  Diffuse Large B-cell Lymphoma (DLBCL) is a lymphatic cancer involving one or
more lymph nodes and extranodal sites. Its diagnostic and follow-up rely on
Positron Emission Tomography (PET) and Computed Tomography (CT). After
diagnosis, the number of nonresponding patients to standard front-line therapy
remains significant (30-40%). This work aims to develop a computer-aided
approach to identify high-risk patients requiring adapted treatment by
efficiently exploiting all the information available for each patient,
including both clinical and image data. We propose a method based on recent
graph neural networks that combine imaging information from multiple lesions,
and a cross-attention module to integrate different data modalities
efficiently. The model is trained and evaluated on a private prospective
multicentric dataset of 583 patients. Experimental results show that our
proposed method outperforms classical supervised methods based on either
clinical, imaging or both clinical and imaging data for the 2-year
progression-free survival (PFS) classification accuracy.
