Gynecologic imaging errors and pitfalls: how to avoid misdiagnosis on pelvic MRI

Abstract Pelvic magnetic resonance imaging (MRI) plays a pivotal role in evaluating gynecologic diseases, offering superior soft-tissue characterization, multiplanar imaging, and functional assessment compared to ultrasound and computed tomography. This article proposes evidence-based, standardized MRI protocols and a reproducible interpretative framework to enhance diagnostic accuracy, reduce misdiagnoses, and support multidisciplinary management in routine clinical practice. Key insights cover MR imaging of major gynecologic diseases: endometriosis, adnexal masses including the O-RADS MR scoring system, and pathological hypothesis as well as uterine diseases including endometrial, cervical, and myometrial benign and malignant conditions. For all situations, we present typical and atypical presentations as well as mimickers. This article will give some tips and tricks to help the radiologist in clinical routine for detecting and differentiating the main issues that impact patient management. By adopting standardized protocols and reports with adequate pathology knowledge, radiologists can improve patient outcomes through precise staging, surgical planning, and personalized care. Key Points Question How can radiologists avoid common diagnostic errors and pitfalls when interpreting pelvic MRI for gynecologic diseases, including endometriosis, uterine tumors, and adnexal masses? Findings Knowledge of common pitfalls, standardized MRI protocols with Dixon-based T1-weighted sequences, diffusion-weighted imaging, and dynamic contrast enhancement, if necessary, combined with structured reporting frameworks including O-RADS MRI and FIGO 2023, reduces misdiagnosis across gynecologic conditions. Critical Relevance Statement This article critically reviews typical and atypical presentations alongside common mimickers of major gynecologic diseases on pelvic MRI, providing radiologists with practical evidence-based strategies to improve diagnostic accuracy, reduce unnecessary interventions, and support multidisciplinary clinical decision-making.