12-Month Disease-Free Survival Prediction in Diffuse Large B-Cell Lymphoma: A Multimodal Transfer Learning Innovation.

Early relapse of diffuse large B-cell lymphoma (DLBCL) within 12 months of diagnosis confers a poor prognosis, underscoring the critical need for accurate pretreatment prediction. Current approaches relying on radiomics and clinical indicators are limited by scarce medical imaging data, time-consuming manual feature extraction (susceptible to human subjectivity), and insufficient use of multimodal deep learning frameworks. To address these challenges, we propose MM-DLBCL, a novel multimodal transfer learning framework for pretreatment 12-month disease-free survival (DFS12) prediction. It leverages the biomedical pre-trained PMC-CLIP model to mitigate small-sample constraints, uses pretreatment FDG-PET/CT scans to generate coronal and sagittal Maximum Intensity Projection (MIP) images (focusing on metabolically active lesions), and converts key clinical covariates (e.g., International Prognostic Index (IPI) score, disease stage, serum lactate dehydrogenase (LDH) levels) into structured text embeddings. The architecture integrates an enhanced ResNet50 visual encoder (for dual-plane MIP feature extraction), a PubMedBERT text encoder (for clinical data processing), a Medical-Statistical Feature Fusion (MSFF) module (to quantify tumor heterogeneity via higher-order statistics), a Multi-Scaled Cooperative Attention (MSCA) module (to align imaging and clinical features), and a Cross-Modal Contrastive Head (CMCH) module (to optimize discriminative power via triple contrastive loss). Trained and validated on a retrospective cohort of 338 DLBCL adult patients, MM-DLBCL achieved an end-to-end AUC of 0.8316 ± 0.06 and accuracy of 0.8044 ± 0.05 for DFS12 prediction, demonstrating significant potential for multimodal transfer learning in automated DLBCL disease-free survival risk stratification.