good prognosis; however, PTRE-related enhancement underlies erroneous declaration of treatment failure in up to half of cases.7–10 In addition, tumor often coexists and variably admixes with PTRE in most patients.11 The histologic tumor fraction (i.e., tumor burden) therefore comprises a subcomponent of the total CE-MRI enhancement and represents a potentially useful predictor of survival in patients with recurrent brain tumor.12–14 In fact, studies suggest that histologically quantifying tumor burden provides more meaningful prognostic information than simply reporting the presence of tumor.12–15 Because CE-MRI cannot reliably distinguish the coexistence of PTRE and tumor burden, surgical biopsy and histologic evaluation remain the current benchmark.13 Unfortunately, surgery is not without medical risk, morbidity, and cost. These issues underscore a clear need to develop a noninvasive method to accurately estimate tumor burden as a potential alternative or adjunct to surgical biopsy. Perfusion MRI (pMRI) noninvasively detects GBM and PTRE microvascular characteristics, most commonly with the dynamic susceptibility-weighted contrast-enhanced (DSC) method. With use of pMRI, relative cerebral blood volume (rCBV) is measured on a voxel-wise basis across CE-MRI lesions, providing regionally specific estimates of tissue microvasculature and histologic identity.16–24 To date, several pMRI-based analytic methods have been proposed to estimate histologic tumor fraction, each with potential advantages and limitations. First, calculating mean rCBV across all CE-MRI lesion voxels is the easiest method but does not assess intervoxel variations that may reflect histologic heterogeneity.16,17 Second, histogram analysis contributes additional metrics, including mode, maximum, and histogram width, although these may be biased by spatial statistics.18,19 Finally, the voxel-based thresholding method applies predetermined rCBV criteria to classify individual voxels based on histologic identity, but requires that the accuracy of the threshold be prospectively validated.20,21 To date, no published studies support consensus regarding which pMRI-based analytic method best estimates histologic tumor fraction as a predictor of survival in recurrent GBM. Our hypothesis is that tumor burden and OS will correlate more strongly with those pMRI-based methods that use a voxel-based approach to assess histologic heterogeneity. We here report a study in a cohort of patients with recurrent GBM with CE-MRI evidence of tumor progression. Our goals were to (1) determine the strengths of correlation between histologic tumor fraction and previously published pMRI-based metrics (rCBV mean, mode, maximum, and histogram width) and a new voxel-based thresholding metric, called pMRI fractional tumor burden (pMRI-FTB), and (2) identify which pMRI-based metric best correlates with OS.