Objective: This study evaluated the metabolic parameters and texture features of fluorodeoxyglucose positron emission tomography–computed tomography (PET/CT) for the diagnosis and differentiation of endometrial atypical hyperplasia (EAH), EAH with field cancerization (FC), and stage 1A endometrial carcinoma (EC 1a). Materials and Methods: We retrospectively analyzed the metabolic parameters of PET/CT in 170 patients with diagnoses confirmed by pathology, including 57 cases of EAH (57/170, 33.53%), 45 cases of FC (45/170, 26.47%), and 68 cases of EC 1a (68/170, 40.0%). Then, the texture features of each tumor were extracted and compared with the metabolic parameters and pathological results using nonparametric tests and linear regression analysis. The diagnostic performance was assessed by the area under the curve (AUC) values obtained from receiver operating characteristic analysis. Results: There were moderate positive correlations between the PET standardized uptake values (SUVpeak, SUVmax, and SUVmean) and postoperative pathological features with correlation coefficients ( r s ) of 0.663, 0.651, and 0.651, respectively ( P < .001). Total lesion glycolysis showed relatively low correlation with pathological characteristics ( r s = 0.476), whereas metabolic tumor volume and age showed the weakest correlations ( r s = 0.186 and 0.232, respectively). To differentiate between the diagnosis of EAH and FC, SUVmax displayed the largest AUC of 0.857 (sensitivity, 82.2%; specificity, 84.2%). Five texture features were screened out as Percentile 40, Percentile 45, InverseDifferenceMoment_AllDirection_offset 1, InverseDifferenceMoment_angle 45_offset 4, and ClusterProminence_angle 135_offset 7 ( P < .001) by linear model of texture analysis (AUC = 0.851; specificity = 0.692; sensitivity = 0.871). To differentiate between the diagnoses of FC and EC 1a, SUVpeak displayed the largest AUC of 0.715 (sensitivity, 67.6%; specificity, 77.8%), and 2 texture features were identified as Percentile 10 and CP_angle 135_offset 7 (AUC = 0.819; specificity = 0.871; sensitivity = 0.766; P < .001). Conclusions: SUVmax and SUVpeak had the highest diagnostic values for EAH, FC, and EC 1a compared with the other tested parameters. SUVmax, Percentile 40, Percentile 45, InverseDifferenceMoment_AllDirection_offset 1, InverseDifferenceMoment_angle 45_offset 4, and ClusterProminence_angle 135_offset 7 distinguished EAH from FC. SUVpeak, Percentile 10, and ClusterProminence_angle 135_offset 7 distinguished FC from EC 1a. This study showed that the addition of texture features provides valuable information for differentiating EAH, FC, and EC 1a diagnoses.

Background: The value of multiple magnetic resonance imaging (MRI) signs in predicting pernicious placenta previa (PPP) with placenta accreta spectrum disorders (PAS) is still controversial. This study aimed to investigate the value of a self-made fetal magnetic resonance imaging scoring system in predicting the different types of PAS in pernicious placenta previa and its associated risk of bleeding.Methods: This retrospective study included 193 patients diagnosed with PPP based on MRI findings before delivery. Based on pathological and intraoperative findings, we divided patients into four groups: non-PAS, placental adhesion, placental implantation, and placenta percreta. Receiver operator characteristic curves of the MRI total score and placental implantation type were drawn using pROC packages in the R Studio environment, and cutoff values of each type were calculated, as well as diagnostic evaluation indexes, such as sensitivity, specificity, and the Youden index. Hemorrhage during surgery was compared between the groups.Results: The boundary value between the non-PAS and placental adhesion was 5.5, that between placental adhesion and placental implantation was 11.5, and that between placental implantation and placenta percreta was 15.5 points. The respective specificities were 0.700, 0.869, and 0.958, and the respective sensitivities were 0.994, 0.802, and 0.577. The Youden indices were 0.694, 0.671, and 0.535, respectively. The median (minimum, maximum) quantities of hemorrhage during the operation in the non-PAS, placental adhesion, placental implantation, and placenta percreta groups were 225 (100, 3700), 600 (200, 6000), 1500 (300, 7000), and 3000 (400, 6300) ml, respectively. Hemorrhage was significantly different between the four groups (p &lt; 0.001).Conclusion: These results suggest that the proposed MRI scoring system could be an effective diagnostic tool for assessing PPP types and predicting the associated bleeding risk.

No abstract

Objective: To explore the risk factors of postpartum hemorrhage (PPH) in patients with pernicious placenta previa (PPP) and to develop and validate a clinical and imaging-based predictive model.Methods: A retrospective analysis was conducted on patients diagnosed surgically and pathologically with PPP between January 2018 and June 2022. All patients underwent PPP magnetic resonance imaging (MRI) and ultrasound scoring in the second trimester and before delivery, and were categorized into two groups according to PPH occurrence. The total imaging score and sub-item prediction models of the MRI risk score/ultrasound score were used to construct Models A and B/Models C and D. Models E and F were the total scores of the MRI combined with the ultrasound risk and sub-item prediction model scores. Model G was based on the subscores of MRI and ultrasound with the introduction of clinical data. Univariate logistic regression analysis and the logical least absolute shrinkage and selection operator (LASSO) model were used to construct models. The receiver operating characteristic curve andision curve analysis (DCA) were drawn, and the model with the strongest predictive ability and the best clinical effect was selected to construct a nomogram. Internal sampling was used to verify the prediction model’s consistency.Results: 158 patients were included and the predictive power and clinical benefit of Models B and D were better than those of Models A and C. The results of the area under the curve of Models B, D, E, F, and G showed that Model G was the best, which could reach 0.93. Compared with Model F, age, vaginal hemorrhage during pregnancy, and amniotic fluid volume were independent risk factors for PPH in patients with PPP (p &lt; 0.05). We plotted the DCA of Models B, D, E, F, and G, which showed that Model G had better clinical benefits and that the slope of the calibration curve of Model G was approximately 45°.Conclusion: LASSO regression nomogram based on clinical risk factors and multiple conventional ultrasound plus MRI signs has a certain guiding significance for the personalized prediction of PPH in patients with PPP before delivery.

No abstract