Computational and Imaging-Based Approaches to Tumor Pathophysiology: Characterizing Heterogeneity, Microenvironment, and Treatment Response

Quantitative imaging, radiomics, machine learning, and multimodal data integration are expanding our ability to characterize pathophysiological states across tissues, organs, and disease settings. When linked to clinical, molecular, pathological, or functional information, these approaches can move beyond descriptive classification to generate biologically meaningful markers of tissue state, disease heterogeneity, and response to intervention. This is particularly relevant in oncology, where computational and imaging-based strategies can help define tumor behavior, microenvironmental features, and treatment-associated changes, while remaining grounded in pathophysiological principles applicable to other disease contexts.This Research Topic aims to bring together studies that use computational and imaging-based approaches to characterize pathophysiological states in disease, with particular emphasis on oncology. The collection will highlight work that combines data-driven analysis with biological and physiological interpretation, with interest in translational studies that bridge imaging, computational methods, and complementary molecular or clinical data. Contributions from non-oncology settings are welcome where they share the same methodological framework and connect imaging or computational outputs to meaningful pathophysiological characterization.We welcome original research, reviews, methods, mini-reviews, perspectives, and technology-focused contributions addressing:1. Computational and imaging-based characterization of pathophysiological states, including machine learning approaches linked to tissue function or disease biology.2. Quantitative imaging, radiomics, and image-derived phenotyping with clear biological or pathophysiological interpretation.3. Integration of imaging with pathology, clinical variables, molecular data, or multi-omics for disease characterization.4. Computational models that use imaging or multimodal data to support disease stratification, state monitoring, or response assessment, with appropriate validation.5. Oncology-focused studies that define biologically meaningful states such as tumor heterogeneity, tumor–host interactions, microenvironmental dynamics, or treatment-related change from a pathophysiological perspective. Topic editors: Xuhui Fan, Wenxin Chen, Yihui Wang Source: https://www.frontiersin.org/research-topics/80515/computational-and-imaging-based-approaches-to-tumor-pathophysiology-characterizing-heterogeneity-microenvironment-and-treatment-response