There is an urgent need for drug development in brain tumors. While current radiographic response assessment provides instructions for identifying large treatment effects in simple high- and low-grade gliomas, there remains a void of strategies to evaluate complex or difficult to measure tumors or tumors of mixed grade with enhancing and non-enhancing components. Furthermore, most patients exhibit some period of alteration in tumor growth after starting a new therapy, but simple response categorization (e.g., stable disease, progressive disease) fails to provide any meaningful insight into the depth or degree of potential "subclinical" therapeutic response. We propose a creative solution to these issues based on a tiered strategy meant to increase confidence in identifying therapeutic effects even in the most challenging tumor types, while also providing a framework for complex evaluation of combination and sequential treatment schemes. Specifically, we demonstrate the utility of digital "flipbooks" to quickly identify subtle changes in complex tumors. We show how a modified Levin criteria can be used to quantify the degree of visual changes, while establishing estimates of the association between tumor volume and visual inspection. Lastly, we introduce the concept of quantifying therapeutic response using control systems theory. We propose measuring changes in volume (proportional), the area under the volume vs. time curve (integral) and changes in growth rates (derivative) to utilize a "PID" controller model of single or combination therapeutic activity.

The use of contrast enhancement within the brain on CT or MRI has been the gold standard for diagnosis and therapeutic response assessment in malignant gliomas for decades. The use of contrast enhancing tumor size, however, remains controversial as a tool for accurately diagnosing and assessing treatment efficacy in malignant gliomas, particularly in the current, quickly evolving therapeutic landscape. The current article consolidates overwhelming evidence from hundreds of studies in the field of neuro-oncology, providing the necessary evidence base and specific contexts of use for consideration of contrast enhancing tumor size as an appropriate surrogate biomarker for disease burden and as a tool for measuring treatment response in malignant glioma, including glioblastoma.

Radiographic endpoints including response and progression are important for the evaluation of new glioblastoma therapies. The current RANO criteria was developed to overcome many of the challenges identified with previous guidelines for response assessment, however, significant challenges and limitations remain. The current recommendations build on the strengths of the current RANO criteria, while addressing many of these limitations. Modifications to the current RANO criteria include suggestions for volumetric response evaluation, use contrast enhanced T1 subtraction maps to increase lesion conspicuity, removal of qualitative non-enhancing tumor assessment requirements, use of the post-radiation time point as the baseline for newly diagnosed glioblastoma response assessment, and "treatment-agnostic" response assessment rubrics for identifying pseudoprogression, pseudoresponse, and a confirmed durable response in newly diagnosed and recurrent glioblastoma trials.

Abstract

Several new therapeutic strategies have emerged over the past decades to address unmet clinical needs in high-grade gliomas, including targeted molecular agents and various forms of immunotherapy. Each of these strategies requires addressing fundamental questions, depending on the stage of drug development, including ensuring drug penetration into the brain, engagement of the drug with the desired target, biologic effects downstream from the target including metabolic and/or physiologic changes, and identifying evidence of clinical activity that could be expanded upon to increase the likelihood of a meaningful survival benefit. The current review article highlights these strategies and outlines how imaging technology can be used for therapeutic response evaluation in both targeted and immunotherapies in early phases of drug development in high-grade gliomas.

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.

The growing amount of longitudinal data for a large population of patients has necessitated the application of algorithms that can discover patterns to inform patient management. This study demonstrates how temporal patterns generated from a combination of clinical and imaging measurements improve residual survival prediction in glioblastoma patients. Temporal patterns were identified with sequential pattern mining using data from 304 patients. Along with patient covariates, the patterns were incorporated as features in logistic regression models to predict 2-, 6-, or 9-month residual survival at each visit. The modeling approach that included temporal patterns achieved test performances of 0.820, 0.785, and 0.783 area under the receiver operating characteristic curve for predicting 2-, 6-, and 9-month residual survival, respectively. This approach significantly outperformed models that used tumor volume alone (p < 0.001) or tumor volume combined with patient covariates (p < 0.001) in training. Temporal patterns involving an increase in tumor volume above 122 mm³/day, a decrease in KPS across multiple visits, moderate neurologic symptoms, and worsening overall neurologic function suggested lower residual survival. These patterns are readily interpretable and found to be consistent with known prognostic indicators, suggesting they can provide early indicators to clinicians of changes in patient state and inform management decisions.

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.

Cell invasion, motility, and proliferation level estimate (CIMPLE) mapping is a new imaging technique that provides parametric maps of microscopic invasion and proliferation rate estimates using serial diffusion MRI data. However, a few practical constraints have limited the use of CIMPLE maps as a tool for estimating these dynamic parameters, particularly during short-interval follow-up times. The purpose of the current study was to develop an approximation for the CIMPLE map solution for short-interval scanning involving the assumption that net intervoxel tumor invasion does not occur within sufficiently short time frames. Proliferation rate maps created using the no invasion approximation were found to be increasingly similar to maps created from full solution during increasingly longer follow-up intervals (3D cross correlation, R (2) = 0.5298, P = 0.0001). Results also indicate proliferation rate maps from the no invasion approximation had significantly higher sensitivity (82 vs. 64%) and specificity (90 vs. 80%) for predicting 6 month progression free survival and was a better predictor of time to progression during standard radiochemotherapy compared to the full CIMPLE solution (log-rank; no invasion estimation, P = 0.0134; full solution, P = 0.0555). Together, results suggest the no invasion approximation allows for quick estimation of proliferation rate using diffusion MRI data obtained from multiple scans obtained daily or biweekly for use in quantifying early treatment response.

Diffusion-weighted imaging estimates of apparent diffusion coefficient (ADC) have shown sensitivity to brain tumor cellularity as well as response to therapy. Functional diffusion maps (fDMs) exploit these principles by examining voxelwise changes in ADC within the same patient over time. Currently, the fDM technique involves linear image registration of ADC maps from subsequent follow-up times to pretreatment ADC maps; however, misregistration of ADC maps due to geometric distortions as well as mass effect from growing tumor can confound fDM measurements. In this study, we compare the use of a nonlinear registration scheme to the current linear fDM technique in 70 patients with recurrent glioblastoma multiforme treated with bevacizumab. Results suggest that nonlinear registration of pretreatment ADC maps to post-treatment ADC maps improves the clinical predictability, sensitivity, and specificity of fDMs for both progression-free and overall survival.