A novel MRI-based diagnostic model for predicting placenta accreta spectrum.

Autor: Xia J; Department of Radiology, The First People's Hospital of Qinzhou, 53500, China., Hu Y; Department of Radiology, The First People's Hospital of Qinzhou, 53500, China., Huang Z; Department of Radiology, The First People's Hospital of Qinzhou, 53500, China., Chen S; Department of Radiology, The First People's Hospital of Qinzhou, 53500, China;. Electronic address: 370123012@qq.com., Huang L; Department of Radiology, The First People's Hospital of Qinzhou, 53500, China., Ruan Q; Department of Radiology, The First People's Hospital of Qinzhou, 53500, China., Zhao C; MR Research Collaboration, Siemens Healthineers, Guangzhou 510620, China., Deng S; Department of Radiology, The First People's Hospital of Qinzhou, 53500, China., Wang M; MR Research Collaboration, Siemens Healthineers, Beijing 100102, China., Zhang Y; Department of Research Administration, The First People's Hospital of Qinzhou, 53500, China.
Jazyk: angličtina
Zdroj: Magnetic resonance imaging [Magn Reson Imaging] 2024 Jun; Vol. 109, pp. 34-41. Date of Electronic Publication: 2024 Feb 24.
DOI: 10.1016/j.mri.2024.02.014
Abstrakt: Objective To develop and evaluate a diagnostic model based on MRI signs for predicting placenta accreta spectrum. Materials and Methods A total of 155 pregnant women were included in this study, randomly divided into 104 cases in the training set and 51 cases in the validation set. There were 93 Non-PAS cases, and 62 cases in the PAS group. The training set included 62 Non-PAS cases and 42 PAS cases. Clinical factors and MRI signs were collected for univariate analysis. Then, binary logistic regression analysis was used to develop independent diagnostic models with clinical relevant risk factors or MRI signs, as well as those combining clinical risk factors and MRI signs. The ROC curve analysis was used to evaluate the diagnostic performance of each diagnostic model. Finally, the validation was performed with the validation set. Results In the training set, four clinical factors (gestity, parity, uterine surgery history, placental position) and 11 MRI features (T2-dark bands, placental bulge, T2 hypointense interface loss, myometrial thinning, bladder wall interruption, focal exophytic mass, abnormal placental bed vascularization, placental heterogeneity, asymmetric placental thickening/shape, placental ischemic infarction, abnormal intraplacental vascularity) were considered as risk factors for PAS. The AUC of the clinical diagnostic model, MRI diagnostic model, and clinical + MRI model of PAS were 0.779, 0.854, and 0.874, respectively. In the validation set, the AUC of the clinical diagnostic model, MRI diagnostic model, and clinical + MRI model of PAS were 0.655, 0.728, and 0.735, respectively. Conclusion Diagnosis model based on MRI features in this study can well predict placenta accreta spectrum.
Competing Interests: Competing interests No potential conflict of interest was reported by the author(s) that could have appeared to influence the work reported in this paper. Data Availability Statements: The authors are grateful for the support and assistance of participating patients during this project. Author Statement. Jianfeng Xia (First Author):Conceptualization, Data Curation, Formal Analysis, Methodology, Project Administration, Resources, Visualization, Writing - Original Draft; Yongren Hu: Data Curation, Methodology, Resources; Zehe Huang: Methodology, Project Administration; Song Chen (Corresponding Author): Conceptualization, Data Curation, Methodology, Project Administration, Writing - Review & Editing. Lanbin Huang:Data Curation, Resources; Qizeng Ruan:Data Curation, Resources; Chen Zhao:Software, Validation；. Shicai Deng: Resources,Visualization. Mengzhu Wang: Methodology. Yu Zhang: Visualization.
(Copyright © 2024. Published by Elsevier Inc.)
Databáze: MEDLINE
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