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Zhang J, Kong L, Qu F, Chen T, Zhou X, Ge Z, Jin B, Zhang X, Zhao M. The predictive value of conventional magnetic resonance imaging combined with intravoxel incoherent motion parameters for evaluating maternal and neonatal clinical outcomes in patients with placenta accreta spectrum disorders. Placenta 2024; 151:10-17. [PMID: 38631235 DOI: 10.1016/j.placenta.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/31/2024] [Accepted: 04/12/2024] [Indexed: 04/19/2024]
Abstract
INTRODUCTION We aimed to identify factors predictive of adverse maternal and neonatal outcomes in patients with placenta accreta spectrum (PAS) disorders using magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) parameters. METHOD Fifty-six normal singleton pregnancies at 33-39 weeks of gestation underwent MRI examination at 1.5 T. The IVIM parameters were obtained from the placenta. The correlation between the f value and postpartum hemorrhage (PPH) and between the f value and transfused units of red blood cells (RBCs) was estimated by linear regression. The correlation between various influencing factors (clinical risk factors, MRI features, and IVIM parameters) and poor outcomes was investigated using univariate and multivariate analyses. RESULT The interobserver agreement ranged from fair to excellent (k = 0.30-0.88). Multivariate analyses showed that previous cesarean sections, low signal intensity bands on T2WI and the D value were independent risk factors for adverse outcomes. The combination of three risk factors demonstrated the highest AUC of 0.903, with a sensitivity and specificity of 73.10 % and 96.90 %, respectively. Last, f was positively correlated with PPH and units of RBCs transfused. DISCUSSION Preoperative MRI features and IVIM parameters may be used to predict poor outcomes in patients with invasive placental disorders like PAS.
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Affiliation(s)
- Jin Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Lingnan Kong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Feifei Qu
- Research Collaboration Team, Siemens Healthineers Ltd, Shanghai, China
| | - Ting Chen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Xin Zhou
- Department of Obstetrics & Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Zhiping Ge
- Department of Obstetrics & Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Bai Jin
- Department of Obstetrics & Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Xuan Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Meng Zhao
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
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Li H, Lu T, Li M, Wang Y, Zhang F, Yuan Y, Zhu M, Zhao X. Differentiation of placenta percreta through MRI features and diffusion-weighted magnetic resonance imaging. Insights Imaging 2023; 14:93. [PMID: 37222836 DOI: 10.1186/s13244-023-01448-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/09/2023] [Indexed: 05/25/2023] Open
Abstract
OBJECTIVES To identify whether parameters measured from diffusion kurtosis and intravoxel incoherent motion help diagnose placenta percreta. METHODS We retrospectively enrolled 75 patients with PAS disorders including 13 patients with placenta percreta and 40 patients without PAS disorders. Each patients underwent diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI). The apparent diffusion coefficient (ADC), perfusion fraction (f), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), mean diffusion kurtosis (MK) and mean diffusion coefficient (MD) were measured by the volumetric analysis and compared. MRI features were also analyzed and compared. The receiver operating characteristic (ROC) curve and logistic regression analysis were used to evaluate the diagnostic efficiency of different diffusion parameters and MRI features for distinguishing placental percreta. RESULTS D* was an independent risk factor from DWI for predicting placenta percreta with sensitivity of 73% and specificity of 76%. Focal exophytic mass remained as independent risk factor from MRI features for predicting placenta percreta with sensitivity of 72.7% and specificity of 88.1%. When the two risk factors were combined together, the AUC was the highest, 0.880 (95% CI 0.8-0.96). CONCLUSION D* and focal exophytic mass were associated with placenta percreta. A combination of the 2 risk factors can be used to predict placenta percreta. CRITICAL RELEVANCE STATEMENT A combination of D* and focal exophytic mass can be used to differentiate placenta percreta.
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Affiliation(s)
- Hang Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32 West Second Section, First Ring Road, Chengdu, 610072, China
| | - Tao Lu
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32 West Second Section, First Ring Road, Chengdu, 610072, China.
| | - Mou Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32 West Second Section, First Ring Road, Chengdu, 610072, China
| | - Yishuang Wang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32 West Second Section, First Ring Road, Chengdu, 610072, China
| | - Feng Zhang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32 West Second Section, First Ring Road, Chengdu, 610072, China
| | - Yi Yuan
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32 West Second Section, First Ring Road, Chengdu, 610072, China
| | - Meilin Zhu
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32 West Second Section, First Ring Road, Chengdu, 610072, China
| | - Xinyi Zhao
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32 West Second Section, First Ring Road, Chengdu, 610072, China
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