The clinical aim is to improve treatment response prediction of patients suffering from DLBCL based on information provided by [18]FDG-PET in combination with clinical data. As 40% of the patients do not respond to standard chemotherapy, there is an urgent need to better predict treatment outcome to avoid futile treatments and severe side effects without clinical benefit.


  • Investigating different machine learning models and apply them to predict treatment response based on radiomics data obtained from [18]FDG-PET scans.

  • Designing and training convolutional neural networks to automatically segment lesions from the scans and exploring the impact of using different loss functions

  • Combining the previously designed CNN segmentation model with the machine learning predictive model to generate a fully automate treatment prediction pipeline.

  • Developing a novel CNN which willl extract the predictive information directly from the scans. The robustness of this AI model will be tested using a test-retest study and different reconstruction settings to assess its clinical applicability.