Clinical imaging · 2021
Quantifying magnetic resonance imaging features to classify placenta accreta spectrum (PAS) in high-risk gravid patients.
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Chu Caiting, Liu Ming, Zhang Yuzheng, Yu Lingwei, Wang Dengbin, Gao Chengjin, Li Wenhua
Аннотация
OBJECTIVE: This study aimed to quantify the magnetic resonance imaging (MRI) features of placenta accreta spectrum (PAS) and to use MRI-based scores to classify them in high-risk gravid patients.
MATERIALS AND METHODS: The clinical data and MRI features of 65 high-risk gravid patients diagnosed with PAS were retrospectively reviewed. The MRI features of PAS were analysed and compared using the chi-squared test, and the odds ratios (ORs) for significant risk factors for classification of PAS were identified via a multivariate logistic regression model. A receiver-operating characteristic (ROC) curve was used to calculate cut-off values and their corresponding sensitivity, specificity, and accuracy in classifying PAS.
RESULTS: We identified 3 significant risk features for classification of PAS, including placental heterogeneity (OR = 13.604), abnormal vascularization at the placental-maternal interface (OR = 9.528), and focal myometrial interruption (OR = 118.779). The significant risk features for classification of PAS were scored according to their OR values, as 3 points (OR ≥ 20), 2 points (10 ≤ OR < 20), or 1 point (OR < 10). Based on the scores of the 3 risk features, a cut-off score of 4.5 points achieved optimal sensitivity (94.3%), specificity (90%), and accuracy (92.3%) for classifying PAS in high-risk gravid patients.
CONCLUSION: Quantifying these MRI features including placental heterogeneity, abnormal vascularization at the placental-maternal interface, and focal myometrial interruption can make a classification of PAS in high-risk gravid patients.
MATERIALS AND METHODS: The clinical data and MRI features of 65 high-risk gravid patients diagnosed with PAS were retrospectively reviewed. The MRI features of PAS were analysed and compared using the chi-squared test, and the odds ratios (ORs) for significant risk factors for classification of PAS were identified via a multivariate logistic regression model. A receiver-operating characteristic (ROC) curve was used to calculate cut-off values and their corresponding sensitivity, specificity, and accuracy in classifying PAS.
RESULTS: We identified 3 significant risk features for classification of PAS, including placental heterogeneity (OR = 13.604), abnormal vascularization at the placental-maternal interface (OR = 9.528), and focal myometrial interruption (OR = 118.779). The significant risk features for classification of PAS were scored according to their OR values, as 3 points (OR ≥ 20), 2 points (10 ≤ OR < 20), or 1 point (OR < 10). Based on the scores of the 3 risk features, a cut-off score of 4.5 points achieved optimal sensitivity (94.3%), specificity (90%), and accuracy (92.3%) for classifying PAS in high-risk gravid patients.
CONCLUSION: Quantifying these MRI features including placental heterogeneity, abnormal vascularization at the placental-maternal interface, and focal myometrial interruption can make a classification of PAS in high-risk gravid patients.