Poster + Presentation + Paper
4 April 2022 Placenta accreta spectrum and hysterectomy prediction using MRI radiomic features
Author Affiliations +
Conference Poster
Abstract
In women with placenta accreta spectrum (PAS), patient management may involve cesarean hysterectomy at delivery. Magnetic resonance imaging (MRI) has been used for further evaluation of PAS and surgical planning. This work tackles two prediction problems: predicting presence of PAS and predicting hysterectomy using MR images of pregnant patients. First, we extracted approximately 2,500 radiomic features from MR images with two regions of interest: the placenta and the uterus. In addition to analyzing two regions of interest, we dilated the placenta and uterus masks by 5, 10, 15, and 20 mm to gain insights from the myometrium, where the uterus and placenta overlap in the case of PAS. This study cohort includes 241 pregnant women. Of these women, 89 underwent hysterectomy while 152 did not; 141 with suspected PAS, and 100 without suspected PAS. We obtained an accuracy of 0.88 for predicting hysterectomy and an accuracy of 0.92 for classifying suspected PAS. The radiomic analysis tool is further validated, it can be useful for aiding clinicians in decision making on the care of pregnant women.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ka'Toria Leitch, Maysam Shahedi, James D. Dormer, Quyen N. Do, Yin Xi, Matthew A. Lewis, Christina L. Herrera, Catherine Y. Spong, Ananth J. Madhuranthakam, Diane M. Twickler, and Baowei Fei "Placenta accreta spectrum and hysterectomy prediction using MRI radiomic features", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120331I (4 April 2022); https://doi.org/10.1117/12.2611587
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KEYWORDS
Magnetic resonance imaging

Uterus

Feature selection

Feature extraction

Image segmentation

Machine learning

Image filtering

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