First published in 2019, the Ovarian-Adnexal Reporting and Data System (O-RADS) US provides a standardized lexicon for ovarian and adnexal lesions, enables stratification of these lesions with use of a numeric score based on morphologic features to indicate the risk of malignancy, and offers management guidance. This risk stratification system has subsequently been validated in retrospective studies and has yielded good interreader concordance, even with users of different levels of expertise. As use of the system increased, it was recognized that an update was needed to address certain clinical challenges, clarify recommendations, and incorporate emerging data from validation studies. Additional morphologic features that favor benignity, such as the bilocular feature for cysts without solid components and shadowing for solid lesions with smooth contours, were added to O-RADS US for optimal risk-appropriate scoring. As O-RADS US 4 has been shown to be an appropriate cutoff for malignancy, it is now recommended that lower-risk O-RADS US 3 lesions be followed with US if not excised. For solid lesions and cystic lesions with solid components, further characterization with MRI is now emphasized as a supplemental evaluation method, as MRI may provide higher specificity. This statement summarizes the updates to the governing concepts, lexicon terminology and assessment categories, and management recommendations found in the 2022 version of O-RADS US.
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http://dx.doi.org/10.1148/radiol.230685 | DOI Listing |
Eur Radiol
November 2024
Department of Diagnostic Radiology, Faculty of Human Medicine, Zagazig University, Zagazig, Egypt.
Arch Gynecol Obstet
December 2024
Department of Medical Ultrasound, The Fourth Affiliated Hospital of Harbin Medical University, No. 37 Yiyuan Street, Nangang District, Harbin, 150001, Heilongjiang, China.
Purpose: The study aimed to create a deep convolutional neural network (DCNN) model based on ConvNeXt-Tiny to identify classic benign lesions (CBL) from other lesions (OL) within the Ovarian-Adnexal Reporting and Data System (O-RADS), enhancing the system's utility for novice ultrasonographers.
Methods: Two sets of sonographic images of pathologically confirmed adnexal lesions were retrospectively collected [development dataset (DD) and independent test dataset (ITD)]. The ConvNeXt-Tiny model, optimized through transfer learning, was trained on the DD using the original images directly and after automatic lesion segmentation by a U-Net model.
BMC Med Imaging
November 2024
Department of Ultrasound, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University Health Science Center, No.3002, Sungang West Road, Futian District, Shenzhen, China.
Background: Ovarian cancer remains a leading cause of death among women, largely due to its asymptomatic early stages and high mortality when diagnosed late. Early detection significantly improves survival rates, and the Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) is currently the most commonly used method, but has limitations in specificity and accuracy. While O-RADS US has standardized reporting, its sensitivity can lead to the misdiagnosis of benign masses as malignant, resulting in overtreatment.
View Article and Find Full Text PDFRadiol Clin North Am
January 2025
Department of Radiology and Ob/Gyn, Michigan Medicine, 1500 East Medical Center Dr, B1 D530G, Ann Arbor, MI, USA.
This review of the American College of Radiology Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound (US) v2022 will familiarize the reader with the updated O-RADS US system, highlight new updates, and outline key technical and reporting components. Additionally, this review will outline how to approach and incorporate the system into clinical practice, with reporting and real-world examples. Future directions will focus on addressing knowledge gaps and expanding on research opportunities.
View Article and Find Full Text PDFRadiol Case Rep
January 2025
Department of Radiology, National Institute of Oncology, UHC Ibn Sina, Mohamed V University, Rabat, Morocco.
Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis. It constitutes a public health problem, especially in developing countries. Pelvic localization is rare with tubal involvement being the most frequent.
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