Advances in predicting omics profiles from imaging data.

Abstract

While traditional imaging techniques, such as histopathology, are often part of clinical workflows, molecular profiling remains more difficult to conduct and is less cost-effective. Thus, the prediction of molecular 'omics' data directly from imaging has emerged as an appealing alternative. While existing reviews have mentioned image-based prediction of biomarkers within specific disease contexts, this review provides a comprehensive overview of current methods that leverage imaging to predict (i) DNA-based aberrations, (ii) bulk transcriptomic profiles, (iii) single-cell transcriptomics, and (iv) spatial transcriptomics across disease contexts and imaging modalities. To address the complexity of these predictive tasks, we find that many studies employ cutting-edge deep learning strategies for image processing, feature extraction, feature aggregation, and downstream molecular prediction. In this review, we highlight the diverse applications of both deep learning-based and modern statistical frameworks designed for image-based omics prediction. The insights gleaned from these inferred molecular data have broad clinical relevance and will continue to improve our understanding of the relationships between molecular and visual features, paving the way for new diagnostic and therapeutic applications.