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Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy.
Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context
We present an efficient deep learning approach for the challenging task of
tumor segmentation in multisequence MR images. In recent years, Convolutional
Neural Networks (CNN) have achieved state-of-the-art performances in a large
variety of recognition tasks in medical imaging. Because of the considerable
computational cost of CNNs, large volumes such as MRI are typically processed
by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D
patches. In this paper we introduce a CNN-based model which efficiently
combines the advantages of the short-range 3D context and the long-range 2D
context. To overcome the limitations of specific choices of neural network
architectures, we also propose to merge outputs of several cascaded 2D-3D
models by a voxelwise voting strategy. Furthermore, we propose a network
architecture in which the different MR sequences are processed by separate
subnetworks in order to be more robust to the problem of missing MR sequences.
Finally, a simple and efficient algorithm for training large CNN models is
introduced. We evaluate our method on the public benchmark of the BRATS 2017
challenge on the task of multiclass segmentation of malignant brain tumors. Our
method achieves good performances and produces accurate segmentations with
median Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854
(enhancing core). Our approach can be naturally applied to various tasks
involving segmentation of lesions or organs.Comment: Submitted to the journal Computerized Medical Imaging and Graphic
Magnetic Resonance Imaging of Gliomas
Open Access.This work was supported in part by grants CTQ2010-20960-C02-02 to P.L.L. and grant SAF2008-01327 to S.C. A.M.M. held an Erasmus Fellowship from Coimbra University and E.C.C. a predoctoral CSIC contract.Peer Reviewe
Hypoxic Cell Waves around Necrotic Cores in Glioblastoma: A Biomathematical Model and its Therapeutic Implications
Glioblastoma is a rapidly evolving high-grade astrocytoma that is
distinguished pathologically from lower grade gliomas by the presence of
necrosis and microvascular hiperplasia. Necrotic areas are typically surrounded
by hypercellular regions known as "pseudopalisades" originated by local tumor
vessel occlusions that induce collective cellular migration events. This leads
to the formation of waves of tumor cells actively migrating away from central
hypoxia. We present a mathematical model that incorporates the interplay among
two tumor cell phenotypes, a necrotic core and the oxygen distribution. Our
simulations reveal the formation of a traveling wave of tumor cells that
reproduces the observed histologic patterns of pseudopalisades. Additional
simulations of the model equations show that preventing the collapse of tumor
microvessels leads to slower glioma invasion, a fact that might be exploited
for therapeutic purposes.Comment: 29 pages, 9 figure
Neoangiogenesis assessment in gliomas with 68GaâNODAGAâRGD PET and IVIM MR Imaging - a pilot study.
Background and purpose: Recent development of anti-angiogenic drugs in oncology without any direct marker of angiogenesis has lead to the elaboration of a new PET tracer referred as 68Ga-NODAGA-RGD. This radiotracer consists of a sequence of three amino acids abbreviated RGD and has the to capacity to bind to αVÎČ3 integrins present tumoral vessels. We evaluate this new tracer in the framework of tumoral angiogenesis in native gliomas. The aim of this study is to describe the distribution of the tracer compared to 18F- FET PET that highlights tumor protein transport and to IVIM MRI that highlights micro-perfusion. Long-term work consists of determining whether RGD tracer could allow a better selection of patients who could benefit from an anti-angiogenic treatment and an earlier assessment of response to treatment.
Materials and methods: Two patients were included in this study. A qualitative analysis of the tracer uptake compared to 18F-FET PET and IVIM MRI was realized.
Results: Our first patient had a bi-component glioblastoma/high-grade glioma with an anterior part corresponding to a WHO grade IV glioblastoma and a posterior part to a high-grade glioma. RGD was only taken up by the glioblastoma part whereas 18F-FET was taken up by both parts. The comparison with IVIM showed no correlation in this patient. The second patient with a WHO grade II ganglioglioma showed no RGD uptake, no IVIM signal but a high 18F-FET uptake by the whole tumor.
Conclusions: RGD uptake shows a different process than 18F-FET PET and IVIM MRI in gliomas in two patients. This needs to be further examined in a larger cohort to consolidate our interesting preliminary results
P35. Intratumoral patterns of clonal evolution in meningiomas
NĂșm. a Art PĂșblic: 1815Digitalitzat per Tecnodo
Mathematical modeling to elucidate brain tumor abrogation by immunotherapy with T11 target structure
T11 Target structure (T11TS), a membrane glycoprotein isolated from sheep
erythrocytes, reverses the immune suppressed state of brain tumor induced
animals by boosting the functional status of the immune cells. This study aims
at aiding in the design of more efficacious brain tumor therapies with T11
target structure. We propose a mathematical model for brain tumor (glioma) and
the immune system interactions, which aims in designing efficacious brain tumor
therapy. The model encompasses considerations of the interactive dynamics of
macrophages, cytotoxic T lymphocytes, glioma cells, TGF-, IFN-
and the T11TS. The system undergoes sensitivity analysis, that determines which
state variables are sensitive to the given parameters and the parameters are
estimated from the published data. Computer simulations were used for model
verification and validation, which highlight the importance of T11 target
structure in brain tumor therapy
Mathematical Modeling of Human Glioma Growth Based on Brain Topological Structures: Study of Two Clinical Cases
Gliomas are the most common primary brain tumors and yet almost incurable due mainly to their great invasion capability. This represents a challenge to present clinical oncology. Here, we introduce a mathematical model aiming to improve tumor spreading capability definition. The model consists in a time dependent reaction-diffusion equation in a three-dimensional spatial domain that distinguishes between different brain topological structures. The model uses a series of digitized images from brain slices covering the whole human brain. The Talairach atlas included in the model describes brain structures at different levels. Also, the inclusion of the Brodmann areas allows prediction of the brain functions affected during tumor evolution and the estimation of correlated symptoms. The model is solved numerically using patient-specific parametrization and finite differences. Simulations consider an initial state with cellular proliferation alone (benign tumor), and an advanced state when infiltration starts (malign tumor). Survival time is estimated on the basis of tumor size and location. The model is used to predict tumor evolution in two clinical cases. In the first case, predictions show that real infiltrative areas are underestimated by current diagnostic imaging. In the second case, tumor spreading predictions were shown to be more accurate than those derived from previous models in the literature. Our results suggest that the inclusion of differential migration in glioma growth models constitutes another step towards a better prediction of tumor infiltration at the moment of surgical or radiosurgical target definition. Also, the addition of physiological/psychological considerations to classical anatomical models will provide a better and integral understanding of the patient disease at the moment of deciding therapeutic options, taking into account not only survival but also life quality
Forecasting Molecular Features in IDH-Wildtype Gliomas: The State of the Art of Radiomics Applied to Neurosurgery
Simple Summary The prognostic expectancies of patients affected by glioblastoma have remained almost unchanged during the last thirty years. Along with specific oncological research and surgical technical alternatives, corollary disciplines are requested to provide their contributions to improve patient management and outcomes. Technological improvements in radiology have led to the development of radiomics, a new discipline able to detect tumoral phenotypical features through the extraction and analysis of a large amount of data. Intuitively, the early foreseeing of glioma features may constitute a tremendous contribution to the management of patients. The present manuscript analyzes the pertinent literature regarding the current role of radiomics and its potentialities. Background: The fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS), published in 2021, marks a step forward the future diagnostic approach to these neoplasms. Alongside this, radiomics has experienced rapid evolution over the last several years, allowing us to correlate tumor imaging heterogeneity with a wide range of tumor molecular and subcellular features. Radiomics is a translational field focused on decoding conventional imaging data to extrapolate the molecular and prognostic features of tumors such as gliomas. We herein analyze the state-of-the-art of radiomics applied to glioblastoma, with the goal to estimate its current clinical impact and potential perspectives in relation to well-rounded patient management, including the end-of-life stage. Methods: A literature review was performed on the PubMed, MEDLINE and Scopus databases using the following search items: "radiomics and glioma", "radiomics and glioblastoma", "radiomics and glioma and IDH", "radiomics and glioma and TERT promoter", "radiomics and glioma and EGFR", "radiomics and glioma and chromosome". Results: A total of 719 articles were screened. Further quantitative and qualitative analysis allowed us to finally include 11 papers. This analysis shows that radiomics is rapidly evolving towards a reliable tool. Conclusions: Further studies are necessary to adjust radiomics' potential to the newest molecular requirements pointed out by the 2021 WHO classification of CNS tumors. At a glance, its application in the clinical routine could be beneficial to achieve a timely diagnosis, especially for those patients not eligible for surgery and/or adjuvant therapies but still deserving palliative and supportive care
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