In short, Liesbeth Hondelinks' fellowship entails the following:
Multi-input prognostic models for time to resistance prediction
Non-small cell lung adenocarcinoma is one of the deadliest and most common cancers globally. In the past decades, treatment with the targeted tyrosine kinase inhibitor Osimertinib has substantially improved disease-free survival in selected patients whose cancers harbor a mutation in the epithelial growth factor receptor (EGFR). However, acquired resistance to Osimertinib inevitably occurs, resulting in disease progression. In the management of Osimertinib-treated disease, swift detection of acquired resistance is paramount, in order to keep treating patients effectively. However, there is substantial variance between patients with regard to time to resistance, which complicates treatment and monitoring. An accurate prediction of time to resistance would greatly benefit Osimertinib-treated patients.
Over the course of the Hanarth Fellowship, Liesbeth Hondelink will develop a convolutional deep learning model, which combines Pathology images, Radiology data, clinical parameters and molecular sequencing data into a comprehensive prediction tool, using the newest data science technology. The ultimate aim is to predict time to resistance in Osimertinib-treated lung cancer patients, which will eventually lead to better patient management.