@inproceedings{a2356011440a4b049f8c93b363f9f32e,
title = "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)",
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.",
author = "Yin Xi and Maysam Shahedi and Do, {Quyen N.} and James Dormer and Lewis, {Matthew A.} and Baowei Fei and Spong, {Catherine Y.} and Madhuranthakam, {Ananth J.} and Twickler, {Diane M.}",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Medical Imaging 2021: Computer-Aided Diagnosis ; Conference date: 15-02-2021 Through 19-02-2021",
year = "2021",
doi = "10.1117/12.2581467",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Mazurowski, {Maciej A.} and Karen Drukker",
booktitle = "Medical Imaging 2021",
}