A Jackson Laboratory research team is boosting the speed and accuracy of tumor sample image analysis, for multiple cancer types, offering the potential for more targeted treatment direction without additional testing.
Pathologist Todd Sheridan describes his job this way: “I look through a microscope to evaluate slides containing tissue from a biopsy or resection. Usually this is a histopathology image that we call H&E because the sample is treated with two histological stains, hematoxylin and eosin.” Pathologists are trained to recognize cancer cells, he says, “and we have a number of techniques to improve our diagnostic accuracy such as immunochemistry, which uses antibodies to confirm the presence of certain markers on the surface of cancer cells.”
But, Sheridan says, pathology has lagged behind radiology and some other medical fields in deploying advanced computer image analysis to improve tumor sample interpretation. Sheridan holds joint appointments with Hartford Pathology Associates at Hartford Hospital in Connecticut and The Jackson Laboratory for Genomic Medicine in Farmington, where he works with Professor Jeffrey Chuang on a project to harness the power of machine learning for pathology.
“If we can provide pathologists with tools to interpret tumor images faster and more precisely,” Chuang says, “cancer patients will benefit from more targeted and effective treatment approaches.”
Read the full article at the Jackson Laboratory website here.