New optical method promises more accurate diagnosis for cancer patients
Mike Koon, Engineering Communications Office
- A team of University of Illinois researchers have developed a breakthrough optical method for determining whether tissue is cancerous.
- While regular microscopes use one beam of light, which gives the doctor a brightness map, Associate Professor Gabriel Popescu's invention uses two.
- The team plans to create a server with biopsy slides, which they call the "Netflix of biopsies," which pathologists can have access to in order to better correlate the patient's prognosis and treatment based on correlations with a vast repository.
Current state-of-the-art in cancer pathology relies on the human evaluation under the microscope of stained tissue slices, which is intrinsically a subjective procedure.
The future, researchers say, will involve a much more engineering-based approach to biomedicine. On the optics side, that future will involve providing not just visual images of cells and tissues, but also numbers and physical quantities that tell more about structure and dynamics.
With that in mind, a team of University of Illinois researchers have developed a breakthrough optical method for determining whether tissue is cancerous. Their findings, which were published in SPIE’s Journal of Biomedical Optics, will greatly improve the accuracy of treatment in cancer patients.Hassan Majeed, one of the lead authors of the study. “Since the pixel values in our images represent physical properties of tissue we can now move on to build a quantitative, automated screening method based on advanced computer algorithms.”
Majeed researches with Gabriel Popescu, an associate professor of Electrical and Computer Engineering and the director of the Quantitative Light Imaging Laboratory at the Beckman Institute. Much of Popescu’s work to date in this area has been on prostate cancer.
“Only one out of every 30 surgeries on prostate cancer patients is actually saving a life,” Popescu said. “However, right now there are no good ways to tailor the treatment for each patient, so surgeries are done for precaution. Clearly, there is a need for predictive power. We showed recently that our label-free technique, because it is sensitive to the nano-scale tissue architecture, can inform much more accurately about prognosis of prostate cancer and help doctors better individualize treatment for each patient.”
Majeed has been using the label-free technique in targeting breast cancer cells, and published the paper “Breast Cancer Diagnosis Using Spatial Light Interface Microscopy.” This high-throughput label-free imaging modality has shown imaging resolution and contrast comparable with standard histopathological imaging, while the image pixel values contain quantitative information about cellular organization.
Popescu has been in the process of developing microscopy techniques, which don’t require staining, for the past 12 years or so. One of the major benefits of using unstained cells is that they have a longer lifespan, which means they can be studied longer. His lab has been partnering with pathologists at both Presence Covenant Medical Center in Urbana and at the University of Illinois Hospital in Chicago.
“We realize there is a lot of information in our images that could be useful for clinical applications like cancer screening and blood testing,” Popescu said.
Because researchers in his lab, like Majeed, have shown that it is possible to obtain quantitative information from a cell, that information can be shared over a large network. That’s where the research becomes a Big Data project, using computation to even more accurately make diagnoses, prognoses, and individualize treatment.
“We have a dream that in a few years, we will have these machines (our microscopes) paired with computing servers that will be able to screen tissues and automatically provide clinical information by quickly sifting through the majority of the specimens and then only flagging the important cases, to be further evaluated by the pathologist,” Popescu said. “The future, we believe, will be combining an advanced imaging technique like ours, which has the advantage of being quantitative and therefore more suitable to computation, with advanced algorithms in machine learning.”
This method compares closely with the computational advances, for example, in facial recognition, fingerprint reading, and more. By putting all the biopsies on a server (some 200 million procedures per year in the United States alone), which Popescu refers to as the “Netflix of Biopsies,” doctors will have the ability to better correlate the patient’s prognosis and treatment based on correlations with a vast repository.
Popescu realizes that in order for the technique to be accepted in the community it must be validated. The group has trained a number of pathologists on the technique, who in turn are comparing results with those of current methods for diagnosis. Popescu has also started a company called Phi Optics, which sells an interferometric imaging module that attaches to existing microscopes and incorporates this latest state-of-the-art technology.
“What we hope is that having the microscopes available to the pathologists directly will make a huge difference,” Popescu said. “The instruments are much faster than they used to be, but really to be able to validate it, you need to increase the number of users.”
Popescu adds that while researchers are scratching the surface in this quantitative phase imaging, the field is growing fast. At least 10 startups across the world are manufacturing similar optical devices for the academic environment.
“That’s a sign that biologists are going to have access to this commercially,” Popescu said. “ That makes me very happy because it means we will be able to discover new applications that way.”