Combination of Imaging and Machine Learning Can Predict Melanoma Prognosis

An AI neural network can accurately predict the prognosis of melanoma patients based on pre-treatment histology imaging data, shows research led by the NYU Grossman School of Medicine. Immune checkpoint inhibitors have revolutionized melanoma treatment, but only some tumors respond well to them and they can be quite toxic to patients. Having a more reliable way to predict who is most likely to respond to these therapies is therefore crucial. "An unmet need is the ability to accurately predict which tumors will respond to which therapy," says Iman Osman, M.D., a medical oncologist based at New York University (NYU) Grossman School of Medicine and NYU Langone's Perlmutter Cancer Center, who co-led the work. "This would enable personalized treatment strategies that maximize the potential for clinical benefit and minimize exposure to unnecessary toxicity." In collaboration with Aristotelis Tsirigos, Ph.D., professor in the Institute for Computational Medicine at NYU Grossman School of Medicine and member of NYU Langone's Perlmutter Cancer Center, Osman and team first trained an artificial neural network using pre-treatment histology images from 121 patients with metastatic melanoma.