Johannes Textor

Radboud UMC

Kort samengevat, behelst het onderzoek van Johannes Textor het volgende: 

Transfer Learning for Immune Cell Landscape Analysis in Salivary Gland Cancer

'Salivary gland cancer is a rare type of head-and-neck cancer with 150-200 diagnoses per year in the Netherlands, and the most aggressive subtypes have poor prognosis. To develop new treatment options, we are imaging the interactions between immune system cells and tumor cells within patient biopsies using high-resolution digital microscopy. Machine learning approaches are the state of the art for analyzing such data, but they can require very large datasets to train on, which are usually not available for rare cancer types. In our project, we will address this problem using "transfer learning" methodology that allows machine learning algorithms to benefit from experience gained on larger datasets from more common cancer types and train more effectively on smaller datasets. Leveraging existing data and knowledge in this manner, we hope that our project will help to build a rationale for future immunotherapy treatments for salivary gland caner patients.'

Korte samenvatting van voortgang / resultaten 2022

In samenwerking met onze partners in Japan hebben wij meerdere honderd stukjes weefsel van Nederlandse en Japanse speekselklierkankerpatiënten kunnen verzamelen. Wij analyseren de aanwezige immuuncellen in deze monsters nu met behulp van kunstmatige intelligentie om meer over de interacties tussen tumorcellen en afweercellen te weten te komen. Deze kennis is belangrijk voor het ontwikkelen van gerichte immuuntherapien voor speekselklierkanker.