In short, Bart de Keizer's research entails the following:
Artificial intelligence-based phenotyping of metastatic renal cell carcinoma to better understand (pseudo)progression under immune checkpoint inhibition
This project aims to determine if there are differences in the way that metastatic renal cell carcinoma (cancer originating from the kidneys that has spread to lymph nodes and/or other organs) appears on PET/CT imaging scans between cases where the cancer has truly progressed and cases where it appears to have progressed but is actually responding to treatment (called pseudo progression). If such differences do exist, it may be possible in the future to switch patients from one type of treatment (called immune checkpoint inhibitors) to another type (called targeted therapy) earlier, in order to stop the cancer from progressing further while also reducing unnecessary side effects.
Immune checkpoint inhibitors are often continued when the cancer appears to be growing because of the possibility of pseudo progression, but these treatments can cause severe side effects in some patients. If a way can be found to distinguish between true progression and pseudo progression using imaging scans, it could help doctors decide whether to continue immune checkpoint inhibitors or switch to targeted therapy, potentially prolonging the time before the cancer progresses and reducing side effects.
Currently, the distinction between true progression and pseudo progression is made on the basis of the immune Response Evaluation Criteria in Solid Tumors (iRECIST), which involves taking multiple imaging scans over a period of time to see if the cancer responds to treatment. However, this process can take several weeks or even years.
To find faster and more objective response characteristics early after start of treatment we will use machine learning (deep learning and radiomics), combined with explainable artificial intelligence.
The project a collaboration between the departments of radiology, Image Sciences Institute, and medical oncology at UMC Utrecht. In addition, three other university medical centers participate as well.