Artificial Intelligence Predicts Metastatic Risk in Skin Cancers

UT Southwestern Medical Center researchers have discovered a method to predict which skin cancers are highly metastatic with the help of artificial intelligence. The study in Cell Systems examined how AI-based tools could revolutionize pathology for cancer as well as other diseases.

“We now have a general framework that allows us to take tissue samples and predict mechanisms inside cells that drive disease, mechanisms that are currently inaccessible in any other way,” study leader and Professor and Chair of the Lyda Hill Department of Bioinformatics at UTSW Gaudenz Danuser, PhD, said in a press release.

AI technology has significantly advanced over the last several years. According to Danuser, with deep learning-based methods, AI technology can distinguish differences in images invisible to the human eye.

Researchers have recommended using AI technology to look for differences in disease characteristics to offer insight into diagnoses or guide treatment plans. However, Danuser said, the differences distinguished by AI are typically not interpretable in terms of specific cellular characters, making AI difficult for clinical use.

To address this challenge, Danuser and his team used AI to search for differences in images of melanoma cells with high and low metastatic potential. The team then reverse-engineered the AI findings to discover which features in the image were responsible for the differences.

Using tumor samples from seven patients and available information on their disease progression, including metastasis, the researchers filmed a video of about 12,000 random cells living in petri dishes, which generated around 1,700,000 raw images. The research team then used an AI algorithm to find 56 different abstract numerical features from the images.

The researchers found one feature that accurately differentiated between cells with high and low metastatic potential. By manipulating the abstract numerical feature, the researchers created artificial images that exaggerated visible characteristics inherent to metastasis undetectable by the human eye.

The highly metastatic cells created slightly more pseudopodia extensions and had an increased light scatting, an effect that may be due to subtle rearrangements of cellular organelles.

“To further prove the utility of this tool, the researchers first classified the metastatic potential of cells from human melanomas that had been frozen and cultured in petri dishes for 30 years, and then implanted them into mice. Those predicted to be highly metastatic formed tumors that readily spread throughout the animals, while those predicted to have low metastatic potential spread little or not at all,” the press release stated.

Danuser said that this method needs further examination before it can be incorporated into clinical care. However, Danuser added that it might be possible to use AI to differentiate between important features of cancer and other diseases.