Adenomyosis and Uterine Fibroids: Two Distinct Yet Overlapping Clinical Entities
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Abstract
Adenomyosis and uterine fibroids are among the most common benign uterine disorders in women of reproductive age and represent major causes of abnormal uterine bleeding, pelvic pain, and infertility. Despite their distinct histopathological features, they frequently coexist and share several hormonal and molecular mechanisms, making diagnosis and treatment challenging. This narrative review examines the current evidence on their epidemiology, pathophysiology, clinical presentation, imaging characteristics, reproductive impact, and therapeutic management, with particular attention to the role of imaging in the differential diagnosis and management of coexisting disease. A non-systematic narrative review of the literature was conducted using PubMed/MEDLINE and Scopus. Relevant original studies, systematic reviews, meta-analyses, consensus statements, and clinical guidelines addressing adenomyosis, uterine fibroids, imaging, fertility, and treatment were considered. Additional references were identified through manual screening of the bibliographies of the retrieved articles. Although adenomyosis and uterine fibroids differ in their biological origin and morphological appearance, they commonly present with overlapping symptoms, including heavy menstrual bleeding, dysmenorrhea, chronic pelvic pain, and reproductive dysfunction. Transvaginal ultrasound remains the first-line imaging modality, whereas magnetic resonance imaging is particularly valuable in complex cases, lesion characterization, and preoperative assessment. Accurate imaging phenotyping may help distinguish isolated from coexisting disease and support individualized medical, surgical, or minimally invasive treatment planning. A structured imaging-based approach integrated with clinical findings and reproductive goals may improve disease characterization and support individualized management of adenomyosis and uterine fibroids. Further standardization of imaging criteria and validation of emerging diagnostic technologies may improve patient stratification and treatment selection.
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