Big data-driven intelligent analysis of tumor pathology: a diagnostic and therapeutic bridge across morphology and multi-omics

This paper systematically summarizes the development context, key technologies, and clinical translation applications of big data and artificial intelligence (AI) in intelligent analysis systems for tumor pathology. With the maturation and popularization of whole slide imaging (WSI) technology, tumor pathology analysis is undergoing a transformation from traditional digital pathology (DP) to big data-driven AI-based computational pathology (CP), providing critical technical support for precision oncology. Technically, this transformation is reflected in the architectural upgrade from early convolutional neural network (CNN) to vision transformer (ViT) with global receptive field and foundation models (FMs). Meanwhile, weakly supervised learning (WSL) and self-supervised learning (SSL) effectively break through the bottleneck of scarce high-quality annotated data. Clinically, AI has been widely applied in globally prevalent cancers such as lung cancer and breast cancer. It not only addresses inter-observer variability through automated grading but also demonstrates considerable potential in identifying subvisual features that are undetectable by human eyes. The introduction of cutting-edge generative AI technologies such as multimodal fusion and virtual staining is further breaking down the barriers between morpho-logy, genomics and transcriptomics, providing more comprehensive decision support for tumor diagnosis. Although challenges such as domain shift, interpretability, and regulatory and ethical concerns remain to be addressed, establishing a human-machine collaborative intelligent diagnosis and treatment model is becoming a key evolutionary direction for improving diagnostic efficiency and realizing precision medicine, building a core technical bridge for tumor diagnosis and treatment across morphology and multi-omics.