Breast and gynecologic cancers consist of two groups of complex solid tumors, each with unique genomic features, immune microenvironments, and treatment responses. Recent advances in next-generation sequencing, spatial profiling, and digital pathology have transformed diagnostic methods, enabling seamless integration of morphological and molecular data. Artificial intelligence (AI) and machine learning (ML) are now essential tools for linking histomorphology, immunophenotype, and molecular alterations in ways that were previously unachievable. This review discusses recent progress in integrating digital and molecular pathology for these cancers, with an emphasis on practical clinical applications. We highlight emerging research in breast, endometrial, ovarian, and cervical cancers, where combined image-based and molecular approaches can predict treatment response and survival. Additionally, spatial transcriptomics and proteomics are deepening our understanding of tumor heterogeneity and the interactions between tumor cells, stroma, and immune cells that drive disease progression. We also address current challenges, such as standardization, reproducibility, regulation, and workflow integration, and propose priorities to facilitate the clinical adoption of multimodal data.
Polit et al. (Fri,) studied this question.