3.8 Proceedings Paper

Placenta Accreta Spectrum and Hysterectomy Prediction Using MRI Radiomic Features

Journal

Publisher

SPIE-INT SOC OPTICAL ENGINEERING
DOI: 10.1117/12.2611587

Keywords

Placenta accreta spectrum (PAS); radiomics; magnetic resonance imaging (MRI); uterus; machine learning; pregnant; hysterectomy

Funding

  1. U.S. National Institutes of Health (NIH) [R01CA156775, R01CA204254, R01HL140325, R21CA231911]
  2. Cancer Prevention and Research Institute of Texas (CPRIT) [RP190588]

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This study uses MRI images to predict the presence of PAS and the need for cesarean hysterectomy. By analyzing the placenta and uterus regions of interest and incorporating information from their dilation, the study achieves high accuracy in predicting hysterectomy and classifying suspected PAS.
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.

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