A research team, headed by Dr. Assaf Zaritsky, from BGU’s Department of Software and Information Systems Engineering, has developed a new method, based on artificial intelligence, aimed at identifying melanoma cells that are likely to metastasize.
The method, called “quantitative live cell histology,” was presented at the American Society for Cell Biology/EMBO conference in San Diego in December, 2018 by BGU’s Dr. Zaritsky and Gaudenz Danuser of the University of Texas Southwestern Medical Center (UTSW) at Dallas, who teamed up to work on the project.
According to BGU, the method involves filming live cancer cells with microscopic cameras and using artificial intelligence to analyze the video sequence in order to identify the appearance and behavioral patterns of the cells that are associated with metastatic potential, meaning that they could spread to other parts of the body.
The method was first developed as part of Dr. Zaritsky’s postdoctoral studies at UTSW. “Beyond metastasis potential, the computer models also allowed us to distinguish between cancer cells taken from different patients by quantifying factors that are not visible to the naked human eye,” says Dr. Zaritsky. “In addition we found that different melanoma cell lines are much more similar to one another than to tumor cells taken from different patients that have not undergone prolonged culturing outside the human body.”
According to Dr. Zaritsky, the source of the phenomenon is natural selection due to the artificial process of transforming patient-derived tumor cells to cell lines. However, the researchers fear that the use of the method as a functional model for melanoma may be “clinically irrelevant.”
Skin cancer is an abnormal growth of cells in the skin that mostly develops on areas of the skin that are exposed to sun rays. The disease affects people of all colors and races. If diagnosed and treated early, skin cancer is one of the easiest forms of cancer to cure. When allowed to progress, it can result in disfigurement or death.
Quelle: American Associates, Ben-Gurion University of the Negev