Magnetic resonance imaging (MRI) is the cornerstone for evaluating patients with brain masses such as primary and metastatic tumors. Important challenges in effectively detecting and diagnosing brain metastases and in accurately characterizing their subsequent response to treatment remain. These difficulties include discriminating metastases from potential mimics such as primary brain tumors and infection, detecting small metastases, and differentiating treatment response from tumor recurrence and progression. Optimal patient management could be benefited by improved and well-validated prognostic and predictive imaging markers, as well as early response markers to identify successful treatment prior to changes in tumor size. To address these fundamental needs, newer MRI techniques including diffusion and perfusion imaging, MR spectroscopy, and positron emission tomography (PET) tracers beyond traditionally used 18-fluorodeoxyglucose are the subject of extensive ongoing investigations, with several promising avenues of added value already identified. These newer techniques provide a wealth of physiologic and metabolic information that may supplement standard MR evaluation, by providing the ability to monitor and characterize cellularity, angiogenesis, perfusion, pH, hypoxia, metabolite concentrations, and other critical features of malignancy. This chapter reviews standard and advanced imaging of brain metastases provided by computed tomography, MRI, and amino acid PET, focusing on potential biomarkers that can serve as problem-solving tools in the clinical management of patients with brain metastases.

Magnetic resonance imaging is integral to the care of patients with high-grade gliomas. Anatomic detail can be acquired with conventional structural imaging, but newer approaches also add capabilities to interrogate image-derived physiologic and molecular characteristics of central nervous system neoplasms. These advanced imaging techniques are increasingly employed to generate biomarkers that better reflect tumor burden and therapy response. The following is an overview of current strategies based on advanced magnetic resonance imaging that are used in the assessment of high-grade glioma patients with an emphasis on how novel imaging biomarkers can potentially advance patient care.

The most common type of primary brain tumor is malignant glioma. Despite intensive therapeutic efforts, the majority of these neoplasms remain incurable. Imaging techniques are important for initial tumor detection and comprise indispensable tools for monitoring treatment. Structural imaging using contrast-enhanced MRI is the method of choice for brain tumor surveillance, but its capacity to differentiate tumor from nonspecific tissue changes can be limited, particularly with posttreatment gliomas. Metabolic imaging using positron-emission-tomography (PET) can provide relevant additional information, which may allow for better assessment of tumor burden in ambiguous cases. Specific PET tracers have addressed numerous molecular targets in the last decades, but only a few have achieved relevance in routine clinical practice. At present, PET studies using radiolabeled amino acids appear to improve clinical decision-making as these tracers can offer better delineation of tumor extent as well as improved targeting of biopsies, surgical interventions, and radiation therapy. Amino acid PET imaging also appears useful for distinguishing glioma recurrence or progression from postradiation treatment effects, particularly radiation necrosis and pseudoprogression, and provides information on histological grading and patient prognosis. In the last decade, the tracers O-(2-[(18)F]fluoroethyl)-L-tyrosine (FET) and 3,4-dihydroxy-6-[(18)F]-fluoro-L-phenylalanine (FDOPA) have been increasingly used for these indications. This review article focuses on these tracers and summarizes their recent applications for patients with brain tumors. Current uses of tracers other than FET and FDOPA are also discussed, and the most frequent practical questions regarding PET brain tumor imaging are reviewed.

Bevacizumab is thought to normalize tumor vasculature and restore the blood-brain barrier, decreasing enhancement and peritumoral edema. Conventional measurements of tumor response rely upon dimensions of enhancing tumor. After bevacizumab treatment, glioblastomas are more prone to progress as nonenhancing tumor. The RANO (Response Assessment in Neuro-Oncology) criteria for glioma response use fluid-attenuated inversion recovery (FLAIR)/T2 hyperintensity as a surrogate for nonenhancing tumor; however, nonenhancing tumor can be difficult to differentiate from other causes of FLAIR/T2 hyperintensity (e.g., radiation-induced gliosis). Due to these difficulties, recent efforts have been directed toward identifying new biomarkers that either predict treatment response or accurately measure response of both enhancing and nonenhancing tumor shortly after treatment initiation. This will allow for earlier treatment decisions, saving patients from the adverse effects of ineffective therapies while allowing them to try alternative therapies sooner. An active area of research is the use of physiologic imaging, which can potentially detect treatment effects before changes in tumor size are evident.

The purpose of this report is to describe the state of imaging techniques and technologies for detecting response of brain tumors to treatment in the setting of multicenter clinical trials. Within currently used technologies, implementation of standardized image acquisition and the use of volumetric estimates and subtraction maps are likely to help to improve tumor visualization, delineation, and quantification. Upon further development, refinement, and standardization, imaging technologies such as diffusion and perfusion MRI and amino acid PET may contribute to the detection of tumor response to treatment, particularly in specific treatment settings. Over the next few years, new technologies such as 2(3)Na MRI and CEST imaging technologies will be explored for their use in expanding the ability to quantitatively image tumor response to therapies in a clinical trial setting.

Aside from bidimensional measurements from conventional contrast-enhanced MRI, there are no validated or FDA-qualified imaging biomarkers for high-grade gliomas. However, advanced functional MRI techniques, including perfusion- and diffusion-weighted MRI, have demonstrated much potential for determining prognosis, predicting therapeutic response, and assessing early treatment response. They may also prove useful for differentiating pseudoprogression from true progression after temozolomide chemoradiation and pseudoresponse from true response after anti-angiogenic therapy. This review will highlight recent developments using these techniques and emphasize the need for technical standardization and validation in prospective studies in order for these methods to become incorporated into standard-of-care imaging for brain tumor patients.

Glioblastoma multiforme (GBM) is the most common primary CNS neoplasm, and continues to have a dismal prognosis. A widely-used approach to the mathematical modeling of GBM involves utilizing a reaction-diffusion model of cell density as a function of space and time, which accounts for both the infiltrative nature of the tumor using a diffusion term, and the proliferation of tumor cells using a proliferation term. The current paper extends the standard models by incorporating an advection term to account for the so-called 'cell streaming' which is often seen with GBM, where some of the tumor cells seem to stream widely along the white matter pathways. The current paper introduces a bicompartmental GBM model in the form of coupled partial differential equations with a component of dispersive cells. The parameters needed for this model are explored. It is shown that this model can account for the rapid distant dispersal of GBM cells in the CNS, as well as such phenomena as multifocal gliomas with tumor foci distant from the core tumor site. The model suggests a higher percentage of tumor cells below the threshold of MRI images in comparison to the standard model. By incorporating an advection component, the proposed model is able to account for phenomena such as multicentric gliomas and rapid distant dispersion of a small fraction of tumor cells throughout the CNS, features important to the prognosis of GBM, but not easily accounted for by current models.

Low-grade gliomas (LGGs) represent a significant proportion of hemispheric gliomas in adults. Although less aggressive than glioblastomas (GBMs), they have a broad range of biologic behavior, and often a limited prognosis. The aim of the present study was to explore LGG growth kinetics through a combination of routine MRI imaging and a novel adaptation of a mathematical tumor model. MRI imaging in 14 retrospectively identified grade II LGGs that showed some tumor enhancement was used to assess tumor radii at two separate time-points. This information was combined with a reaction-diffusion partial-differential equation model of tumor growth to calculate diffusion (D) and proliferation (ρ) coefficients for each tumor, representing measures of tumor invasiveness and cellular multiplication, respectively. The results were compared to previously published data on GBMs. The average value of D was 0.034 mm(2)/day and ρ was 0.0056/day. Grade II LGGs had a broad range of D and ρ. On average, the proliferation coefficient ρ was significantly lower than previously published values for GBM, by about an order of magnitude. The diffusion coefficient, modeling invasiveness, however, was only slightly lower but without statistical significance. It was possible to calculate detailed growth kinetic parameters for some LGGs, potentially providing a new way to assess tumor aggressiveness and possibly gauge prognosis. Even within a single-grade (WHO II), LGGs were found to have broad range of D and ρ, possibly correlating to their variable biologic behavior. Overall, the model parameters suggest that LGG is less aggressive than GBM based primarily on a lower index of tumor proliferation rather than on lesser invasiveness.

The wide variety of treatment options that exist for glioblastoma, including surgery, ionizing radiation, anti-neoplastic chemotherapies, anti-angiogenic therapies, and active or passive immunotherapies, all may alter aspects of vascular permeability within the tumor and/or normal parenchyma. These alterations manifest as changes in the degree of contrast enhancement or T2-weighted signal hyperintensity on standard anatomic MRI scans, posing a potential challenge for accurate radiographic response assessment for identifying anti-tumor effects. The current review highlights the challenges that remain in differentiating true disease progression from changes due to radiation therapy, including pseudoprogression and radionecrosis, as well as immune or inflammatory changes that may occur as either an undesired result of cytotoxic therapy or as a desired consequence of immunotherapies.