Graph-based multimodal multi-lesion DLBCL treatment response prediction from PET images
This work addresses the challenge of identifying high-risk DLBCL patients who do not respond to standard therapy, which affects 30-40% of patients, by providing a computer-aided prediction tool that could help adapt treatments.
The paper tackled the problem of predicting treatment response in Diffuse Large B-cell Lymphoma (DLBCL) patients by developing a graph neural network method that integrates multimodal data from PET images and clinical information, achieving improved accuracy for 2-year progression-free survival classification compared to classical supervised methods.
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.