Fractal Dimension of Tumor Microvasculature by DCE-US: Preliminary Study in Mice

Abstract

Neoangiogenesis, which results in the formation of an irregular network of microvessels, plays a fundamental role in the growth of several types of cancer. Characterization of microvascular architecture has therefore gained increasing attention for cancer diagnosis, treatment monitoring and evaluation of new drugs. However, this characterization requires immunohistologic analysis of the resected tumors. Currently, dynamic contrast-enhanced ultrasound imaging (DCE-US) provides new options for minimally invasive investigation of the microvasculature by analysis of ultrasound contrast agent (UCA) transport kinetics. In this article, we propose a different method of analyzing UCA concentration that is based on the spatial distribution of blood flow. The well-known concept of Mandelbrot allows vascular networks to be interpreted as fractal objects related to the regional blood flow distribution and characterized by their fractal dimension (FD). To test this hypothesis, the fractal dimension of parametric maps reflecting blood flow, such as UCA wash-in rate and peak enhancement, was derived for areas representing different microvascular architectures. To this end, subcutaneous xenograft models of DU-145 and PC-3 prostate-cancer lines in mice, which show marked differences in microvessel density spatial distribution inside the tumor, were employed to test the ability of DCE-US FD analysis to differentiate between the two models. For validation purposes, the method was compared with immunohistologic results and UCA dispersion maps, which reflect the geometric properties of microvascular architecture. The results showed good agreement with the immunohistologic analysis, and the FD analysis of UCA wash-in rate and peak enhancement maps was able to differentiate between the two xenograft models (p < 0.05).

Application of fractal dimension analysis and photodynamic diagnosis in the case of differentiation between lichen planus and leukoplakia: A preliminary study

“…Fractal dimension analysis can be very useful in medicine; examples of FDA usage in medicine are mammographic image analysis, or estimation of tumor neoangiogenesis or of the pattern of coronary vessels. [10][11][12] Fractal dimension analysis of jaw bone cone beam computed tomography (CBCT) images is useful in the diagnosis of osteoporosis. 13 In our study, we tried to distinguish oral leukoplakia and LP using FDA with a classical white-light examination and PDD.…”

Ley exponencial de la dinámica cardiaca caótica aplicada a 18 horas

“…La ley de la dinámica caótica cardiaca exponencial permitió deducir la totalidad de posibles atractores cardiacos; la dinámica cardiaca presenta un comportamiento caótico, y la representación gráfica de dicha dinámica presenta una forma irregular que puede ser caracterizada con la geometría fractal, esta geometría ha sido aplicada en diferentes ámbitos de la medicina [21][22][23][24][25] buscando diferenciar normalidad de enfermedad, sin embargo, las dimensiones fractales aisladas han mostrado no ser concluyentes para lograr esta diferenciación 26,27 , por esto se han desarrollado diferentes conceptos para lograr dicha diferenciación 28,29 . De igual forma, en la dinámica cardiaca, las dimensiones fractales aisladas han mostrado ser insuficientes para diferenciar normalidad y enfermedad 20,30,31 , siendo los espacios de ocupación del atractor, lo que permite establecer un diagnóstico para cada caso particular, independientemente de metodologías estadísticas y epidemiológicas que describen comportamientos poblacionales.…”

Comparison of region-of-interest-averaged and pixel-averaged analysis of DCE-MRI data based on simulations and pre-clinical experiments

“…However, for human prostate cancer in mouse xenograft models, it is known that different cell lines may lead to different microvascular density (MVD) (Rofstad and Mathiesen, 2010). Some cell lines may show higher vascular density in the center and lower in periphery, such as DU-145 cell line; and others show homogeneous MVD, such as human prostate cancer PC-3 cell lines (Saidov et al, 2016, Farace et al, 2011, Pang et al, 2011). Extracted physiological parameters could be impacted by spatial distribution of MVD within the tumor ROI.…”