You want to generalize the "ikarus" approach so that it can distinguish all possible cell types in a biopsy, not just tumor cells. What applications would be conceivable then?
Dr. Akalin: Our project aims to go far beyond the classification of "healthy" versus "cancerous" cells. In initial tests, ikarus already demonstrated that the method can also distinguish other types (and certain subtypes) of cells from tumor cells. We want to make the approach more comprehensive by developing it further so that it can distinguish between all possible cell types in a biopsy.
In hospitals, pathologists tend only to examine tissue samples of tumors under the microscope in order to identify the various cell types. It is laborious, time-consuming work. With ikarus, this step could one day become a fully automated process. Furthermore, the data could be used to draw conclusions about the tumor’s immediate environment. And that could help doctors to choose the best therapy, for the makeup of the cancerous tissue and the microenvironment often indicate whether a certain treatment or medication will be effective or not.
How is Big Data changing medicine right now?
Dr. Akalin: More diagnostic approaches are initially mined from big datasets. Recently, more and more companies are using large datasets on topics ranging from target identification to drug discovery. New drugs and diagnostics are more likely to be developed through analysis of large data sets.