Gain In Days Research Articles

Days gained response discriminates treatment response in patients with recurrent glioblastoma receiving bevacizumab-based therapies.

BackgroundAccurate assessments of patient response to therapy are a critical component of personalized medicine. In glioblastoma (GBM), the most aggressive form of brain cancer, tumor growth dynamics are heterogenous across patients, complicating assessment of treatment response. This study aimed to analyze days gained (DG), a burgeoning model-based dynamic metric, for response assessment in patients with recurrent GBM who received bevacizumab-based therapies.MethodsDG response scores were calculated using volumetric tumor segmentations for patients receiving bevacizumab with and without concurrent cytotoxic therapy (N = 62). Kaplan–Meier and Cox proportional hazards analyses were implemented to examine DG prognostic relationship to overall (OS) and progression-free survival (PFS) from the onset of treatment for recurrent GBM.ResultsIn patients receiving concurrent bevacizumab and cytotoxic therapy, Kaplan–Meier analysis showed significant differences in OS and PFS at DG cutoffs consistent with previously identified values from newly diagnosed GBM using T1-weighted gadolinium-enhanced magnetic resonance imaging (T1Gd). DG scores for bevacizumab monotherapy patients only approached significance for PFS. Cox regression showed that increases of 25 DG on T1Gd imaging were significantly associated with a 12.5% reduction in OS hazard for concurrent therapy patients and a 4.4% reduction in PFS hazard for bevacizumab monotherapy patients.ConclusionDG has significant meaning in recurrent therapy as a metric of treatment response, even in the context of anti-angiogenic therapies. This provides further evidence supporting the use of DG as an adjunct response metric that quantitatively connects treatment response and clinical outcomes.

NIMG-26. EVALUATING THE DAYS GAINED RESPONSE METRIC IN CLINICAL TRIALS USING BEVACIZUMAB PLUS ADDITIONAL AGENTS FOR RECURRENT GLIOBLASTOMA

Abstract Determining effective therapies for patients with recurrent glioblastoma remains difficult especially in small early phase clinical trials. Currently utilized estimators of treatment response rely on two-dimensional measurements of tumor progression (e.g. RANO, RECIST, MacDonald) that have often fallen short in reliably connecting patients who respond with positive survival outcomes. This has prompted consideration of advanced imaging and volumetric measurements by the RANO working group and others. Our proposed approach, Days Gained (DG), uses volumetric measurements to calculate a personalized response metric based on change in tumor growth characteristics pre/post-treatment. DG has shown promise for connecting imageable treatment response with clinically meaningful (statistically significant) survival benefit when applied in upfront and recurrent therapy settings, even in the context of coincident bevacizumab treatment. In this study, we seek to consider a new cohort of patients receiving bevacizumab in combination with other novel therapies to distinguish any subpopulation of patients with meaningful response. In this work, we identified a subset of 16 patients from bevacizumab clinical trials with data available for DG analysis requiring serial imaging (T1Gd and FLAIR) prior to and after the first cycle of therapy. Each trial evaluated bevacizumab alone versus bevacizumab plus either dasatinib or TRC105 in recurrent disease. Discrimination of time to progression (TTP) was evaluated at DG thresholds identified from our prior analysis of patients treated with bevacizumab. Using previously identified DG thresholds for clinically significant treatment benefit, in our cohort, DG measured by serial FLAIR imaging was significant in predicting longer TTP (120 DG, p=0.014), but T1Gd DG scores did not attain significance (p=0.630). Given the limited insights in connecting response metrics to accurately predicted outcomes, these data further support the use of DG as a burgeoning marker of treatment response (or lack of response) that may be useful for more routine integration into clinical trials.

Quantifying Uncertainty and Robustness in a Biomathematical Model-Based Patient-Specific Response Metric for Glioblastoma.

PurposeGlioblastomas, lethal primary brain tumors, are known for their heterogeneity and invasiveness. A growing body of literature has been developed demonstrating the clinical relevance of a biomathematical model, the proliferation-invasion model, of glioblastoma growth. Of interest here is the development of a treatment response metric, days gained (DG). This metric is based on individual tumor kinetics estimated through segmented volumes of hyperintense regions on T1-weighted gadolinium-enhanced and T2-weighted magnetic resonance images. This metric was shown to be prognostic of time to progression. Furthermore, it was shown to be more prognostic of outcome than standard response metrics. Although promising, the original article did not account for uncertainty in the calculation of the DG metric, leaving the robustness of this cutoff in question.MethodsWe harnessed the Bayesian framework to consider the impact of two sources of uncertainty: (1) image acquisition and (2) interobserver error in image segmentation. We first used synthetic data to characterize what nonerror variants are influencing the final uncertainty in the DG metric. We then considered the original patient cohort to investigate clinical patterns of uncertainty and to determine how robust this metric is for predicting time to progression and overall survival.ResultsOur results indicate that the key clinical variants are the time between pretreatment images and the underlying tumor growth kinetics, matching our observations in the clinical cohort. Finally, we demonstrated that for this cohort, there was a continuous range of cutoffs between 94 and 105 for which the prediction of the time to progression was over 80% reliable.ConclusionAlthough additional validation must be performed, this work represents a key step in ascertaining the clinical utility of this metric.

NIMG-06. KINETICS-BASED RESPONSE METRIC DISCRIMINATE IMPROVED OUTCOMES FOR PATIENTS RECEIVING BEVACIZUMAB-BASED THERAPIES

INTRODUCTION Evaluating treatment response in glioblastoma is a difficult task for clinicians, particularly in recurrent disease. Current response metrics focus on changes in imageable tumor burden which can be ambiguously affected by anti-angiogenic therapy. In previous work, we reported the ability of the Days Gained (DG) response metric to discriminate patients receiving bevacizumab into long and short-term survival groups. In this work, we investigate how the DG metric performs for patients who received bevacizumab with a concurrent cytotoxic agent (e.g. CCNU) compared to patients who received bevacizumab alone.

NIMG-59PREDICTING THE RESPONSE TO TREATMENT IN GBM: MACHINE LEARNING ON CLINICAL IMAGES

NIMG-59. PREDICTING THE RESPONSE TO TREATMENT IN GBM: MACHINE LEARNING ON CLINICAL IMAGES Corbin Rayfield and Kristin Swanson; Mayo Clinic, Scottsdale, AZ, USA Patients suffering from glioblastoma exhibit a wide response to therapy. Studies relying on genetic/phenotypic markers to predict treatment response struggle to deliver clinically useful information in an actionable timeframe which underscores the need for a predictive measure produced by pretreatment clinical information. Our group has previously described the Days Gained (DG) metric of response which is capable of stratifying patients by overall and progression free survival (Neal 2013). We sought to explore whether a predictive algorithm could be used to predict patients’ response to therapy as measured by the DG metric. We studied 52 patients with newly diagnosed glioblastoma who had at least 2 pre-treatment and 1 post radiation therapy MRIs. The group was divided into a training (n 1⁄4 31) and a test set (n 1⁄4 21). A decision tree calculated through multiple iterations of linear regressions was used to generate variables that were correlated with the DG score and cluster the training patients according to clinically available variables. A random forest algorithm was used to search for the optimal model that would differentiate those patients who had a significant response to treatment (DG . 73), previously shown to be predictive of better overall survival (Neal 2013). The variables that clustered patients by treatment response were age, gender, Karnofsky Performance Score, tumors size on MRI (T1Gd and T2) at diagnosis, extent of resection, pre-treatment velocity, and the ratio of the net rates of diffusion to proliferation. A test performed on the test cohort showed 95% accuracy in predicting their response to treatment. This tool, utilizing nothing more than information available at the time of surgery, patients can be stratified according to expected response to adjuvant therapy. By targeting those patients that are predicted not to respond, clinicians can begin to consider more aggressive radiation schemes, faster implementation of salvage therapy or clinical trials. Neuro-Oncology 17:v153–v171, 2015. doi:10.1093/neuonc/nov225.59 Published by Oxford University Press on behalf of the Society for Neuro-Oncology 2015.

RT-07 * APPLICATION OF A GROWTH-RATE BASED RESPONSE METRIC TO RECURRENT MALIGNANT GLIOMAS TREATED WITH LARGE-VOLUME RE-IRRADIATION USING PROTON BEAM THERAPY

BACKGROUND: The Days Gained (DG) response metric, which incorporates knowledge of the tumor growth rate, has been shown to correlate with progression-free (PFS) and overall survival (OS) following concurrent chemoradiotherapy for newly diagnosed glioblastoma (Neal et al., 2013 PLOS One; Neal et al., 2013 Cancer Research). We describe the first use of this metric to analyze radiographic response of recurrent malignant gliomas to large-volume re-irradiation using proton beam therapy (PBT). METHODS: The equation described in Neal, et al., 2013 was utilized to derive the DG score for 8 patients with histologically confirmed, contrast-enhancing recurrent gliomas. For each patient, the DG response metric was calculated from the enhancing T1 + Gad sequences of 2 MRIs immediately prior to PBT and the first MRI following completion of PBT. OS time from PT start was estimated with Kaplan-Meier analysis; comparisons used log-rank statistic. RESULTS: Median age was 43, median KPS score was 90, and median number of salvage treatments was 2. The mean DG score was 78.8 (range: -32.3-276.8). Concomitant use of temozolomide and bevacizumab during PBT was associated with a higher mean DG score (191.2 vs. 11.4; p = 0.03). In addition, association between higher DG score and superior OS trended to statistical significance (HR = 0.986, p = 0.08). CONCLUSIONS: First analysis of radiographic response of recurrent malignant gliomas to re-irradiation using PBT supports a possible association between DG and OS and the prolongation of DG with the concomitant use of temozolomide and bevacizumab during re-irradiation. Further analysis with larger sample size remains ongoing.

Patient-specific mathematical modeling as a precision-medicine approach to evaluating therapeutic gains of a novel chemoprotection treatment in newly diagnosed glioblastoma.

e13041 Background: Glioblastoma (GBM) is the most common and lethal of all brain tumors (median overall survival (OS)14.6 months), yet the incidence is low posing difficulty in creating statistically powered clinical trials, delaying advances in treatment. A patient-specific mathematical model for disease progression coupled with clinical imaging data may provide a tool for quantifying each individual tumor’s response relative to its inherent growth kinetics rather than a universal approach, and, potentially, predicting treatment efficacy. Methods: An exploratory analysis with a patient-specific mathematical model of GBM was used to evaluate therapeutic effect of a novel treatment for newly diagnosed GBM compared to standard care treatment. A cohort of 3 patients from a novel-chemoprotective stem cell gene therapy treatment study (CSCGT) received RT only, followed by dose-intense BCNU and infusion of autologous gene-modified hematopoietic stem cells. Patients then received TMZ combined with O6-benzylguanine (O6BG). Tumor volumes were calculated using T1 weighted gadolinium -enhanced and T2-weighted pretreatment MRI images to estimate patient-specific net rates of proliferation and invasion for predicting untreated tumor growth. Control patients were selected based on matching model-based rates of net proliferation and invasion and standard prognostic variables (age, performance, extent of resection). Deflection of tumor growth, days gained (DG), was determined by finding the time point on the untreated growth curve that corresponded to the post-treatment tumor size and calculating the amount of time treatment gained for the patient. DG per mg of TMZ dosed is used to compare treatment effects of TMZ with differing dose regimens. Results: CSCGT patients experienced higher DG per mg of TMZ dosed compared to their matched controls treated with standard care regimens. Conclusions: Patient-specific mathematical modeling can be used to evaluate treatment gains in small clinical trial design. The addition of O6BG to TMZ in the post RT milieu can provide greater treatment gains per mg of TMZ dose than TMZ alone. Clinical trial information: NCT 00669669.