Project: Improved treatment stratification for Hodgkin lymphoma patients through the use of deep learning, molecular imaging and relevant clinical data

Acronym HOLY-2020 (Reference Number: ID49)
Project Topic We aim to validate established algorithms using retrospective FDG PET/CT image data, clinical data and machine learning (ML)/deep learning (DL) for treatment stratification in early stage Hodgkin lymphoma (HL). The results of this project will be the basis for prospective studies, which will pave the way for customized therapies with fewer side effects and a better quality of life for the patients. Despite of FDG PET/CT being already an integral part of initial staging and treatment monitoring in lymphoma patients, standard prognostic scores fail to reliably stratify patients. In particular, the Deauville score fails to identify patients who do not need intensified treatment with detrimental late side effects in a predominantly adolescent patient population. It is agreed that not only malignant lesions, but the entire organism must be investigated in a systemic way in order to correctly characterize a disease. ML and radiomics models have proven potential in prognostic stratification in lymphoma. However, they mostly analyze the main site of lymphoma-involvement only, without accounting for additional factors such as features associated with immune response to the disease or disease-induced imaging features throughout the body. Given the complex and highly variable distribution of lesions throughout the body of HL patients, and the variety of imaging-based, biological and clinical prognostic factors on hand, this disease is a model of choice to develop a systems medicine approach. This consortium seeks to effectively combine already proven prognostic factors including metabolic features from state-of-the-art FDG PET/CT imaging with clinical data such as genetic subtype, parameters included in the International Prognostic Score, and patient characteristics in order to validate a clinical decision support system that is capable of stratifying early stage HL with high accuracy.
Network ERAcoSysMed
Call 3rd Joint Transnational Call for European Research Projects on Systems Medicine

Project partner

Number Name Role Country
1 Medical University of Vienna Coordinator Austria
2 Hospital Sant Pau Partner Spain
3 Inserm Partner France