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

doi:10.1117/12.2611580

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 proceeding › Conference contribution

1 Scopus citations

Abstract

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 language	English (US)
Title of host publication	Medical Imaging 2022
Subtitle of host publication	Image Processing
Editors	Olivier Colliot, Ivana Isgum, Bennett A. Landman, Murray H. Loew
Publisher	SPIE
ISBN (Electronic)	9781510649392
DOIs	https://doi.org/10.1117/12.2611580
State	Published - 2022
Event	Medical Imaging 2022: Image Processing - Virtual, Online Duration: Mar 21 2021 → Mar 27 2021

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	12032
ISSN (Print)	1605-7422

Conference

Conference	Medical Imaging 2022: Image Processing
City	Virtual, Online
Period	3/21/21 → 3/27/21

Keywords

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

Access to Document

10.1117/12.2611580

Cite this

Dormer, J. D., Villordon, M., Shahedi, M., Leitch, KT., Do, Q. N., Xi, Y., Lewis, M. A., Madhuranthakam, A. J., Herrera, C. L., Spong, C. Y., Twickler, D. M., & Fei, B. (2022). CascadeNet for hysterectomy prediction in pregnant women due to placenta accreta spectrum. In O. Colliot, I. Isgum, B. A. Landman, & M. H. Loew (Eds.), Medical Imaging 2022: Image Processing Article 120320N (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 12032). SPIE. https://doi.org/10.1117/12.2611580

CascadeNet for hysterectomy prediction in pregnant women due to placenta accreta spectrum. / Dormer, James D.; Villordon, Michael; Shahedi, Maysam et al.
Medical Imaging 2022: Image Processing. ed. / Olivier Colliot; Ivana Isgum; Bennett A. Landman; Murray H. Loew. SPIE, 2022. 120320N (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 12032).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Dormer, JD, Villordon, M, Shahedi, M, Leitch, KT, Do, QN , Xi, Y , Lewis, MA , Madhuranthakam, AJ , Herrera, CL , Spong, CY , Twickler, DM & Fei, B 2022, CascadeNet for hysterectomy prediction in pregnant women due to placenta accreta spectrum. in O Colliot, I Isgum, BA Landman & MH Loew (eds), Medical Imaging 2022: Image Processing., 120320N, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 12032, SPIE, Medical Imaging 2022: Image Processing, Virtual, Online, 3/21/21. https://doi.org/10.1117/12.2611580

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title = "CascadeNet for hysterectomy prediction in pregnant women due to placenta accreta spectrum",

abstract = "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.",

keywords = "Deep Learning, Image Processing, Placenta Accreta, Radiomics",

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

note = "Publisher Copyright: {\textcopyright} 2022 SPIE; Medical Imaging 2022: Image Processing ; Conference date: 21-03-2021 Through 27-03-2021",

year = "2022",

doi = "10.1117/12.2611580",

language = "English (US)",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

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AU - Dormer, James D.

AU - Villordon, Michael

AU - Shahedi, Maysam

AU - Leitch, Ka'Toria

AU - Do, Quyen N.

AU - Xi, Yin

AU - Lewis, Matthew A.

AU - Madhuranthakam, Ananth J.

AU - Herrera, Christina L.

AU - Spong, Catherine Y.

AU - Twickler, Diane M.

AU - Fei, Baowei

PY - 2022

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N2 - 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.

AB - 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.

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KW - Image Processing

KW - Placenta Accreta

KW - Radiomics

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BT - Medical Imaging 2022

A2 - Colliot, Olivier

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ER -

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

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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