Mapping tumor growth rates in patients with malignant gliomas: A test of two algorithms

Haney, Sean M.; Thompson, Paul M.; Cloughesy, Tim; Alger, Jeff; Toga, Arthur W.

doi:10.1016/s1053-8119(00)91833-0

Cited by 19 publications

(22 citation statements)

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“…The concept may be subtle but the effect is real and important. For another example, the 'volume-doubling time' that can be calculated at any moment in our model is not constant but decreases continuously, as actually shown in the report by Haney et al (2001). The concept of a volume-doubling time is meaningless for infiltrating tumours such as gliomas.…”

Section: Discussionmentioning

confidence: 54%

“…At one theoretical extreme, for example, if there had been no dispersal, both the volume and the radius of the solid tumour could have increased exponentially with time, as postulated by Blankenberg et al (1995) and Haney et al (2001). With dispersal, however, the infiltrating cells become invisible and represent an unknowable fraction of the total, leaving the mass of those cells that remain visible (the complementary but still unknowable fraction of the total) to appear to grow quite differently: the radius actually increases linearly with time (according to Fisher's approximation) and the volume as a cubic.…”

Section: Discussionmentioning

confidence: 95%

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A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle

2007

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The prediction of the outcome of individual patients with glioblastoma would be of great significance for monitoring responses to therapy. We hypothesise that, although a large number of genetic-metabolic abnormalities occur upstream, there are two 'final common pathways' dominating glioblastoma growth -net rates of proliferation (r) and dispersal (D). These rates can be estimated from features of pretreatment MR images and can be applied in a mathematical model to predict tumour growth, impact of extent of tumour resection and patient survival. Only the pre-operative gadolinium-enhanced T1-weighted (T1-Gd) and T2-weighted (T2) volume data from 70 patients with previously untreated glioblastoma were used to derive a ratio D/r for each patient. We developed a 'virtual control' for each patient with the same size tumour at the time of diagnosis, the same ratio of net invasion to proliferation (D/r) and the same extent of resection. The median durations of survival and the shapes of the survival curves of actual and 'virtual' patients subjected to biopsy or subtotal resection (STR) superimpose exactly. For those actually receiving gross total resection (GTR), as shown by post-operative CT, the actual survival curve lies between the 'virtual' results predicted for 100 and 125% resection of the T1-Gd volume. The concordance between predicted (virtual) and actual survivals suggests that the mathematical model is realistic enough to allow precise definition of the effectiveness of individualised treatments and their site(s) of action on proliferation (r) and/or dispersal (D) of the tumour cells without knowledge of any other clinical or pathological information.

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Section: Discussionmentioning

confidence: 54%

Section: Discussionmentioning

confidence: 95%

A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle

2007

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“…From a time series of MR images, it is possible to picture the 3D invasion of GBM in the brain [1]. Since tumors can exhibit different rates of growth, it is then possible to find the best model parameters -that best match the predicted with the observed invasionto characterize the local or global tumor aggressiveness.…”

Section: B Assessing the Tumor Growth Ratementioning

confidence: 99%

Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation

Clatz

Sermesant

Bondiau

et al. 2005

IEEE Trans. Med. Imaging

319

415

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We propose a new model to simulate the 3D growth of glioblastomas multiforma (GBMs), the most aggressive glial tumors. The GBM speed of growth depends on the invaded tissue: faster in white than in gray matter, it is stopped by the dura or the ventricles. These different structures are introduced into the model using an atlas matching technique. The atlas includes both the segmentations of anatomical structures and diffusion information in white matter fibers.We use the finite element method (FEM) to simulate the invasion of the GBM in the brain parenchyma and its mechanical interaction with the invaded structures (mass effect). Depending on the considered tissue, the former effect is modeled with a reaction-diffusion or a Gompertz equation, while the latter is based on a linear elastic brain constitutive equation. In addition, we propose a new coupling equation taking into account the mechanical influence of the tumor cells on the invaded tissues. The tumor growth simulation is assessed by comparing the in-silico GBM growth with the real growth observed on two magnetic resonance images (MRIs) of a patient acquired with six months difference. Results show the feasibility of this new conceptual approach and justifies its further evaluation.

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“…First, present a two stage denoising algorithm using the image synthesis concept. The algorithm starts with internationally de noising the brain images (3D volume) and SBIR based algorithms follow by combine the outputs using entropy based fusion move toward [24,25].…”

Section: Image Processing and Segmentationmentioning

confidence: 99%

Adaptive analysis & reconstruction of 3D DICOM images using enhancement based SBIR algorithm over MRI

Gr¹,

He²,

Edara³

et al. 2018

biomedicalresearch

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The images can be visualized in three dimensional (3D) by means of different technique, these 3D techniques are used to improve the view of images. Converting two dimensional (2D) images into 3D images is a significant part of image application. A resourceful approach of 3D image reconstruction is implementing to execute a certain procedure that applied to X-ray medical image. The paper deals with the DICOM images reconstruction which is originally very helpful for manufacture of modified anatomical implant by using rapid prototyping technology. The researchers are gradually focusing on biomedical 3Dimaging improvement. The MAT LAB image processing has been powerfully developed and almost implements in every modern topographical modalities. The CT slices of involved region of modified implant in DICOM arrangement are initially pre-processed using developed MATLAB code. These files can be additional used for Rapid Prototyping. This project in biomechanical area has been urbanized to give the ability in very cheap cost with highest technological support moderately with expensive import facilities and software. This approach is implemented via four steps preprocessing, image enhancement, image contour then image reconstruction and visualization. Suggest a proposed researcher is based on novel analytical model which can put to gather in sequence from any source (i.e.) Slicer Based Image Retrieval (SBIR) mechanism to get back applicable medical imagery bottom on their features.

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Mapping tumor growth rates in patients with malignant gliomas: A test of two algorithms

Cited by 19 publications

References 0 publications

A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle

A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle

Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation

Adaptive analysis & reconstruction of 3D DICOM images using enhancement based SBIR algorithm over MRI

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