DEEP LEARNING FOR CROSS-MODALITY MAPPING BETWEEN HISTOPATHOLOGY AND RADIOLOGICAL IMAGING

Abstract

Cross-modality mapping between histopathology and radiological imaging represents a critical challenge in precision medicine, bridging microscopic tissue analysis with macroscopic anatomical visualization. This research develops deep learning frameworks that establish bidirectional mappings between whole-slide histopathology images and corresponding radiological scans, enabling virtual biopsy prediction and histology-guided image interpretation. We collected paired data from 487 cancer patients including digitized histopathology slides and preoperative CT/MRI scans across lung, breast, liver, and brain tumors. Our approach employs generative adversarial networks and vision transformers to learn complex nonlinear mappings between imaging modalities operating at vastly different spatial scales and information content. The model achieved 0.84 structural similarity index and 0.78 Pearson correlation between predicted and actual histopathology features when generating virtual histology from radiology images. Conversely, radiological feature prediction from histopathology reached 0.81 SSIM, enabling radiologists to understand which microscopic tissue characteristics produce specific imaging appearances. Clinical validation demonstrated that virtual histology predictions accurately identified tumor grade in 82% of cases and histological subtype in 76% of cases, approaching the 89% and 84% accuracy of actual tissue sampling. The framework discovered interpretable cross-modality relationships including correlations between CT attenuation patterns and tissue cellularity (r=0.72), MRI signal characteristics and nuclear pleomorphism (r=0.68), and enhancement kinetics and microvessel density (r=0.71). These findings demonstrate that deep learning can bridge the resolution gap between radiology and pathology, enabling non-invasive tissue characterization and improving understanding of radiological-pathological correlations.