Accurate disease diagnosis depends on effective collaboration between medical specialties, yet departments often use distinct data systems and proprietary formats. This heterogeneity hinders joint analysis and integration of complementary diagnostic information. The use of separate viewers for each modality further restricts cross-specialty collaboration. Although multimodal integration, particularly between radiology and pathology, has demonstrated potential for identifying novel biomarkers, it still relies heavily on manual, time-consuming data pairing. This project introduces an interdisciplinary toolbox that can operate within the Kaapana framework or as a standalone tool to bridge radiology and pathology. By linking modality-specific viewers and extending them with automated image registration and alignment, the platform enables efficient, scalable multimodal analysis. The integrated environment promotes reproducible workflows, accelerates cross-disciplinary research, and facilitates deeper insights into disease mechanisms and patient care. The method will be integrated in the Kaapana toolkit and as standalone package https://github.com/MIC-DKFZ/combinedmodalityviewer.