Hope for quicker, more accurate cancer cell identification
By Jeff Salton
November 26, 2010
Not many things are tougher than dealing with a diagnosis of cancer. But often the protracted wait for biopsy results, and the uncertainty surrounding them, can be excruciating for patients and their loved ones. Now a research team at the University of Illinois has developed a tissue-imaging technique that produces easily identifiable, color-coded images of body tissue that clearly outline tumor boundaries. What’s more, the process takes less than five minutes.
The team at the University of Illinois, led by professor and physician Stephen A. Boppart, demonstrated the process, called nonlinear interferometric vibrational imaging (NIVI) – a form of microscopy – on rat breast-cancer cells and tissues. Prof Boppart says the process delivers results with more than 99 percent confidence.
Present cancer diagnosis methods focus on visual interpretations of cell shape and structure. Doctors will usually remove a small suspect tissue sample from a patient, add a dye to make the cells more identifiable. Then a pathologist uses a microscope to study the sample, looking for anything unusual. It is then common for another pathologist to be consulted to confirm the findings.
“The diagnosis is made based on very subjective interpretation – how the cells are laid out, the structure, the morphology,” says Prof Boppart. “This is what we call the gold standard for diagnosis. We want to make the process of medical diagnostics more quantitative and more rapid.”
The NIVI differs from the traditional method of identification by assessing and constructing images based on molecular composition. Normal cells have high concentrations of lipids, but cancerous cells have abnormally high protein concentrations, allowing the researchers to easily and accurately differentiate between tumors and healthy tissue – without having to wait for the dye to take effect.
The researchers say each type of molecule can be distinguished by its unique vibrational state of energy in its bonds. They add that by using the right frequencies to excite the molecules, a signal can be produced that can identify cells with high concentrations of that particular molecule. NIVI does this by using two beams of light on a tissue sample.
“The analogy is like pushing someone on a swing,” says Prof Boppart. “If you push at the right time point, the person on the swing will go higher and higher. If you don’t push at the right point in the swing, the person stops.
“If we use the right optical frequencies to excite these vibrational states, we can enhance the resonance and the signal.”
When used in tandem, NIVI’s two laser beams work a delicate balancing act. One beam acts as a reference, and when combined with the second beam that produces the excited sample signal, background noise is canceled and the molecular signal is isolated, making it easier to identify.
Statistical analysis is then converted into a color-coded image at each point in the tissue: blue for normal cells, red for cancer.
The NIVI technique is not only quicker, the researchers say it delivers more accurate mapping of tumor boundaries, which has been troublesome for pathologists in the past.
They say the margin of uncertainty in visual diagnosis can be wide as pathologists struggle to discern where a tumor ends and normal tissue begins. But the red-blue color coding shows an uncertain boundary zone of about 100 microns (a cell or two).
“Sometimes it’s very hard to tell visually whether a cell is normal or abnormal,” Boppart said. “But molecularly, there are fairly clear signatures.”
The researchers are looking at applying their technique to test for other types of molecules, and how to make their analysis even faster. They are also studying new laser sources in an effort to reduce the size of the NIVI device, in order to make it more compact with a view to portability.
“We think it will have many different applications for cancer diagnostics, for optical biopsies and other types of diagnostics,” says Prof Boppart.
The research team includes Eric Chaney, a research specialist at the Beckman Institute; Stephen Boppart, a professor of electrical and computer engineering, of bioengineering and of medicine; Martin Gruebele, a professor of chemistry and of physics; and Wladamir Benalcazar, a graduate fellow at the Beckman Institute.
The findings will be published in the December issue of the journal Cancer Research.
The paper is also available online.