ReviewJapanese journal of radiology2026
Updates on molecular classification and treatment in endometrial cancer.
Review in Japanese journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
Endometrial cancer (EC) is the most common gynecological malignancy in developed countries, with a steadily rising global incidence. The Cancer Genome Atlas (TCGA) study identified four molecular subtypes- POLE (ultramutated), MSI (hypermutated), copy number low, and copy number high-each with distinct prognostic and therapeutic implications. Integration of molecular classification into the WHO 2020 classification and the FIGO 2023 staging system has transformed clinical practice. Although major European ESGO/ESTRO/ESP guidelines now incorporate molecular subtyping into treatment algorithms, the Japanese JSGO 2023 guidelines have not yet formally integrated molecular classification into adjuvant treatment decisions. Notable differences exist between Japanese and Western approaches, particularly regarding the role of adjuvant radiation therapy versus chemotherapy. Recent landmark trials have provided compelling evidence for biomarker-driven treatment based on molecular classification. Furthermore, some immune checkpoint inhibitors (ICIs) and antibody-drug conjugates (ADCs) are emerging as promising therapeutic options. This review provides an updated overview of the molecular classification of EC, its impact on current guidelines with emphasis on radiation therapy, and the latest therapeutic advances including immunotherapy and ADCs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.