Graph-based multimodal multi-lesion DLBCL treatment response prediction from PET images

• URL: http://arxiv.org/abs/2310.16863v1
• Date: Wed, 25 Oct 2023 08:16:45 GMT
• Title: Graph-based multimodal multi-lesion DLBCL treatment response prediction from PET images
• Authors: Oriane Thiery (LS2N, LS2N - \'equipe SIMS, CFE, Nantes Univ - ECN, Nantes Univ), Mira Rizkallah (LS2N, LS2N - \'equipe SIMS, CFE, Nantes Univ - ECN, Nantes Univ), Cl\'ement Bailly (CFE, IT, CRCI2NA, Nantes Univ), Caroline Bodet-Milin (CFE, IT, CRCI2NA, Nantes Univ), Emmanuel Itti, Ren\'e-Olivier Casasnovas, Steven Le Gouill (CFE, IT, CRCI2NA, Nantes Univ), Thomas Carlier (CFE, IT, CRCI2NA, Nantes Univ), Diana Mateus (LS2N - \'equipe SIMS, LS2N, CFE, Nantes Univ - ECN, Nantes Univ)
• Abstract summary: 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. We propose a method based on recent graph neural networks that combine imaging information from multiple lesions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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