Predicting Outcomes for Uterine Fibroid Embolization by using Deep Learning of Paired MRI Scans

Weill Medical Coll Of Cornell Univ, New York NY

Investigators

Abstract

PROJECT SUMMARY Uterine fibroids represent the highest prevalence of benign tumors in women, with reports ranging anywhere from 4.5% to 68.6%, with a significant bias towards African American women. It is estimated that the economic burden on the healthcare system from symptomatic women with uterine fibroids is up to $34 million. Currently, uterine fibroid embolization (UFE) is considered a highly effective minimally invasive procedure with up to an 85% success rate. However, hysterectomies are the most commonly performed procedures, accounting for nearly 600,000 annually, while only 14,000 UFE procedures are performed annually. It has been well documented that minorities are less likely to be referred for minimally invasive procedures, even though there is universal insurance coverage for them. Furthermore, women in lower socio-economic status, particularly African Americans, have been disproportionately referred for open surgery. Therefore, automated tools, like the ones in this proposal, that can provide unbiased referrals will be significant advantage at combating this unfortunate bias. This proposal will specifically explore the use of machine learning and deep learning methods to leverage a novel retrospective dataset that compiles features extracted from paired pre-operative and post-operative magnetic resonance imaging (MRI) scans of up to 700 patients who underwent a UFE. These models will provide a UFE treatment effectiveness score that will provide an objective and quantitative metric to decide whether a patient is good candidate for UFE. The short term impact of this proposal will be the creation of a curated database of paired UFE MRI scans that have been analyzed for various metrics regarding fibroid positions and patient characteristics, that will allow the clinical community to begin providing quantitative methods to determine UFE candidates. The long-term impact of this proposal will be realized in subsequent clinical trials that validate these AI models properly to predict which patients should be leveraging UFE as a non-surgical alternative for treating fibroids.