Metastatic melanoma used to have a very poor prognosis, which has improved since the introduction of immunotherapy (immune checkpoint inhibition; ICI) and targeted therapy. Although some patients attain long-term disease control and survival with ICI, about 50% of patients do not respond. As the median time to response is 2-5 months, considering the possibility of late responses and pseudo-progression, most patients have been treated for over 5 months before being identified as a non-responder, putting them at risk for severe toxicity. Furthermore, for nonresponders, alternative treatment could have been initiated earlier, possibly with better response changes. Lastly, ICI are expensive, with ~50,000 Euro per patient for 5 months treatment. Therefore, a reliable and affordable personalized response prediction algorithm could reduce costs, optimize treatment efficacy and reduce unnecessary side effects..

In the PREMIUM project we aim to predict non-response to ICI by applying machine learning techniques to clinical and genomic data and pathology and CT scan images from 1500 immunotherapy treated melanoma patients. These data and images are routinely acquired during standard of care for every patient. A joint neural network based on both types of data (clinicopathological data and histological and CT images) is developed and trained to evaluate the predictive performance of combining these different data sources.