Deep Learning–Driven Integration of Radiological and Genomic Data for Precision Cancer Care

Abstract

Precision oncology aims to deliver personalized cancer diagnosis, prognosis, and treatment by integrating the molecular and phenotypic characteristics of individual tumors. Rapid advances in high-throughput genomic sequencing and medical imaging technologies have generated vast volumes of heterogeneous data, offering unprecedented opportunities to characterize tumor biology comprehensively. Radiological imaging provides non-invasive insights into tumor morphology, spatial heterogeneity, and therapeutic response, while genomic profiling reveals the molecular alterations underlying tumor initiation, progression, metastasis, and treatment resistance. However, conventional analytical methods are often limited in their ability to capture the complex, nonlinear relationships that exist across these complementary data modalities. Multimodal deep learning has emerged as a transformative computational framework for integrating radiological and genomic information, enabling more comprehensive and accurate clinical decision-making in precision oncology. Leveraging advanced neural network architectures—including convolutional neural networks, transformers, graph neural networks, and multimodal fusion models—these approaches can identify latent associations between imaging phenotypes and genomic signatures that are not readily discernible through traditional analyses. Recent studies have demonstrated that multimodal integration consistently outperforms unimodal models across a range of applications, including cancer detection, molecular subtyping, prognostic prediction, treatment response assessment, biomarker discovery, and patient risk stratification. Despite these promising advances, several challenges continue to hinder widespread clinical adoption. Limited availability of large, well-annotated multimodal datasets, data heterogeneity across institutions, model interpretability, privacy and security concerns, computational complexity, and regulatory considerations remain significant barriers to implementation. At the same time, the emergence of foundation models, self-supervised learning, and large-scale multimodal pretraining is reshaping the field by enabling robust representation learning from extensive unlabeled medical datasets and improving model generalizability across diverse clinical settings. This review provides a comprehensive overview of recent advances in multimodal deep learning for integrating radiological imaging and genomic data in precision oncology. It discusses key methodological developments, radiogenomic integration strategies, multimodal fusion architectures, clinical applications, current limitations, and emerging trends, with particular emphasis on foundation models and next-generation artificial intelligence frameworks that have the potential to accelerate the translation of precision oncology into routine clinical practice.