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USMRI Features and Clinical Data-Based Model for Predicting the Degree of Placenta Accreta Spectrum Disorders and Developing Prediction Models

AIM: This study aimed to investigate the ability of ultrasound/magnetic resonance imaging (MRI) signature and clinical data-based model for preoperatively predicting the degree of placenta accreta spectrum disorders and develop combined prediction models. METHODS: The clinicopathological characteris...

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Autores principales: An, Peng, Zhang, Junyan, Yang, Feng, Wang, Zhongqiu, Hu, Yan, Li, Xiumei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159129/
https://www.ncbi.nlm.nih.gov/pubmed/35685563
http://dx.doi.org/10.1155/2022/9527412
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author An, Peng
Zhang, Junyan
Yang, Feng
Wang, Zhongqiu
Hu, Yan
Li, Xiumei
author_facet An, Peng
Zhang, Junyan
Yang, Feng
Wang, Zhongqiu
Hu, Yan
Li, Xiumei
author_sort An, Peng
collection PubMed
description AIM: This study aimed to investigate the ability of ultrasound/magnetic resonance imaging (MRI) signature and clinical data-based model for preoperatively predicting the degree of placenta accreta spectrum disorders and develop combined prediction models. METHODS: The clinicopathological characteristics, prenatal ultrasound images, and MRI features of 132 pregnant women with placenta accreta spectrum disorders at Xiangyang No. 1 People's Hospital were retrospectively reviewed from January 2016 to December 2020. In the training set of 99 patients, the ultrasound/MRI features model, clinical characteristics model, and combined model were developed by multivariate logistic regression analysis to predict the degree of placenta accreta spectrum disorders. The prediction performance of different models was compared using the Delong test. The developed models were validated by assessing their prediction performance in a test set of 33 patients. RESULTS: The multivariate logistic regression analysis identified history of abortion, history of endometrial injury, and blurred boundary between the placenta and the myometrium/between the uterine serosa and the bladder to construct a combined model for predicting the degree of placenta accreta spectrum disorders (area under the curve (AUC) = 0.931; 95% confidence interval (CI): 0.882–0.980). The AUC of the clinical characteristics model and ultrasound/MRI features model was 0.858 (95% CI 0.794–0.921) and 0.709 (95% CI 0.624–0.798), respectively. The AUC of the combined model was significantly higher than that of the ultrasound/MRI features model (P < 0.001) or clinical characteristics model (P < 0.0015) in the training set. In the test set, the combined model also showed higher prediction performance. CONCLUSIONS: Ultrasound/MRI-based signature is a powerful predictor for the degree of placenta accreta spectrum disorders in an early stage. A combined model (constructed with history of abortion, history of endometrial injury, and blurred boundary between the placenta and the myometrium/between the uterine serosa and the bladder) can improve the accuracy for predicting the degree of placenta accreta spectrum disorders in an early stage.
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spelling pubmed-91591292022-06-07 USMRI Features and Clinical Data-Based Model for Predicting the Degree of Placenta Accreta Spectrum Disorders and Developing Prediction Models An, Peng Zhang, Junyan Yang, Feng Wang, Zhongqiu Hu, Yan Li, Xiumei Int J Clin Pract Research Article AIM: This study aimed to investigate the ability of ultrasound/magnetic resonance imaging (MRI) signature and clinical data-based model for preoperatively predicting the degree of placenta accreta spectrum disorders and develop combined prediction models. METHODS: The clinicopathological characteristics, prenatal ultrasound images, and MRI features of 132 pregnant women with placenta accreta spectrum disorders at Xiangyang No. 1 People's Hospital were retrospectively reviewed from January 2016 to December 2020. In the training set of 99 patients, the ultrasound/MRI features model, clinical characteristics model, and combined model were developed by multivariate logistic regression analysis to predict the degree of placenta accreta spectrum disorders. The prediction performance of different models was compared using the Delong test. The developed models were validated by assessing their prediction performance in a test set of 33 patients. RESULTS: The multivariate logistic regression analysis identified history of abortion, history of endometrial injury, and blurred boundary between the placenta and the myometrium/between the uterine serosa and the bladder to construct a combined model for predicting the degree of placenta accreta spectrum disorders (area under the curve (AUC) = 0.931; 95% confidence interval (CI): 0.882–0.980). The AUC of the clinical characteristics model and ultrasound/MRI features model was 0.858 (95% CI 0.794–0.921) and 0.709 (95% CI 0.624–0.798), respectively. The AUC of the combined model was significantly higher than that of the ultrasound/MRI features model (P < 0.001) or clinical characteristics model (P < 0.0015) in the training set. In the test set, the combined model also showed higher prediction performance. CONCLUSIONS: Ultrasound/MRI-based signature is a powerful predictor for the degree of placenta accreta spectrum disorders in an early stage. A combined model (constructed with history of abortion, history of endometrial injury, and blurred boundary between the placenta and the myometrium/between the uterine serosa and the bladder) can improve the accuracy for predicting the degree of placenta accreta spectrum disorders in an early stage. Hindawi 2022-01-31 /pmc/articles/PMC9159129/ /pubmed/35685563 http://dx.doi.org/10.1155/2022/9527412 Text en Copyright © 2022 Peng An et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
An, Peng
Zhang, Junyan
Yang, Feng
Wang, Zhongqiu
Hu, Yan
Li, Xiumei
USMRI Features and Clinical Data-Based Model for Predicting the Degree of Placenta Accreta Spectrum Disorders and Developing Prediction Models
title USMRI Features and Clinical Data-Based Model for Predicting the Degree of Placenta Accreta Spectrum Disorders and Developing Prediction Models
title_full USMRI Features and Clinical Data-Based Model for Predicting the Degree of Placenta Accreta Spectrum Disorders and Developing Prediction Models
title_fullStr USMRI Features and Clinical Data-Based Model for Predicting the Degree of Placenta Accreta Spectrum Disorders and Developing Prediction Models
title_full_unstemmed USMRI Features and Clinical Data-Based Model for Predicting the Degree of Placenta Accreta Spectrum Disorders and Developing Prediction Models
title_short USMRI Features and Clinical Data-Based Model for Predicting the Degree of Placenta Accreta Spectrum Disorders and Developing Prediction Models
title_sort usmri features and clinical data-based model for predicting the degree of placenta accreta spectrum disorders and developing prediction models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159129/
https://www.ncbi.nlm.nih.gov/pubmed/35685563
http://dx.doi.org/10.1155/2022/9527412
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