CascadeNet for hysterectomy prediction in pregnant women due to placenta accreta spectrum

James D. Dormer, Michael Villordon, Maysam Shahedi, Ka'Toria Leitch, Quyen N. Do, Yin Xi, Matthew A. Lewis, Ananth J. Madhuranthakam, Christina L. Herrera, Catherine Y. Spong, Diane M. Twickler, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In severe cases, placenta accreta spectrum (PAS) requires emergency hysterectomy, endangering the life of both mother and fetus. Early prediction may reduce complications and aid in management decisions in these high-risk pregnancies. In this work, we developed a novel convolutional network architecture to combine MRI volumes, radiomic features, and custom feature maps to predict PAS severe enough to result in hysterectomy after fetal delivery in pregnant women. We trained, optimized, and evaluated the networks using data from 241 patients, in groups of 157, 24, and 60 for training, validation, and testing, respectively. We found the network using all three paths produced the best performance, with an AUC of 87.8, accuracy 83.3%, sensitivity of 85.0, and specificity of 82.5. This deep learning algorithm, deployed in clinical settings, may identify women at risk before birth, resulting in improved patient outcomes.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2022
Subtitle of host publicationImage Processing
EditorsOlivier Colliot, Ivana Isgum, Bennett A. Landman, Murray H. Loew
ISBN (Electronic)9781510649392
StatePublished - 2022
EventMedical Imaging 2022: Image Processing - Virtual, Online
Duration: Mar 21 2021Mar 27 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2022: Image Processing
CityVirtual, Online


  • Deep Learning
  • Image Processing
  • Placenta Accreta
  • Radiomics

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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