医学影像-病理-基因融合的智能分析和诊疗应用OA

Medical imaging,pathology,and genomics respectively provide information on tumor spatial-morphological phenotypes,histopathological architecture,and molecular mechanisms.Single-modal approaches are constrained by scale discrepancies,sampling biases,and cross-center domain shifts,limiting their support for clinical decision-making.For precision oncology,imaging-pathology-genomics integration aims at semantical alignment and consistency validation among macroscopic imaging,microscopic histological,and mechanistic molecular evidence.This review systematically examines the field via fusion methodologies and clinical applications.We discuss the clinical advantages of multimodal integration,summarize key fusion paradigms,and emphasize its clinical necessity.In applications,focusing on completing the clinical evidence chain,we summarize its advantages in differential diagnosis,molecular subtyping,surgical planning,treatment response stratification,and systematic decision output,highlighting how integration turns predictive results into verifiable,actionable clinical decisions via cross-modal validation.Finally,we discuss emerging trends:spatial omics with multi-region sampling,longitudinal tumor evolution modeling,multimodal foundation models,and multi-center collaborative validation.We propose clinical translation recommendations and a utility evaluation system,offering a roadmap for next-generation intelligent multimodal systems in precision oncology.