Angioarchitectural heterogeneity in human glioblastoma multiforme: A fractal-based histopathological assessment (original) (raw)
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Scientific Reports, 2012
Fractal analysis is widely applied to investigate the vascular system in physiological as well as pathological states. We propose and examine a computer-aided and fractal-based image analysis technique to quantify the microvascularity in histological specimens of WHO grade II and III gliomas. A computer-aided and fractal-based analysis was used to describe the microvessels and to quantify their geometrical complexity in histological specimens collected from 17 patients. The statistical analysis showed that the fractal-based indexes are the most discriminant parameters to describe the microvessels. The computer-aided quantitative analysis also showed that grade III gliomas are generally more vascularized than grade II gliomas. The fractal parameters are reliable quantitative indicators of the neoplastic microvasculature, making them potential surrogate biomarkers. The qualitative evaluation currently performed by the neuropathologist can be combined with the computer-assisted quantitative analysis of the microvascularity to improve the diagnosis and optimize the treatment of patients with brain cancer. SUBJECT AREAS: MEDICAL RESEARCH CANCER IMAGING SOFTWARE
Microvascular Research, 2010
Neuroradiological and metabolic imaging is a fundamental diagnostic procedure in the assessment of patients with primary and metastatic brain tumors. The correlation between objective parameters capable of quantifying the neoplastic angioarchitecture and imaging data may improve our understanding of the underlying physiopathology and make it possible to evaluate treatment efficacy in brain tumors. Only a few studies have so far correlated the quantitative parameters measuring the neovascularity of brain tumors with the metabolic profiles measured by means of amino acid uptake in positron emission tomography (PET) scans. Fractal geometry offers new mathematical tools for the description and quantification of complex anatomical systems, including microvascularity. In this study, we evaluated the microvascular network complexity of six cases of human glioblastoma multiforme quantifying the surface fractal dimension on CD34 immunostained specimens. The microvascular fractal dimension was estimated by applying the box-counting algorithm. As the fractal dimension depends on the density, size and shape of the vessels, and their distribution pattern, we defined it as an index of the whole complexity of microvascular architecture and compared it with the uptake of 11 C-methionine (MET) assessed by PET. The different fractal dimension values observed showed that the same histological category of brain tumor had different microvascular network architectures. Fractal dimension ranged between 1.19 and 1.77 (mean: 1.415 ± 0.225), and the uptake of 11 C-methionine ranged between 1.30 and 5.30. A statistically significant direct correlation between the microvascular fractal dimension and the uptake of 11 C-methionine (p = 0.02) was found. Our preliminary findings indicate that that vascularity (estimated on the histologic specimens by means of the fractal dimension) and 11 C-methionine uptake (assessed by PET) closely correlate in glioblastoma multiforme and that microvascular fractal dimension can be a useful parameter to objectively describe and quantify the geometrical complexity of the microangioarchitecture in glioblastoma multiforme.
MODELING OF BRAIN CANCER DEVELOPMENT USING FRACTAL GEOMETRY
This investigation attempts to analyze the growth of brain cancerous tissue employing the technique of fractal geometry, specifically computing the fractal dimensions. The fractal dimensions were determined using a box-counting method. The earliest stage showed lowest fractal dimension (D), 1.6541. The intermediate stage showed maximum fractal dimension of 1.7016 while the last stage available showed slightly lower D value of 1.6847. These results were found to be in agreement with those of previous studies on certain cancer types. The use of fractal analysis in the diagnosis of cancer is discussed. It has been shown that the fractal dimension of 2D microvasculature networks can discriminate between normal versus tumor tissue. Fractal dimensions also differed between various stages of malignancy.
Neuroradiology, 2012
Introduction Susceptibility-weighted imaging (SWI) with high-and ultra-high-field magnetic resonance is a very helpful tool for evaluating brain gliomas and intratumoral structures, including microvasculature. Here, we test whether objective quantification of intratumoral SWI patterns by applying fractal analysis can offer reliable indexes capable of differentiating glial tumor grades. Methods Thirty-six patients affected by brain gliomas (grades II-IV, according to the WHO classification system) underwent MRI at 7 T using a SWI protocol. All images were collected and analyzed by applying a computer-aided fractal image analysis, which applies the fractal dimension as a measure of geometrical complexity of intratumoral SWI patterns. The results were subsequently statistically correlated to the histopathological tumor grade. Results The mean value of the fractal dimension of the intratumoral SWI patterns was 2.086±0.413. We found a trend of higher fractal dimension values in groups of higher histologic grade. The values ranged from a mean value of 1.682±0.278 for grade II gliomas to 2.247±0.358 for grade IV gliomas (p00.013); there was an overall statistically significant difference between histopathological groups. Conclusion The present study confirms that SWI at 7 T is a useful method for detecting intratumoral vascular architecture of brain gliomas and that SWI pattern quantification by means of fractal dimension offers a potential objective morphometric image biomarker of tumor grade.
Evaluation of malignancy in tumors of the central nervous system using fractal dimension
2000
In this work, we propose the use of the concepts of Fractal Dimension and Digital Image Processing, as a possible methodology to characterize the degree of malignancy of neoplastic structures located in the Central Nervous System. The images were detected by Magnetic Resonance Imaging (MRI) techniques including Proton Density, T 1 , and T 2 images. Malignant lesions (Gliomas) were compared with benign ones (Cysts). The Correlation Dimension, Lyapunov Exponents and Information Dimension were used as the relevant geometrical properties to characterize the irregular edge present in a particular structure. The edge was obtained by means of an edge detector operator and afterwards a codification procedure based on Fourier Descriptors was used to generate a numerical array or Time Series. The analysis of the processed images revealed that the relevant geometrical properties exhibit a different behavior in the case of gliomas compared to cystic lesions, a fact that can be used by the physician as an auxiliary tool to evaluate the malignancy of neoplastic structures in the brain.
From homogeneous to fractal normal and tumorous microvascular networks in the brain
Journal of Cerebral Blood Flow & Metabolism, 2007
We studied normal and tumorous three-dimensional (3D) microvascular networks in primate and rat brain. Tissues were prepared following a new preparation technique intended for high-resolution synchrotron tomography of microvascular networks. The resulting 3D images with a spatial resolution of less than the minimum capillary diameter permit a complete description of the entire vascular network for volumes as large as tens of cubic millimeters. The structural properties of the vascular networks were investigated by several multiscale methods such as fractal and powerspectrum analysis. These investigations gave a new coherent picture of normal and pathological complex vascular structures. They showed that normal cortical vascular networks have scaleinvariant fractal properties on a small scale from 1.4 lm up to 40 to 65 lm. Above this threshold, vascular networks can be considered as homogeneous. Tumor vascular networks show similar characteristics, but the validity range of the fractal regime extend to much larger spatial dimensions. These 3D results shed new light on previous two dimensional analyses giving for the first time a direct measurement of vascular modules associated with vessel-tissue surface exchange.
Morphological and Fractal Properties of Brain Tumors
Frontiers in Physiology
Tumor interface dynamics is a complex process determined by cell proliferation and invasion to neighboring tissues. Parameters extracted from the tumor interface fluctuations allow for the characterization of the particular growth model, which could be relevant for an appropriate diagnosis and the correspondent therapeutic strategy. Previous work, based on scaling analysis of the tumor interface, demonstrated that gliomas strictly behave as it is proposed by the Family-Vicsek ansatz, which corresponds to a proliferative-invasive growth model, while for meningiomas and acoustic schwannomas, a proliferative growth model is more suitable. In the present work, other morphological and dynamical descriptors are used as a complementary view, such as surface regularity, one-dimensional fluctuations represented as ordered series and bi-dimensional fluctuations of the tumor interface. These fluctuations were analyzed by Detrended Fluctuation Analysis to determine generalized fractal dimension...
Frontiers in Physiology, 2023
Purpose: The main purpose of this study was to comprehensively investigate the potential of fractal dimension (FD) measures in discriminating brain gliomas into low-grade glioma (LGG) and high-grade glioma (HGG) by examining tumor constituents and non-tumorous gray matter (GM) and white matter (WM) regions. Methods: Retrospective magnetic resonance imaging (MRI) data of 42 glioma patients (LGG, n = 27 and HGG, n = 15) were used in this study. Using MRI, we calculated different FD measures based on the general structure, boundary, and skeleton aspects of the tumorous and non-tumorous brain GM and WM regions. Texture features, namely, angular second moment, contrast, inverse difference moment, correlation, and entropy, were also measured in the tumorous and nontumorous regions. The efficacy of FD features was assessed by comparing them with texture features. Statistical inference and machine learning approaches were used on the aforementioned measures to distinguish LGG and HGG patients. Results: FD measures from tumorous and non-tumorous regions were able to distinguish LGG and HGG patients. Among the 15 different FD measures, the general structure FD values of enhanced tumor regions yielded high accuracy (93%), sensitivity (97%), specificity (98%), and area under the receiver operating characteristic curve (AUC) score (98%). Non-tumorous GM skeleton FD values also yielded good accuracy (83.3%), sensitivity (100%), specificity (60%), and AUC score (80%) in classifying the tumor grades. These measures were also found to be significantly (p < 0.05) different between LGG and HGG patients. On the other hand, among the 25 texture features, enhanced tumor region features, namely, contrast, correlation, and entropy, revealed significant differences between LGG and HGG. In machine learning, the enhanced tumor region texture features yielded high accuracy, sensitivity, specificity, and AUC score. Conclusion: A comparison between texture and FD features revealed that FD analysis on different aspects of the tumorous and non-tumorous components not only distinguished LGG and HGG patients with high statistical significance and classification accuracy but also provided better insights into glioma grade classification. Therefore, FD features can serve as potential neuroimaging biomarkers for glioma.
Three-Dimensional Microvascular Networks Fractal Structure: Potential for Tissue Characterization?
Advances in Heat and Mass Transfer in Biotechnology
It has been shown that the fractal dimension of 2D microvascular networks can discriminate between normal vs. tumor tissue (Gazit et al., 1995, 1997). We have determined the fractal characteristics of five 3D microvascular networks and conclude on the correlation between the computed fractal characteristics and the nature of the tissue of origin. The networks considered in the fractal analysis study were one rat tumor network (RT), one nude mouse tumor (NMT), one hamster skeletal muscle (HSM), one rat cremaster (RC), and one rat cerebral cortex (RCC). The networks were digitized in a 3D lattice starting from the known length, diameter and position of each segment in the network. The digitization process was performed such that the ratio between the initial occupation fraction of the vessels in the network and the occupation fraction after digitization is close to one. The resultant cubic lattices were analyzed using the concept of asymptotic fractals. The fractal dimension df, and t...
World neurosurgery
The need for new and objective indexes for the neuroradiologic follow-up of brain tumors and for monitoring the effects of antiangiogenic strategies in vivo led us to perform a technical study on four patients who received computerized analysis of tumor-associated vasculature with ultra-high-field (7 T) magnetic resonance imaging (MRI). The image analysis involved the application of susceptibility weighted imaging (SWI) to evaluate vascular structures. Four patients affected by recurrent malignant brain tumors were enrolled in the present study. After the first 7-T SWI MRI procedure, the patients underwent antiangiogenic treatment with bevacizumab. The imaging was repeated every 2 weeks for a period of 4 weeks. The SWI patterns visualized in the three MRI temporal sequences were analyzed by means of a computer-aided fractal-based method to objectively quantify their geometric complexity. In two clinically deteriorating patients we found an increase of the geometric complexity of the...