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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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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. |
format | Online Article Text |
id | pubmed-9159129 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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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