Ultrasound specialists update recommendations for endometriosis screening

The Society of Radiologists in Ultrasound (SRU) has issued new recommendations to improve the screening process for endometriosis, which often faces diagnostic delays because it tends to elude transvaginal ultrasounds. In its consensus statement, the SRU panel recommends tweaking existing imaging protocol for the rapid detection of endometriosis, so that women can be treated with minimal delay. The statement is published in Radiology. [1]

The multi-institutional panel of authors still recommend ultrasound as the primary diagnostic method, given it is minimally invasive and easily accessible for most patients. However, they cite a lack of user expertise in performing the scans, writing that “scan protocol limitations and lack of awareness lead to suboptimal detection of deep endometriosis on pelvic ultrasound images."

To ensure the best image quality for diagnostics, the panel recommends augmenting the traditional pelvic ultrasound using a couple relatively simple maneuvers. Specifically, the authors propose a transvaginal ultrasound of the posterior compartment, as well as the uterine sliding sign maneuver, which can both be quickly performed to improve endometriosis detection through clearer images that would show deep endometriosis, thus reducing diagnostic delay for many patients. 

“These additional techniques typically can be performed in less than five minutes and could ultimately decrease the delay of an endometriosis diagnosis in at-risk patients,” lead author of the statement, Scott Young, MD, from the Mayo Clinic, said in a press release sent to Health Imaging.

“The SRU consensus on routine pelvic ultrasound for endometriosis aims to enhance deep endometriosis detection even at an initial ultrasound and with minimal additional time during imaging and no special patient preparation,” Young added. “Focusing imaging on anatomic regions where deep endometriosis is common can increase detection and decrease diagnostic delay.”

The recommendations from Young and his colleagues—all experts on endometriosis, ranging from radiologists, gynecologists, to sonographers—came after extensive literature review, with personal experience in use of the modified pelvic ultrasound technique to achieve better results. 

New rating system proposed

Along with this initial change to how patients are scanned, Young and the other authors also propose an immediate rating system for follow-up imaging and care of deep endometriosis, with four rankings that assess confidence in the diagnosis and potential severity of the condition: Incomplete, Normal, Equivocal and Positive. Each ranking also comes with specific clinical recommendations for next steps, all once again focused on deploying timely interventions to improve outcomes.

The authors note that more research is required to validate and improve the ranking system. However, they expressed confidence in their new ultrasound technique recommendations for diagnosis, and encouraged providers to implement them as soon as possible. 

“Robust communication with minimally invasive gynecologic surgeons is key and multidisciplinary discussions can lead to incremental benefit for patients,” the other authors wrote. “The consensus panel is confident about the feasibility of simple additional maneuvers to improve endometriosis detection in this traditionally underdiagnosed condition.”

The full statement is available at the link below.

Chad Van Alstin Health Imaging Health Exec

Chad is an award-winning writer and editor with over 15 years of experience working in media. He has a decade-long professional background in healthcare, working as a writer and in public relations.

Around the web

The new technology shows early potential to make a significant impact on imaging workflows and patient care. 

Richard Heller III, MD, RSNA board member and senior VP of policy at Radiology Partners, offers an overview of policies in Congress that are directly impacting imaging.
 

The two companies aim to improve patient access to high-quality MRI scans by combining their artificial intelligence capabilities.