Deep Learning Based on MR Imaging for Predicting Outcome of Uterine Fibroid Embolization

Purpose: To develop and validate a deep learning model based on routine magnetic resonance (MR) imaging obtained before uterine fibroid embolization to predict procedure outcome. Materials and Methods: Clinical data were collected on patients treated with uterine fibroid embolization at the Hospital of the University of Pennsylvania from 2007 to 2018. Fibroids for each patient were manually segmented by an abdominal radiologist on a T1-weighted contrast-enhanced (T1C) sequence and a T2-weighted sequence of MR imaging obtained before and after embolization. A residual convolutional neural network (ResNet) model to predict clinical outcome was trained using MR imaging obtained before the procedure. Results: Inclusion criteria were met by 727 fibroids in 409 patients. At clinical follow-up, 85.6% (n = 350) of 409 patients (590 of 727 fibroids; 81.1%) experienced symptom resolution or improvement, and 14.4% (n = 59) of 409 patients (137 of 727 fibroids; 18.9%) had no improvement or worsening symptoms. The T1C trained model achieved a test accuracy of 0.847 (95% confidence interval [CI], 0.745–0.914), sensitivity of 0.932 (95% CI, 0.833–0.978), and specificity of 0.462 (95% CI, 0.232–0.709). In comparison, the average of 4 radiologists achieved a test accuracy of 0.722 (95% CI, 0.609–0.813), sensitivity of 0.852 (95% CI, 0.737–0.923), and specificity of 0.135 (95% CI, 0.021–0.415). Conclusions: This study demonstrates that deep learning based on a ResNet model achieves good accuracy in predicting outcome of uterine fibroid embolization. If further validated, the model may help clinicians better identify patients who can most benefit from this therapy and aid clinical decision making.