ArticleEuropean radiology2026
How to integrate O-RADS MRI scores with clinical and imaging criteria to predict histopathological subtypes.
Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Gynecologic imaging errors and pitfalls: how to avoid misdiagnosis on pelvic MRI.Insights into imaging · 2026Review
- Retrospective evaluation of the mr#ENZIAN classification's applicability in the pre-operative assessment of complex endometriosis: a compartment-dependent challenge.Abdominal radiology (New York) · 2026Article
- Silent giant: a 35-cm mucinous ovarian cystadenoma presenting as failure to lose weight.Journal of surgical case reports · 2026Article
- Refining O-RADS MRI: beyond malignancy risk.European radiology · 2026Article
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7 authors.
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Abstract
objectiveTo enhance diagnostic precision by integrating the O-RADS MRI score with clinical and imaging criteria for histopathological subtyping. MATERIALS AND
methodsThis retrospective study analyzed data from the EURAD (EURopean ADnexal study) database between 2013 and 2018. Univariate and multivariate logistic regression analyses were performed to identify features potentially associated with presumed final histology.
resultsThe study encompassed 869 adnexal lesions from 651 patients (mean age: 49.7, STD: 16.1). On multivariate analysis, the presence of fatty content (OR = 126.5, CI
conclusionCombining the O-RADS MRI score with morphology, size, type of solid tissue and cystic component, including ADC values, enhances the precision of diagnosing presumed histological subtypes. KEY POINTS: Question Does integrating the O-RADS MRI score with clinical and imaging criteria improve diagnostic precision for histopathological subtyping? Findings Combining the O-RADS MRI score with specific clinical or imaging features improved the prediction of borderline, primary invasive, or metastatic tumors. Clinical relevance Combining the O-RADS MRI score with morphological and clinical features can help differentiate ovarian tumors thereby improving patient management, as this can help guide treatment decisions, avoid unnecessary surgeries, and streamline the referral process to specialized care.
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