Digital Twins for Personalized Management of Gynecologic Malignancies

Abstract

Background: Gynecologic malignancies, including cancers of the ovary, cervix, endometrium, vulva, and vagina, remain a major cause of cancerrelated morbidity and mortality among women worldwide despite significant advances in screening, molecular diagnostics, targeted therapies, immunotherapy, and minimally invasive surgical techniques. The biological heterogeneity of these malignancies, coupled with diverse genomic alterations, tumor microenvironment dynamics, hormonal influences, and patient-specific clinical characteristics, presents considerable challenges for individualized treatment planning. Recent progress in artificial intelligence (AI), computational oncology, systems biology, multimodal biomedical data integration, and predictive analytics has accelerated the development of digital twins as an innovative framework for precision gynecologic oncology. A digital twin is a continuously evolving virtual representation of an individual patient that integrates clinical, radiological, pathological, molecular, genomic, physiological, and longitudinal health information to simulate disease progression, therapeutic response, treatment toxicity, and long-term outcomes. Unlike conventional predictive models that rely on static datasets, digital twins dynamically update as new patient data become available, enabling adaptive clinical decision-making throughout diagnosis, treatment, surveillance, and survivorship. Emerging technologies including deep learning, foundation models, graph neural networks, reinforcement learning, explainable AI, and generative AI have significantly enhanced the predictive capabilities of gynecologic oncology digital twins. These systems demonstrate promising applications in early diagnosis, radiogenomics, personalized surgery, fertility preservation, adaptive radiation therapy, immunotherapy prediction, and precision drug development. This review provides a comprehensive overview of the evolution, computational foundations, clinical applications, and future prospects of digital twins for personalized management of gynecologic malignancies while highlighting current challenges related to data interoperability, regulatory validation, ethical governance, and clinical implementation.