In short, Marianne Jonkers' research entails the following:
Improved Transparent AI methods for personalized prediction based on data from rare cancers
The prognosis of patients with rare cancers is worse than that of patients with common cancers. An important reason is that for rare cancers it is more difficult to identify potential treatments, because only small data sets will typically be available for research. When using such small data sets to identify predictive factors, the risk of overfitting in the models used is high. This limits severely our ability to infer statistically significant patterns in these data, which might have suggested novel treatments. Pooling data from different institutions could alleviate the situation, but is in practice challenging due to regulatory and logistical problems.
Two complementary routes for confronting the challenges of small data sets for rare cancers are proposed. The first is to focus on more powerful techniques for inference that are better able to cope with small sample sizes, without overfitting. The second route is to design and improve machine learning algorithms that circumvent the need for data pooling at one location by `cycling’ around medical institutes with small data sets (federated learning). Data on salivary gland cancer will be analyzed with the proposed methods.
The project is a collaboration between: dr. Marianne Jonker, prof. Ton Coolen, prof. Kit Roes and prof. Carla van Herpen, all at the Radboud university medical center or Radboud university.