Tjalling Bosse

LUMC

In short, Tjalling Bosse's research entails the following: 

AIR-MEC: Artificial Intelligence to Refine the Molecular Endometrial Cancer Classification

'Endometrial (womb) cancer is the most common gynaecological malignancy in the Western world, affecting 100,000 women each year in Europe alone. While most women with endometrial cancers are cured by treatment, our inability to identify when surgery alone is sufficient means that many patients receive radiotherapy or chemotherapy that they did not need. For the appreciable fraction of women whose disease has spread at the time of diagnosis, or returns after surgery, the prognosis is poor and treatment options are limited.

Predicting spread or recurrence of endometrial cancer following surgery, and decisions to give additional treatments, have traditionally been based on the appearance of the cancer under a microscope. My previous work has shown that these predictions can be improved by also doing molecular (genetic) tumor testing. This helps to determine the specific type of endometrial cancer and predict how it will behave; however, such molecular testing is costly and not available everywhere.

Artificial intelligence could provide additional information to that which we currently get from molecular testing, and in hospitals where molecular testing is unavailable, serve as an alternative. In this project we will combine image analysis with molecular testing results from participants with womb cancer from the PORTEC trials. This represents an extremely powerful dataset and is one of the world’s largest collections of endometrial cancers. AIR-MEC should help us to better predict outcomes for patients depending on their specific endometrial cancer type and tailor treatments accordingly.'