Assessing reproducibility in magnetic resonance (MR) radiomics features between deep-learning segmented and expert manual segmented data and evaluating their diagnostic performance in pregnant women with suspected placenta accreta spectrum (PAS)

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

Abstract

A Deep-Learning (DL) based segmentation tool was applied to a new magnetic resonance imaging dataset of pregnant women with suspected Placenta Accreta Spectrum (PAS). Radiomic features from DL segmentation were compared to those from expert manual segmentation via intraclass correlation coefficients (ICC) to assess reproducibility. An additional imaging marker quantifying the placental location within the uterus (PLU) was included. Features with an ICC < 0.7 were used to build logistic regression models to predict hysterectomy. Of 2059 features, 781 (37.9%) had ICC <0.7. AUC was 0.69 (95% CI 0.63-0.74) for manually segmented data and 0.78 (95% CI 0.73-0.83) for DL segmented data.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2021
Subtitle of host publicationComputer-Aided Diagnosis
EditorsMaciej A. Mazurowski, Karen Drukker
PublisherSPIE
ISBN (Electronic)9781510640238
DOIs
StatePublished - 2021
EventMedical Imaging 2021: Computer-Aided Diagnosis - Virtual, Online, United States
Duration: Feb 15 2021Feb 19 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11597
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2021: Computer-Aided Diagnosis
CountryUnited States
CityVirtual, Online
Period2/15/212/19/21

ASJC Scopus subject areas

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

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