Tumors display genomic and phenotypic heterogeneity, which holds prognostic significance and may influence therapy response. Radiographic imaging modalities, such as computed tomography, magnetic resonance imaging, nuclear medicine techniques, and ultrasonography, are routinely used to generate parametric maps to identify, measure, and map tumor heterogeneity from different perspectives encompassing anatomy, physiology, and metabolism. This review underscores the potential of artificial intelligence (AI)-based habitat imaging analysis, referred to as Radiomics++, in decoding intratumor heterogeneity compared to conventional radiomics. We highlight the general workflow, underlying principles, detailed methodology, and clinical applications of habitat imaging analysis to guide researchers. Validation advancements are then reviewed to verify the reliability of generated habitats by correlating radiologic phenotypes with biologic underpinnings. Furthermore, we address key challenges and opportunities in clinical translation, including data heterogeneity, model performance, and interpretability. Finally, integrating AI-defined habitats with multi-omics is anticipated to deepen our understanding of tumor evolution and advance precision medicine.
Wu et al. (Tue,) studied this question.