Radiotherapy planning for glioblastoma based on a tumor growth model: Improving target volume delineation

Glioblastoma are known to infiltrate the brain parenchyma instead of forming a solid tumor mass with a defined boundary. Only the part of the tumor with high tumor cell density can be localized through imaging directly. In contrast, brain tissue infiltrated by tumor cells at low density appears normal on current imaging modalities. In clinical practice, a uniform margin is applied to account for microscopic spread of disease. The current treatment planning procedure can potentially be improved by accounting for the anisotropy of tumor growth: Anatomical barriers such as the falx cerebri represent boundaries for migrating tumor cells. In addition, tumor cells primarily spread in white matter and infiltrate gray matter at lower rate. We investigate the use of a phenomenological tumor growth model for treatment planning. The model is based on the Fisher-Kolmogorov equation, which formalizes these growth characteristics and estimates the spatial distribution of tumor cells in normal appearing regions of the brain. The target volume for radiotherapy planning can be defined as an isoline of the simulated tumor cell density. A retrospective study involving 10 glioblastoma patients has been performed. To illustrate the main findings of the study, a detailed case study is presented for a glioblastoma located close to the falx. In this situation, the falx represents a boundary for migrating tumor cells, whereas the corpus callosum provides a route for the tumor to spread to the contralateral hemisphere. We further discuss the sensitivity of the model with respect to the input parameters. Correct segmentation of the brain appears to be the most crucial model input. We conclude that the tumor growth model provides a method to account for anisotropic growth patterns of glioblastoma, and may therefore provide a tool to make target delineation more objective and automated

Prognostic Significance of Growth Kinetics in Newly Diagnosed Glioblastomas Revealed by Combining Serial Imaging with a Novel Biomathematical Model

Glioblastomas (GBMs) are the most aggressive primary brain tumors characterized by their rapid proliferation and diffuse infiltration of the brain tissue. Survival patterns in patients with GBM have been associated with a number of clinico-pathologic factors, including age and neurological status, yet a significant quantitative link to in vivo growth kinetics of each glioma has remained elusive. Exploiting a recently developed tool for quantifying glioma net proliferation and invasion rates in individual patients using routinely available magnetic resonance images (MRIs), we propose to link these patient-specific kinetic rates of biological aggressiveness to prognostic significance. Using our biologically-based mathematical model for glioma growth and invasion, examination of serial pre-treatment MRIs of 32 GBM patients allowed quantification of these rates for each patient’s tumor. Survival analyses revealed that even when controlling for standard clinical parameters (e.g., age, KPS) these model-defined parameters quantifying biologically aggressiveness (net proliferation and invasion rates) were significantly associated with prognosis. One hypothesis generated was that the ratio of the actual survival time after whatever therapies were employed to the duration of survival predicted (by the model) without any therapy would provide a “Therapeutic Response Index” (TRI) of the overall effectiveness of the therapies. The TRI may provided important information, not otherwise available, as to the effectiveness of the treatments in individual patients. To our knowledge, this is the first report indicating that dynamic insight from routinely obtained pre-treatment imaging may be quantitatively useful in characterizing survival of individual patients with GBM. Such a hybrid tool bridging mathematical modeling and clinical imaging may allow for statifying patients for clinical studies relative to their pretreatment biological aggressiveness

MRI in Cancer: Improving Methodology for Measuring Vascular Properties and Assessing Radiation Treatment Effects in Brain

Tumors cannot survive, progress and metastasize without recruiting new blood vessels. Vascular properties, including perfusion and permeability, provide valuable information for characterizing cancers and assessing therapeutic outcomes. Dynamic contrast-enhanced (DCE) MRI is a non-invasive imaging technique that affords quantitative parameters describing the underlying vascular structure of tissue. To date, the clinical application of DCE-MRI has been hampered by the lack of standardized and validated quantitative modeling approaches for data analysis. From a therapeutic perspective, radiation therapy is a central component of the standard treatment for patients with cancer. Besides killing cancer cells, radiation also induces parenchymal and stromal changes in normal tissue, limiting radiation dose and complicating treatment response evaluation. Further, emerging evidence suggest that the radiation-modulated tumor microenvironment may also contribute to the enhanced tumor regrowth and resistance to therapy. Given these clinical problems, the objectives of this dissertation were to: i) improve the DCE MRI-based measurements of vascular properties; and ii) assess the radiation treatment effects on normal tissue (parenchyma) and the interaction between radiation-modulated parenchyma and tumor growth. For the first goal, Bayesian probability theory-based model selection was employed to evaluate four commonly employed DCE-MRI tracer kinetic models against both in silico DCE-MRI data and high-quality clinical data collected from patients with advanced-staged cervical cancer. Further, a constrained local arterial input function (cL-AIF) modeling approach was developed to improve the pharmacokinetic analysis of DCE-MRI data. For the second goal, a novel mouse model of radiation-mediated effects on normal brain was developed. The efficacy of anti-vascular endothelial growth factor (VEGF) antibody treatment of delayed, radiation-induced necrosis (RN) was evaluated. Also, the effects of radiation-modulated brain parenchyma on glioblastoma cell growth were studied. It was found that 1) complex DCE-MRI signal models are more sensitive to noise than simpler models with respect to parameter estimation accuracy and precision. Caution is thus advised when considering application of complex DCE-MRI kinetic models. It follows that data-driven model selection is an important prerequisite to DCE-MRI data analysis; 2) the proposed cL-AIF method, which estimates an unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better measurements vascular properties than the conventional approach employing a single, remotely measured AIF; 3) anti-VEGF antibody decreased MR-derived RN lesion volumes, while large areas of focal calcification formed and the expression of VEGF remained high post-treatment. More effective therapeutic strategies for RN are still needed; 4) the radiation-modulated brain parenchyma promotes aggressive, infiltrative glioma growth. The histologic features of such tumors are consistent with those commonly observed in recurrent high-grade tumors in patients. These findings afford new insights into the highly aggressive tumor regrowth patterns observed following radiotherapy

Treatment Response Assessment in IDH-Mutant Glioma Patients by Noninvasive 3D Functional Spectroscopic Mapping of 2-Hydroxyglutarate

Purpose: Measurements of objective response rates are critical to evaluate new glioma therapies. The hallmark metabolic alteration in gliomas with mutant isocitrate dehydrogenase (IDH) is the overproduction of oncometabolite 2-hydroxyglutarate (2HG), which plays a key role in malignant transformation. 2HG represents an ideal biomarker to probe treatment response in IDH-mutant glioma patients, and we hypothesized a decrease in 2HG levels would be measureable by in vivo magnetic resonance spectroscopy (MRS) as a result of antitumor therapy. Experimental Design: We report a prospective longitudinal imaging study performed in 25 IDH-mutant glioma patients receiving adjuvant radiation and chemotherapy. A newly developed 3D MRS imaging was used to noninvasively image 2HG. Paired Student t test was used to compare pre- and posttreatment tumor 2HG values. Test-retest measurements were performed to determine the threshold for 2HG functional spectroscopic maps (fSM). Univariate and multivariate regression were performed to correlate 2HG changes with Karnofsky performance score (KPS). Results: We found that mean 2HG (2HG/Cre) levels decreased significantly (median=48.1%; 95% confidence interval=27.3%-56.5%; P=0.007) in the posttreatment scan. The volume of decreased 2HG correlates (R2=0.88, P=0.002) with clinical status evaluated by KPS. Conclusions: We demonstrate that dynamic measurements of 2HG are feasible by 3D fSM, and the decrease of 2HG levels can monitor treatment response in patients with IDH-mutant gliomas. Our results indicate that quantitative in vivo 2HG imaging maybe used for precision medicine and early response assessment in clinical trials of therapies targeting IDH-mutant gliomas

Volumetric Assessment Of Imaging Response In The Pnoc Pediatric Glioma Clinical Trials

Response assessment in neuro-oncology relies on radiographic assessment of tumor burden on magnetic resonance (MR) imaging. The most widely used criteria were developed by the Response Assessment in Neuro-Oncology (RANO) group. The RANO criteria rely on bidimensional (2D) measurements of tumor on MR images. The RANO criteria were originally developed to assess response in adult high-grade glioma. However, the heterogeneous appearance of pediatric low-grade gliomas make application of RANO criteria challenging. Volumetric assessment of pediatric gliomas may offer a more comprehensive method for characterizing response. The goal of this thesis was to compare 2D and volumetric assessment methods in two pediatric glioma clinical trials from the Pacific Pediatric Neuro-Oncology Consortium (PNOC). The primary purpose of the thesis was to compare 2D and volumetric response to a clinical reference standard – neuroradiologist visual response assessment via the Brain Tumor Reporting and Data System (BT-RADS). A secondary aim was to determine optimal thresholds for categorizing volumetric response using BT-RADS as a reference standard. A third aim was to compare 2D and volumetric posttreatment trajectories in trial participants. Retrospective analyses of two pediatric glioma clinical trials (PNOC-001 and PNOC-002) were conducted. Changes in tumor 2D area, whole tumor volume, and solid tumor volume were compared to assess response. Follow-up images were assigned a response score on BT-RADS by two neuroradiologists. Empirical receiver operating characteristic (ROC) curves of changes in 2D area, whole, and solid tumor volume were constructed to classify partial response (PR) and progressive disease (PD) based on BT-RADS. In the PNOC-002 trial, a mathematical model was used to construct posttreatment trajectories of changes in 2D area and whole tumor volume in a subset of participants. Empirical ROC curves to classify BT-RADS PD among the 65 follow-up images assessed in the PNOC-001 trial yielded an AUC of 0.78 (95% CI: 0.66-0.90) for 2D area percent change, 0.84 (95% CI: 0.74-0.94) for whole volume percent change, and 0.96 (95% CI: 0.92-1.00) for solid volume percent change. DeLong tests revealed that there was a significant increase in AUC of the solid volume ROC curve compared to both 2D area (p = 0.005) and whole volume (p = 0.006). The empirical ROC curves to classify BT-RADS PR yielded an AUC of 0.87 (95% CI: 0.77-0.96) for 2D area percent change, 0.84 (95% CI: 0.70-0.99) for whole volume percent change, and 0.97 (95% CI: 0.94-1.00) for solid volume percent change. DeLong tests revealed that there was a significant increase in AUC of the solid volume ROC curve compared to 2D area (p = 0.02) but not whole volume (p = 0.08). The thresholds for solid volume percent change that included an 80% sensitivity in their 95% confidence intervals for classifying BT-RADS PD ranged from 15-25% and 15-20% for classifying BT-RADS PR. The empirical ROC curves for classification of BT-RADS PR in the 31 participants at the end of treatment or last available follow-up produced the following AUC values: 0.92 (95% CI: 0.80-1.00) for 2D area percent change, 0.99 (95% CI: 0.97-1.00) for whole volume percent change, and 0.99 (95% CI: 0.97-1.00) for solid volume percent change. DeLong test revealed no statistically significant difference in AUC between 2D area and either solid (p = 0.17) or whole volume (p = 0.17) ROC curves. The empirical ROC curves for classification of BT-RADS PR at the first time of BT-RADS PR detection produced the following AUC values: 0.84 (95% CI: 0.69-0.99) for 2D area percent change, 0.91 (95% CI: 0.80-1.00) for whole volume percent change, and 0.92 (95% CI: 0.82-1.00) for solid volume percent change. There was no statistically significant difference in AUC between the 2D area ROC curve and either solid (p = .34) or whole volume (p = .39) ROC curves based on DeLong tests. Based on mathematically modeled trajectories, there was no significant correlation in time to best response obtained from 2D area vs. whole volume posttreatment changes (? = 0.39, p = 0.054). Eight out of 25 participants (32%) had a difference of 90 days or more in transition time from partial response to stable disease between 2D area and whole volume trajectories. Moreover, of the 16 participants with tumor regrowth following stable disease, 50% had a difference of 90 days or more in transition time from stable disease to progressive disease between 2D area and whole volume trajectories. Solid tumor volume better predicted neuroradiologist assessment of partial response and progressive disease according to BT-RADS criteria in the PNOC-001 trial but performed as well as 2D measurements in classifying partial response in the PNOC-002 trial. Although volumetrics was not consistently superior to 2D measurements in detecting response in our study, there were differences in individual participant 2D and volumetric posttreatment trajectories. Future research comparing volumetric to 2D assessment in prospective trials is required to understand the significance of these differences to clinical management

Functional Imaging of Malignant Gliomas with CT Perfusion

The overall survival of patients with malignant gliomas remains dismal despite multimodality treatments. Computed tomography (CT) perfusion is a functional imaging tool for assessing tumour hemodynamics. The goals of this thesis are to 1) improve measurements of various CT perfusion parameters and 2) assess treatment outcomes in a rat glioma model and in patients with malignant gliomas. Chapter 2 addressed the effect of scan duration on the measurements of blood flow (BF), blood volume (BV), and permeability-surface area product (PS). Measurement errors of these parameters increased with shorter scan duration. A minimum scan duration of 90 s is recommended. Chapter 3 evaluated the improvement in the measurements of these parameters by filtering the CT perfusion images with principal component analysis (PCA). From computer simulation, measurement errors of BF, BV, and PS were found to be reduced. Experiments showed that CT perfusion image contrast-to-noise ratio was improved. Chapter 4 investigated the efficacy of CT perfusion as an early imaging biomarker of response to stereotactic radiosurgery (SRS). Using the C6 glioma model, we showed that responders to SRS (surviving \u3e 15 days) had lower relative BV and PS on day 7 post-SRS when compared to controls and non-responders (P \u3c 0.05). Relative BV and PS on day 7 post-SRS were predictive of survival with 92% accuracy. Chapter 5 examined the use of multiparametric imaging with CT perfusion and 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET) to identify tumour sites that are likely to correlate with the eventual location of tumour progression. We developed a method to generate probability maps of tumour progression based on these imaging data. Chapter 6 investigated serial changes in tumour volumetric and CT perfusion parameters and their predictive ability in stratifying patients by overall survival. Pre-surgery BF in the non-enhancing lesion and BV in the contrast-enhancing lesion three months after radiotherapy had the highest combination of sensitivities and specificities of ≥ 80% in predicting 24 months overall survival. iv Optimization and standardization of CT perfusion scans were proposed. This thesis also provided corroborating evidence to support the use of CT perfusion as a biomarker of outcomes in patients with malignant gliomas