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Artificial Intelligence in Oncology - Supporting scientific research
In short, Karijn Suijkerbuijk's research entails the following:
'Immunotherapy with checkpoint inhibitors has importantly changed perspectives for many metastatic melanoma patients. Still, the majority of patients do not derive long-term benefit from this treatment. Currently, no single biomarker is available that can reliably predict who will and who will not respond to immunotherapy. If non-response could be predicted, patients could be spared the potential severe side effects of immunotherapy and start a more effective treatment. Aim of this study is to develop machine learning algorithms based on clinical data and histological images from the primary melanomas of 1500 immunotherapy- treated metastatic melanoma patients that can predict response to immunotherapy. The project is a collaboration between the UMC Utrecht (dr. Karijn Suijkerbuijk, dr. Willeke Blokx, prof. Paul van Diest, prof. René Eijkemans), the TU Eindhoven (dr. Mitko Veta, prof. Josien Pluim) and several Dutch melanoma treatment centers.'
Immunotherapy works for some patients with metastatic melanoma so well that they survive for years after treatment and may even be able to to cure. However, this treatment works for more than half of the patients not and at the moment we cannot predict this in advance. The purpose of this research is to see if we can use artificial intelligence (so-called machine learning) on data and microscopy images of the melanoma can predict who will or will not benefit from immunotherapy. In the first 9 months of the study, we have had almost 1000 patients who treated with immunotherapy, the (anonymous) data were collected. We hope to expand this to 1500 in the coming months In addition, we are busy with collect the microscopy images. Meanwhile we have the first steps in creating a program that automatically extracts from those microscopy images can recognize the different cells that are present in the melanoma. In the coming year we will continue to collect the data and will we are going to use this data to build an algorithm that measures the effect of immunotherapy before starting treatment.