Using AI might afford large-scale evaluation for splenomegaly on CT examinations

According to an accepted manuscript published in ARRS' own American Journal of Roentgenology (AJR), using an automated deep-learning AI tool, as well as weight-based volumetric thresholds, might afford large-scale evaluation for splenomegaly on CT examinations performed for any indication.

Noting that, historically, the standard linear splenic measurements used as a surrogate for splenic volume yielded suboptimal performance in detecting volume-based splenomegaly

The weight-based volumetric thresholds indicated the presence of splenomegaly in most patients who underwent pre-liver transplant CT."

Perry J. Pickhardt, MD, Study Corresponding Author, Department of Radiology, University of Wisconsin School of Medicine & Public Health

Pickhardt and colleagues' AJR accepted manuscript included a screening sample of 8,901 patients (4,235 men, 4,666 women; mean age, 56 years) who underwent CT colonoscopy (n = 7736) or renal-donor CT (n = 1165) from April 2004 to January 2017.

A secondary cohort of 104 patients (62 men, 42 women; mean age, 56 years) with end-stage liver disease underwent pre-liver transplant CT from January 2011 to May 2013. Pickhardt et al.'s deep learning algorithm-;previously developed, trained, and tested at the National Institutes of Health Clinical Center-;was used for spleen segmentation, to help determine splenic volumes, with two radiologists independently reviewing a subset of said segmentations.

Ultimately, this automated deep-learning AI tool was utilized to calculate splenic volumes from CT examinations in 8,853 patients from the primary outpatient population. Additionally, splenic volume was most strongly associated with weight, among a range of patient factors.

"To our knowledge," the AJR authors concluded, "this study represents the largest reported sample of patients to undergo volumetric segmentation of the spleen."