Lisanne van Dijk

UMC Groningen

In short, Lisanne van Dijk's research entails the following: 

Deep learning to Enhance Therapeutic Outcomes and toXicity for LUNG cancer patients (DETOX-LUNG)   

'Radiotherapy is an important treatment for lung cancer patients, yet they are often faced with severe toxicities due to radiation damage. These toxicities do not only have a big impact on the quality-of-life of patients, but can also have a negative impact on their prognosis. Adequate prediction of treatment response is needed to prevent severe toxicities and (related) fatalities by personalized treatment optimization.

This project aims to improve the lung cancer radiotherapy outcome prediction with Artificial Intelligence. Artificial Intelligence has the capacity to deal with three-dimensional radiation dose admitted to the patient for the prediction of toxicities, instead of the current models than can only deal with an average radiation dose value to a single organ. This can be combines with the patient-specific characteristics from medical images and clinical data.' 

Brief summary of progress / results March 2024

For training the Deep Learning model, a large amount of patient data is needed. This collection and verification of the data are essential for all subsequent steps of the research project. Over the past months, data collection and processing have taken place using clinical and toxicity data from RedCap, as well as DICOM files containing CT scans, radiation dose distribution, and segmentation data of the tumor and critical organs. In total, a dataset of 1107 patients has been obtained for predicting radiation pneumonitis (WP1) and overall survival (WP2). Currently, we are in the process of developing the reference, radiomics, and deep learning models. The collection of PET images (WP3) has been started in parallel. Noteworthy, we have some delay, due to complications in the hiring process.