H.K.G. Shu

TH‐CD‐204‐03: A Glioblastoma Tumor Growth Prediction Model Using Volumetric MR Spectroscopic Imaging for Radiation Therapy Response

Purpose: We implemented high resolution, 3D whole brain MR spectroscopic imaging (MRSI) technology to provide additional information on glioblastoma (GBM) brain tumors not readily apparent on standard-of-care MR images. Here, we report integrating these images into a prediction model of tumor growth with radiation therapy. Materials and Methods: To predict therapy response, a mathematical algorithm of tumor infiltration based on the classical reaction-diffusion equation was customized to create a map of tumoral density for every location in the brain from the presence of choline (Cho), a proliferation-derived metabolite and N-acetylaspartate (NAA), a neuronal metabolite. Input images are pre and mid-treatment (2 weeks) MRSI acquisitions. The model is initialized using the gradient of the Cho/NAA ratio in the vicinity of tumor (periphery) as a measure of tumor infiltration, and the absolute Cho level as a measure of actively proliferating tumor cells. Customized, per-patient model parameters are tested until simulated images of tumor growth derived from the pre-treatment MRSI datasets match mid-treatment imaging at 2 weeks. Customization is done by iteratively modifying model parameters to minimize the discrepancy between simulated and acquired images as judged by the squared sum differences of voxel intensities within the PTV. We hypothesize that this model will predict response at week 10, 4 weeks following completion of concurrent radiation and temozolomide. Results: Utility of the MRSI-based tumor growth model was investigated by comparing the net treatment gain against clinical observations in GBM patients. Model parameters of tumor proliferation plus effectiveness of therapy correlated with clinical outcomes being positive if progression free survival is longer than 6 months. Conclusion: MRSI could provide more accurate information the reaction-diffusion tumor growth model than conventional MR images, as it provides information about proliferation and infiltration directly and can be easily integrated with clinical observations to predict per-patient response to therapy.

SU‐E‐J‐189: The Kullback‐Leiber Divergence for Quantifying Changes in Radiotherapy Treatment Response

PURPOSEnRepeated imaging is an extremely powerful tool in current radiotherapy practice since it allows advanced tumor detection and personalized treatment assessment by quantify tumor response. Change detection algorithms have been developed for remote sensing images to mathematically quantify relevant modifications occurring between datasets of the same subject acquired at different times. We propose usage of change detectors in radiotherapy for an automated quantification of clinical changes occurring in repeated imaging.nnnMETHODSnWe explore usage of the Kullback-Leiber divergence as indicator of tumor change and quantification of treatment response. The Kullbach-Leiber divergence uses the likelihood theory to measures the distance between two statistical distributions and thus does not assume consistency in imaging. By its general nature, it can accommodate the presence of noise and variations in imaging acquisition parameters that usually hinder automated identification of clinically-relevant features.nnnRESULTSnIn a comparison of simple difference maps and the Kullbach-Leiber divergence operator, the difference maps were affected by noise and did not consistently detect changes of low intensity. In contrast, the proposed operator discerned noise by considering regional statistics around each voxel, and marked both regions with low and high contrast changes.nnnCONCLUSIONSnStatistical comparison through Kullback-Leiber divergence provides a reliable means to automatically quantify changes in repeated radiotherapy imaging.

SU-F-J-93: Automated Segmentation of High-Resolution 3D WholeBrain Spectroscopic MRI for Glioblastoma Treatment Planning

PURPOSEnWe report on an automated segmentation algorithm for defining radiation therapy target volumes using spectroscopic MR images (sMRI) acquired at nominal voxel resolution of 100 microliters.nnnMETHODSnWholebrain sMRI combining 3D echo-planar spectroscopic imaging, generalized auto-calibrating partially-parallel acquisitions, and elliptical k-space encoding were conducted on 3T MRI scanner with 32-channel head coil array creating images. Metabolite maps generated include choline (Cho), creatine (Cr), and N-acetylaspartate (NAA), as well as Cho/NAA, Cho/Cr, and NAA/Cr ratio maps. Automated segmentation was achieved by concomitantly considering sMRI metabolite maps with standard contrast enhancing (CE) imaging in a pipeline that first uses the water signal for skull stripping. Subsequently, an initial blob of tumor region is identified by searching for regions of FLAIR abnormalities that also display reduced NAA activity using a mean ratio correlation and morphological filters. These regions are used as starting point for a geodesic level-set refinement that adapts the initial blob to the fine details specific to each metabolite.nnnRESULTSnAccuracy of the segmentation model was tested on a cohort of 12 patients that had sMRI datasets acquired pre, mid and post-treatment, providing a broad range of enhancement patterns. Compared to classical imaging, where heterogeneity in the tumor appearance and shape across posed a greater challenge to the algorithm, sMRIs regions of abnormal activity were easily detected in the sMRI metabolite maps when combining the detail available in the standard imaging with the local enhancement produced by the metabolites. Results can be imported in the treatment planning, leading in general increase in the target volumes (GTV60) when using sMRI+CE MRI compared to the standard CE MRI alone.nnnCONCLUSIONnIntegration of automated segmentation of sMRI metabolite maps into planning is feasible and will likely streamline acceptance of this new acquisition modality in clinical practice.

SU-D-BRD-06: Automated Population-Based Planning for Whole Brain Radiation Therapy

PURPOSEnTreatment planning for whole brain radiation treatment is technically a simple process but in practice it takes valuable clinical time of repetitive and tedious tasks. This report presents a method that automatically segments the relevant target and normal tissues and creates a treatment plan in only a few minutes after patient simulation.nnnMETHODSnSegmentation is performed automatically through morphological operations on the soft tissue. The treatment plan is generated by searching a database of previous cases for patients with similar anatomy. In this search, each database case is ranked in terms of similarity using a customized metric designed for sensitivity by including only geometrical changes that affect the dose distribution. The database case with the best match is automatically modified to replace relevant patient info and isocenter position while maintaining original beam and MLC settings.nnnRESULTSnFifteen patients were used to validate the method. In each of these cases the anatomy was accurately segmented to mean Dice coefficients of 0.970 ± 0.008 for the brain, 0.846 ± 0.009 for the eyes and 0.672 ± 0.111 for the lens as compared to clinical segmentations. Each case was then subsequently matched against a database of 70 validated treatment plans and the best matching plan (termed auto-planned), was compared retrospectively with the clinical plans in terms of brain coverage and maximum doses to critical structures. Maximum doses were reduced by a maximum of 20.809 Gy for the left eye (mean 3.533), by 13.352 (1.311) for the right eye, and by 27.471 (4.856), 25.218 (6.315) for the left and right lens. Time from simulation to auto-plan was 3-4 minutes.nnnCONCLUSIONnAutomated database- based matching is an alternative to classical treatment planning that improves quality while providing a cost-effective solution to planning through modifying previous validated plans to match a current patients anatomy.

TH-A-BRF-09: Integration of High-Resolution MRSI Into Glioblastoma Treatment Planning

PURPOSEnIdentification of a metabolite signature that shows significant tumor cell infiltration into normal brain in regions that do not appear abnormal on standard MRI scans would be extremely useful for radiation oncologists to choose optimal regions of brain to treat, and to quantify response beyond the MacDonald criteria. We report on integration of high-resolution magnetic resonance spectroscopic imaging (HR-MRSI) with radiation dose escalation treatment planning to define and target regions at high risk for recurrence.nnnMETHODSnWe propose to supplement standard MRI with a special technique performed on an MRI scanner to measure the metabolite levels within defined volumes. Metabolite imaging was acquired using an advanced MRSI technique combining 3D echo-planar spectroscopic imaging (EPSI) with parallel acquisition (GRAPPA) using a multichannel head coil that allows acquisition of whole brain metabolite maps with 108 μl resolution in 12 minutes implemented on a 3T MR scanner. Elevation in the ratio of two metabolites, choline (Cho, elevated in proliferating high-grade gliomas) and N-acetyl aspartate (NAA, a normal neuronal metabolite), was used to image infiltrating high-grade glioma cells in vivo.nnnRESULTSnThe metabolite images were co-registered with standard contrast-enhanced T1-weighted MR images using in-house registration software and imported into the treatment-planning system. Regions with tumor infiltration are identified on the metabolic images and used to create adaptive IMRT plans that deliver a standard dose of 60 Gy to the standard target volume and an escalated dose of 75 Gy (or higher) to the most suspicious regions, identified as areas with elevated Cho/NAA ratio.nnnCONCLUSIONnWe have implemented a state-of-the-art HR-MRSI technology that can generate metabolite maps of the entire brain in a clinically acceptable scan time, coupled with introduction of an imaging co-registration/ analysis program that combines MRSI data with standard imaging studies in a clinically useful fashion.

SU‐E‐J‐199: An Image‐Based Model of Glioblastoma Growth for Treatment Response Assessment

Purpose: To develop a mathematical model of tumor growth based on MRI imaging to predict tumor growth, with potential applications in treatment response assessment to differentiate responding and non‐responding tumor types. Method: The model is composed of 3 terms describing tumor proliferation, diffusion in various tissue types, and radiation treatment effect. The proliferation terms describes the rate at which the cell replicates while the diffusion term describes tumor invasion into adjacent tissue. The radiotherapy effect term models cell response to radiation through a linear quadratic radiobiological model. Tissue‐specific evolution taking into account the differential diffusion in white and gray matter is accomplished by segmenting T1Pre, T1POST and FLAIR images using a support vector machine (SVM) algorithm. Patient specific model parameters of perfusion and diffusion are deduced from 3 serial apparent diffusion coefficients (ADC) imaging maps that are evolved under the guidance of a partial differential equation to deduce tumor evolution at later times. Results: The model tumor was applied on 19 glioblastoma patients and initialized from their ADC maps acquired before, after and 1 month after treatment completion. Voxel wise maps of the perfusion, diffusion and tumor spreading speed were created to assess treatment efficiency. The proliferation was measured in region enhancing in the pre‐treatment imaging and ranged between −0.0189 and 0.00820 mm/day while the diffusion coefficients ranged between 0.0317 and 0.4721 per day. Conclusion: We have developed a tumor growth model and applied it on clinical patients to estimate response to treatment. The model may find also applications follow‐up method for radiation treatments as well as in designing treatment margins that take into account tumor diffusion speed and direction.

SU‐E‐J‐191: A Multivariate Framework for N‐Tissue Classification in Treatment Assessment of Glioblastomas

PURPOSEnGlioblastoma is the most common primary brain tumor in adults and is rapidly fatal. Treatment monitoring of these patients has increased awareness that many patients have new areas of contrast enhancement without progressive clinical signs and symptoms. Although the enhancing areas mimic tumor progression, the lesions result from treatment effects and subsequently stabilize or improve without further treatment and are not correlated with poorer outcomes. This phenomenon has been termed pseudoprogression and is hypothesized to occur secondarily to edema and vessel permeability in the tumor area as a result of the combined effects of radiation and chemotherapy. Since the new enhancing lesions of pseudoprogression are indistinguishable from true disease progression, there is a need for a predictive model to distinguish the two phenomena.nnnMETHODnWe developed a classification algorithm that combines perfusion and diffusion MRI imaging to effectively partition the cases as one exhibiting true or pseudo progression based on a vector of features containing T1, rCBV and ADC imaging. The multi-sequence classification algorithm uses an expectation maximization (EM) algorithm that learns from training cases with known clinical outcome to assigns each voxel to a type of tissue.nnnRESULTSnA training set of 20 where the clinical outcome is known from biopsy or from long-term follow-up was used by EM algorithm to model typical imaging values within tissue of pseudo, tumor, edema, necrosis, vessels or brain anatomy to construct a database of expected values for each tissue type. When presented with a new case, the algorithm automatically classifies voxels by their geographical proximities and Mahalanobis distance to the pre-sampled values.nnnCONCLUSIONnUsage of advanced classification techniques allows automated labeling of voxels into normal, pseudoprogression or tumoral tissue types. The technique allows for early detection of pseudo progression to spare patients from unnecessary surgery or toxic chemotherapy.