Download e-book Imaging Gliomas After Treatment: A Case-based Atlas

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We have a dedicated site for Germany. This atlas is a detailed guide to the imaging appearances of gliomas following treatment with neurosurgery, radiation therapy, and chemotherapy. Normal and pathological findings are displayed in detailed MR images that illustrate the potential modifications due to treatment. Particular emphasis is placed on characteristic appearances on the newer functional MR imaging techniques, including MR spectroscopy, diffusion-weighted imaging, and perfusion imaging. These techniques are revolutionizing neuroradiology by going beyond the demonstration of macroscopic alterations to the depiction of preceding metabolic changes at the cellular and subcellular level, thereby allowing earlier and more specific diagnosis.

A key section comprising some 40 clinical cases and more than illustrations offers an invaluable clinical and research tool not only for neuroradiologists but also for neurosurgeons, radiotherapists, and medical oncologists.

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JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser. Medicine Radiology. Free Preview. Show next edition. First atlas devoted to the imaging appearances of gliomas following treatment Special attention to the role of the newer functional MR imaging techniques Includes 40 clinical cases with more than illustrations, making the book an invaluable resource for everyday practice and research see more benefits.

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Finally, if both chemotherapy and radiotherapy were ignored, the JS and DC were decreased by 6. Similarly, when ignoring the chemotherapy effects for the three patients who received only chemotherapy Fig. When the tensor information in Eq. Apparently, the inclusion of DTI did not significantly increase the accuracy as the MR images used in our experiments were for LGG patients where the tumor cell diffusion is slow.

In addition, those patients underwent surgery to include only the left tumor portion as the ground truth after the major tumor bulks were resected.

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However, we believe that this could be useful clinically for patients who are subject to undergo multiple surgeries. Finally, the role of viscoelasticity was noticed to be the least significant factor on the accuracies for LGG patients to have JS and DC decreased by ranges of [0. This is mainly because the growth of LGG is relatively slow and edema is often negligible. However, we included the viscoelasticity so that the proposed model in Eq. The formulation of the proposed model is very flexible and can be considered as a general framework that can be easily configured to produce the other modified versions of the RD model.

This is mainly because of the formulation of the last term in Eq. With the same previous configuration and slight modification to the chemotherapy effect in Eq. On the contrary, if the DTI information is used without the effects of the treatment and viscoelasticity of brain tissues, our model will reproduce the model of Jbabdi et al.

On the other hand, when including the efficacy of radiotherapy only, our model will be similar to those in refs. Our model can be identical to 33 for simulating the efficacy of both chemotherapy and radiotherapy if we exclude brain tissue heterogeneity and viscoelasticity from our model Eq. Generally, our model can be widely applied to different treatment regimens through modifications of some parameters. In addition, we believe it can be applied to other HGG, e.

Although the proposed method has some advantages, the current study is not without limitations. On one hand, comprehensive and precise comparisons were not performed. In fact, one of the biggest challenges in studying tumor growth modeling is comparing the results with recent publications. However, this is very difficult as, to the best of our knowledge, there is no public dataset that researchers can use for benchmarking.

Nevertheless, rough comparison of the proposed method with most recently published study in ref. To be more effective, the performance on other MR images of HGG patients where there will be mass effect has to be investigated but, unfortunately, such MR images are rare and currently unavailable.

Therefore, we plan to handle the aforementioned limitations in our future work when such datasets are available. Our model includes the efficacies of both chemotherapy and radiotherapy as well as the viscoelasticity of brain tissues. Our model accuracy is investigated using different experiments on both synthetic and clinical MR images of 9 LGG patients who underwent surgery and different treatment regimens with ranges of [0. To the best of our knowledge, this is the first study that includes treatment effects with brain tissues heterogeneity and viscoelasticity while ensuring the stability of the numerical solution of the model.

The proposed model aims to be clinically beneficial by providing directional and quantitative information for those patients who undergo multiple surgeries and tailor therapy for them. However, this is a preliminary work and we hope by further investigations on more datasets to be applicable in the near future. Goodenberger, M. Genetics of adult glioma. Cancer Genet. Louis, D. The WHO classification of tumours of the central nervous system. Acta Neuropathol. Claus, E. Cancer , —, doi: Krex, D.

Long-term survival with glioblastoma multiforme. Brain , —, doi: Jaeckle, K. Clatz, O. Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation. IEEE Trans. Imaging 24 , —, doi: Konukoglu, E. Image guided personalization of reaction-diffusion type tumor growth models using modified anisotropic eikonal equations. Imaging 29 , 77—95, doi: Tracqui, P. From passive diffusion to active cellular migration in mathematical models of tumour invasion. Acta Biotheor. Giese, A. Migration of human glioma cells on myelin.

Neurosurgery 38 , —, doi: Le Bihan, D. Diffusion tensor imaging: concepts and applications. Swanson, K.

A Case-based Tumor Board

Quantifying efficacy of chemotherapy of brain tumors with homogeneous and heterogeneous drug delivery. A quantitative model for differential motility of gliomas in grey and white matter. Cell Prolif. A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle. British J. Cancer 98 , —, doi: Yuan, J. Mathematical modeling of brain glioma growth using modified reaction—diffusion equation on brain MR images.

Tommaso Scarabino

Brain glioma growth model using reaction-diffusion equation with viscous stress tensor on brain MR images. Imaging 34 , —, doi: Elazab, A. Content based modified reaction-diffusion equation for modeling tumor growth of low grade glioma. Cairo Inter. Jbabdi, S.

Rekik, I. Tumor growth parameters estimation and source localization from a unique time point: Application to low-grade gliomas. Image Underst. Stretton, E.