Researchers are leveraging Ohio Supercomputer Center resources to develop computer-assisted diagnosis tools that will provide pathologists grading Follicular Lymphoma samples with quicker, more consistently accurate diagnoses.
"The advent of digital whole-slide scanners in recent years has spurred a revolution in imaging technology for histopathology," according to Metin N. Gurcan, Ph.D., an associate professor of Biomedical Informatics at The Ohio State University Medical Center. "The large multi-gigapixel images produced by these scanners contain a wealth of information potentially useful for computer-assisted disease diagnosis, grading and prognosis."
Follicular Lymphoma (FL) is one of the most common forms of non-Hodgkin Lymphoma occurring in the United States. FL is a cancer of the human lymph system that usually spreads into the blood, bone marrow and, eventually, internal organs.
A World Health Organization pathological grading system is applied to biopsy samples; doctors usually avoid prescribing severe therapies for lower grades, while they usually recommend radiation and chemotherapy regimens for more aggressive grades.
Accurate grading of the pathological samples generally leads to a promising prognosis, but diagnosis depends solely upon a labor-intensive process that can be affected by human factors such as fatigue, reader variation and bias. Pathologists must visually examine and grade the specimens through high-powered microscopes.
Processing and analysis of such high-resolution images, Gurcan points out, remain non-trivial tasks, not just because of the sheer size of the images, but also due to complexities of underlying factors involving differences in staining, illumination, instrumentation and goals.
To overcome many of these obstacles to automation, Gurcan and medical center colleagues, Dr. Gerard Lozanski and Dr. Arwa Shana'ah, turned to the Ohio Supercomputer Center.
|Contact: Mr. Jamie Abel|
Ohio Supercomputer Center