Advancing equity in endometrial cancer: A narrative synthesis using a cluster-informed framework for...

Endometrial cancer incidence and mortality are rising globally, disproportionately affecting health systems facing diagnostic, therapeutic, and survivorship constraints. Rapid innovations-including the International Federation of Gynecology and Obstetrics (FIGO) 2023 staging, molecular classification, sentinel lymph node (SLN) mapping, evolving adjuvant strategies, immunotherapy, and digital tools-risk exacerbating inequities if implementation mismatches system readiness. This narrative review synthesizes key trials, international guidelines, and high-impact implementation studies published between 2010 and September 2025. We summarize contemporary evidence across staging, molecular pathology, imaging, surgery, radiotherapy, systemic therapy, survivorship, equity, and artificial intelligence (AI), translating this into a pragmatic, resource-stratified framework. A previously published principal-component-based structural-readiness clustering model encompassing 68 countries is applied-but not re-derived-to illustrate how health-system capacity shapes access to diagnostics, treatment, and innovation. Persistent gaps include limited availability of universal mismatch repair (MMR) and p53 immunohistochemistry, variable adoption of standardized SLN mapping, uneven radiotherapy access, restricted use of immunotherapy for mismatch-repair deficient (dMMR)/microsatellite instability high (MSI-H) tumors, and fragmented survivorship care. A minimum-core-optimal implementation ladder is proposed to guide diagnostic, surgical, radiotherapeutic, systemic-therapy, and survivorship priorities across varying resource levels. AI-supported quality assurance is discussed alongside essential requirements of local validation, bias mitigation, and robust governance. Rather than generating new empirical data, this review employs a cluster-informed, equity-oriented lens, applying previously validated system-level typologies to contextualize implementation gaps and support context-sensitive guideline adaptation.