A method to analyze the evolution of malignant gliomas using MRI (original) (raw)
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Current Medical Imaging Reviews, 2007
Tracking gliomas dynamics on MRI has became more and more important for therapeutic management. Powerful computational tools have been recently developed in this context enabling in silico growth on a virtual brain that can be matched with real 3D segmented evolution through registration between atlases and patient brain MRI data. In this paper, we provide an extensive review of existing algorithms for the three computational tasks involved in patient-specific tumor modeling: image segmentation, image registration, and in silico growth modelling (with special emphasis on the proliferation-diffusion model). Accuracy and limits of the reviewed algorithms are systematically discussed. Finally applications of these methods for both clinical practice and fundamental research are also discussed.
Post-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with Treatment
Scientific Reports, 2017
Reaction diffusion is the most common growth modelling methodology due to its simplicity and consistency with the biological tumor growth process. However, current extensions of the reaction diffusion model lack one or more of the following: efficient inclusion of treatments' effects, taking into account the viscoelasticity of brain tissues, and guaranteed stability of the numerical solution. We propose a new model to overcome the aforementioned drawbacks. Guided by directional information derived from diffusion tensor imaging, our model relates tissue heterogeneity with the absorption of the chemotherapy, adopts the linear-quadratic term to simulate the radiotherapy effect, employs Maxwell-Weichert model to incorporate brain viscoelasticity, and ensures the stability of the numerical solution. The performance is verified through experiments on synthetic and real MR images. Experiments on 9 MR datasets of patients with low grade gliomas undergoing surgery with different treatment regimens are carried out and validated using Jaccard score and Dice coefficient. The growth simulation accuracies of the proposed model are in ranges of [0.673 0.822] and [0.805 0.902] for Jaccard scores and Dice coefficients, respectively. The accuracies decrease up to 4% and 2.4% when ignoring treatment effects and the tensor information, while brain viscoelasticity has no significant impact on the accuracies. Gliomas are a primary brain tumors that arise from the glial cells due to disruption of the normal brain cell growth. Gliomas make up approximately 30% of tumors of brain and central nervous system and 80% of all malignant brain tumors 1. World Health Organization (WHO) divides glioma according to the degree of malignancy and other factors to four grades from I to IV 2. Grades I and II (known as low grade glioma, LGG) tend to be less malignant and slow-growing. These tumors account for about 25% of all glioma patients who may survive for many years (3-8) and have a high quality of life during that period 3. On the other hand, grades III and IV, known as high grade glioma (HGG), are highly malignant tumors that quickly lead to death. HGG, particularly glioblastoma multiforme, grows very fast and invades surrounding tissue. Unlike LGG, the prognosis of HGG is poor and, most likely, subject to recur after treatment with average survival time of 1 year 4. However, LGG are vulnerable to transformation to grades III and IV after variable period of time. In a study on the transformation of LGG 5 , it was observed that 60% of the patients with LGG progressed to HGG. Generally, glioma treatment comes in a form of surgery, radiotherapy, chemotherapy, or, most likely, a combination of them with the guidance of medical imaging techniques such as magnetic resonance imaging (MRI),
Differential MRI analysis for quantification of low grade glioma growth
Medical Image Analysis, 2012
A differential analysis framework of longitudinal FLAIR MRI volumes is proposed, based on non-linear gray value mapping, to quantify low-grade glioma growth. First, MRI volumes were mapped to a common range of gray levels via a midway-based histogram mapping. This mapping enabled direct comparison of MRI data and computation of difference maps. A statistical analysis framework of intensity distributions in midway-mapped MRI volumes as well as in their difference maps was designed to identify significant difference values, enabling quantification of low-grade glioma growth, around the borders of an initial segmentation. Two sets of parameters, corresponding to optimistic and pessimistic growth estimations, were proposed. The influence and modeling of MRI inhomogeneity field on a novel midway-mapping framework using image models with multiplicative contrast changes was studied. Clinical evaluation was performed on 32 longitudinal clinical cases from 13 patients. Several growth indices were measured and evaluated in terms of accuracy, compared to manual tracing. Results from the clinical evaluation showed that millimetric precision on a specific volumetric radius growth index measurement can be obtained automatically with the proposed differential analysis. The automated optimistic and pessimistic growth estimates behaved as expected, providing upper and lower bounds around the manual growth estimations.
A novel tool to analyze MRI recurrence patterns in glioblastoma
Neuro-Oncology, 2008
At least 10% of glioblastoma relapses occur at distant and even contralateral locations. This disseminated growth limits surgical intervention and contributes to neurological morbidity. Preclinical data pointed toward a role for temozolomide (TMZ) in reducing radiotherapy-induced glioma cell invasiveness. Our objective was to develop and validate a new analysis tool of MRI data to examine the clinical recurrence pattern of glioblastomas. MRIcro software was used to map the location and extent of initial preoperative and recurrent tumors on MRI of 63 patients in the European Organisation for Research and Treatment of Cancer (EORTC) 26981/22981/National Cancer Institute of Canada (NCIC) CE.3 study into the same stereotaxic space. This allowed us to examine changes of site and distance between the initial and
Oncology Reports, 2015
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.
Tomography
Reaction-diffusion models have been proposed for decades to capture the growth of gliomas. Nevertheless, these models require an initial condition: the tumor cell density distribution over the whole brain at diagnosis time. Several works have proposed to relate this distribution to abnormalities visible on magnetic resonance imaging (MRI). In this work, we verify these hypotheses by stereotactic histological analysis of a non-operated brain with glioblastoma using a 3D-printed slicer. Cell density maps are computed from histological slides using a deep learning approach. The density maps are then registered to a postmortem MR image and related to an MR-derived geodesic distance map to the tumor core. The relation between the edema outlines visible on T2-FLAIR MRI and the distance to the core is also investigated. Our results suggest that (i) the previously proposed exponential decrease of the tumor cell density with the distance to the core is reasonable but (ii) the edema outlines ...
A survey of MRI-based medical image analysis for brain tumor studies
Physics in Medicine and Biology, 2013
MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review article aims at providing a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then we review the state of the art in segmentation, registration and modeling related to tumorbearing brain images with a focus on gliomas. The objective in segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.
Characterization of a human tumorsphere glioma orthotopic model using magnetic resonance imaging
2011
Magnetic resonance imaging (MRI) is the imaging modality of choice by which to monitor patient gliomas and treatment effects, and has been applied to murine models of glioma. However, a major obstacle to the development of effective glioma therapeutics has been that widely used animal models of glioma have not accurately recapitulated the morphological heterogeneity and invasive nature of this very lethal human cancer. This deficiency is being alleviated somewhat as more representative models are being developed, but there is still a clear need for relevant yet practical models that are well-characterized in terms of their MRI features. Hence we sought to chronicle the MRI profile of a recently developed, comparatively straightforward human tumor stem cell (hTSC) derived glioma model in mice using conventional MRI methods. This model reproduces the salient features of gliomas in humans, including florid neoangiogenesis and aggressive invasion of normal brain. Accordingly, the variable, invasive morphology of hTSC gliomas visualized on MRI duplicated that seen in patients, and it differed considerably from the widely used U87 glioma model that does not invade normal brain. After several weeks of tumor growth the hTSC model exhibited an MRI contrast enhancing phenotype having variable intensity and an irregular shape, which mimicked the heterogeneous appearance observed with human glioma patients. The MRI findings reported here support the use of the hTSC glioma xenograft model combined with MRI, as a test platform for assessing candidate therapeutics for glioma, and for developing novel MR methods.
Magnetic Resonance Imaging of Gliomas
Advances in the Biology, Imaging and Therapies for Glioblastoma, 2011
Brain cancer is a life threatening neurological disorder in which malignant cells, grow, proliferate and invade the original cerebral structures of the host, hampering seriously adequate brain function. Malignant cells generate eventually a dedifferentiated tumoral mass that interferes with vital brain functions as sensory and motor activations, memory and perception and neuroendocrine regulation, among others. The fully developed tumoral mass consumes a significant part of cerebral volume resulting in cerebral compression and serious neurological impairments, such as vision or hearing disturbances and eventually lethal cerebrovascular complications. Most brain tumors remain asymptomatic during early development, revealing their symptoms and lethal nature only at later stages. Therapy is facilitated many times by an early finding, a circumstance making the neuroimaging approaches particularly useful in the detection and handling of these lesions. In the last decades, Magnetic Resonance Imaging (MRI) approaches have evolved into the most powerful and versatile imaging tool for brain tumor diagnosis, prognosis, therapy evaluation, monitoring of disease progression and planning of neurosurgical strategies. MRI methods enable the non invasive assessment of glioma morphology and functionality providing a point of likeness into histopathological grading of the tumor and helping in this way a more successful patient management. This impressive evolution is based not only for the high resolution and quality of the anatomical images obtained, but on the additional possibilities to achieve quantitative functional information on tumoral physiopathology and its repercussions in the sensorial, motor and integrative functions through the brain. The use of conventional paramagnetic or superparamagnetic contrast media allows for the identification of areas with blood-brain barrier (BBB) disruption and the recent molecular imaging approaches enable researchers to visualize molecular events associated to tumor proliferation and invasion, bringing the potentials of diagnostic imaging to the cellular and molecular aspects of tumor biology. Moreover, functional MRI approaches as performed in the clinic are endowed with the potential to detect and characterize the earliest neoangiogenic, metabolic and hemodynamic alterations induced by the neoplasm. Several advanced magnetic resonance (MR) methodologies have been proposed in the last years to assess the functional competence in healthy and pathologic brain tissue. Diffusion and perfusion MRI are probably the two main approaches that have reached a relevant clinical role