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Clarkson University Team Finds Possible New Means of Diagnosing Cancer
[A photograph for media use is available at http://www.clarkson.edu/news/photos/fractal-cell.jpg.]
A Clarkson University team led by Professor Igor Sokolov has found that cancer cells can be identified with very high precision by means of a specific "fractal" analysis of images of cell surface at the nanoscale.
Fractals are “self-similar” irregular shapes that repeat their pattern when zoomed in or out upon. These complex disorderly patterns are typically formed under far-from-equilibrium conditions, or emerge from chaos.
Examples of fractal shape range from the large-scale structure of the universe to the shapes of trees and snowflakes.
The work was published July 8 in the top physics journal Physical Review Letters and selected as one of the "Editors’ Suggestions." It can be found at http://prl.aps.org/abstract/PRL/v107/i2/e028101 .
"Cancerous transformations are associated with chaotic disorganization of many processes in a cell," says Sokolov. "It has been known that fractal behavior can occur in chaotic systems. Fractal behavior was indeed found many decades ago in histological cross-section of tissues, when tissue becomes cancerous. However, the emergence of fractal behavior at the cellular level had not yet been discovered, but we have finally found it. This may shed light on the nature of cancer from a new physics prospective."
This discovery can potentially be applied to cancer diagnostics. One of the problems in cancer detection is its constant variability, mutations. That is one of the main reasons for the difficulty in segregation of cancerous cells by using biochemical labeling methods.
The analysis of fractal behavior could be tried as a new gold standard in identification of cancerous cells -- similar to the histological cross-section analysis of tissues, which is the gold standard in cancer identification today. Thus, fractal behavior might be used for early detection of cervical cancer with accuracy surpassing existing methods.
While this finding may advance to novel methods in diagnosis of cancer, Sokolov says that “the problem is in the variability of human subjects. The found difference was verified for 12 human subjects. This might be enough for a demonstration, but it is not sufficient to speak about a new clinical method. More statistics must be collected before we can speak about clinical applications.”
As the team prepares a more detailed description of the results, Sokolov and Clarkson Biology Professor Craig D. Woodworth have submitted a proposal for further study to the National Institutes of Health.
The Clarkson team consists of Sokolov, who has appointments in Physics and Chemistry and Biomolecular Science; Woodworth, a cervical cancer expert; Maxim Dokukin, a physics postdoctoral fellow; and physics graduate students Ravi M. Gaikwad (now a postdoctoral fellow in the University of Waterloo) and Nataliaa Guz.
The other members of Sokolov’s group, Shajesh Palantavida, a physics postdoctoral fellow, and physics graduate students Shyuzhene Li and Vivekanand Kalaparthi work on biosensors, self-assembly of particles, and the study of human skin.
The research was done within Clarkson's Nanoengineering and Biotechnology Laboratories Center (NABLAB), led by Sokolov, a unit established to promote cross-disciplinary collaborations within the University. It comprises more than a dozen faculty members to capitalize on the expertise of Clarkson scholars in the areas of cancer cell research, fine particles for bio and medical applications, synthesis of smart materials, advancement biosensors, etc.
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Photo caption: A photo illustration demonstrating examples of a fractal and a scanning electron microscopy (SEM) image of a cervical epithelial cell. Zoom images are shown at the bottom raw. The zoom of the cell surface was obtained with atomic force microscopy (AFM). A Clarkson University team led by Professor Igor Sokolov has found that cancer cells can be identified with very high precision by means of a specific "fractal" analysis of images of cell surface at the nanoscale.