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Treatment selection for adenomyosis based on imaging findings and patient characteristics
1Department of Obstetrics and Gynecology, Nara Medical University, Kashihara, Japan.
Abstract:
Adenomyosis is an estrogen-dependent disorder characterized by substantial heterogeneity in lesion distribution, symptom presentation, disease severity, and reproductive impact, which limits the effectiveness of uniform treatment strategies. To summarize recent evidence on pharmacological and minimally invasive treatments for adenomyosis and to propose a clinically practical framework for treatment selection based on magnetic resonance imaging (MRI)-based lesion subtypes, symptom phenotypes, disease severity, and reproductive intentions. A narrative review of recent literature was conducted focusing on hormonal therapies, gonadotropin-releasing hormone (GnRH) antagonists, the levonorgestrel-releasing intrauterine system (LNG-IUS), dienogest, and minimally invasive treatments such as high-intensity focused ultrasound (HIFU) and endometrial ablation. Outcomes were evaluated in relation to MRI-based lesion localization, predominant symptoms, uterine size, and fertility preservation goals. Pharmacological therapy remains the cornerstone of conservative management. Sequential strategies using short-term GnRH antagonists followed by maintenance therapy have been explored, although robust evidence is limited. LNG-IUS and dienogest are commonly selected according to bleeding- or pain-dominant symptoms and MRI-based lesion subtypes, but interindividual variability in treatment response persists. Minimally invasive treatments show favorable outcomes mainly in localized disease, whereas efficacy in diffuse adenomyosis remains inconsistent. Treatment strategies differ according to reproductive intentions, with time-limited interventions being central in fertility-preserving management. Treatment of adenomyosis should move beyond a uniform approach toward individualized strategies integrating imaging findings and patient characteristics. An MRI-based framework may support more rational and effective clinical decision making.
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