A Genomics-Guided Multimodal Contrastive Learning Framework for Clinically Significant Prostate Cancer Risk Stratification with Missing Clinical Data.

backgroundHeterogeneous data integration remains a major challenge in intelligent information systems, particularly under missing-modality and cross-domain conditions. Existing multimodal fusion approaches often rely on complete datasets and weak alignment mechanisms, limiting their robustness and practical applicability.

objectivesThis study aims to develop and evaluate a genomics-guided multimodal representation learning framework that enables robust heterogeneous data fusion, reliable cross-modal correspondence, and accurate prediction under incomplete-data conditions.

methodsWe propose a multimodal learning architecture that models genomics as the primary biological anchor and learns conditional projections to imaging modalities, including multiparametric MRI and whole-slide histopathology (WSI). The framework formulates multimodal fusion as a genomics-guided contrastive learning problem, incorporates domain-specific optimization constraints, and learns a latent shared-state representation to support inference without requiring fully paired datasets. Evaluation was conducted using public datasets, including TCGA-PRAD and TCIA, across low-risk versus higher-risk/clinically significant prostate cancer (csPCa) discrimination, Gleason-based risk stratification, and clinically significant outcome prediction tasks under realistic multimodal and missing-modality scenarios.

resultsIn the adequately powered Genomics+WSI cohort (

conclusionsThe proposed framework provides a scalable and generalizable solution for heterogeneous multimodal data fusion, supporting reliable prediction, robustness to missing modalities, and applicability to complex information systems beyond the studied domain.