Background and aimsThe present study aimed to analyze the effects of factors on cystocele and the Green classification.Materials and methodsWe conducted a cross-sectional study on 357 primiparous women examined at our hospital from January 2019 to May 2021. The following data were recorded: maternal characteristics, neonatal characteristics, and factors of childbirth. It was added to the multivariate logistic regression model to determine the independent predictors of the cystocele and the Green classification.ResultsA total of 242 women had cystocele, including 71 women with Green type I cystocele, 134 women with Green type II cystocele, and 37 women with Green type III cystocele. In multivariate logistic regression analysis, body mass index (BMI) at delivery was associated with cystocele, while BMI at delivery and the second stage of labor (SSL) > 1 h were independently with the distance from the symphysis pubis to the bladder neck (SPBN) abnormal (P < 0.05). BMI at examination was associated with the large retrovesical angle (RVA) (P < 0.05). BMI at delivery and the fetal right occiput anterior position (ROA) were independently associated with the distance from the symphysis pubis to the posterior wall of the bladder (SPBP) abnormal (P < 0.05), while epidural anesthesia (EDA) was the protective factor (P < 0.05).ConclusionPrimipara women should strive to avoid exposure to modifiable risk factors such as controlling weight during pregnancy, reducing weight after delivery, and shortening SSL to reduce the occurrence of cystocele.
Background
Breast cancer is the most common tumor globally. Automated Breast Volume Scanner (ABVS) and strain elastography (SE) can provide more useful breast information. The use of radiomics combined with ABVS and SE images to predict breast cancer has become a new focus. Therefore, this study developed and validated a radiomics analysis of breast lesions in combination with coronal plane of ABVS and SE to improve the differential diagnosis of benign and malignant breast diseases.
Patients and Methods
620 pathologically confirmed breast lesions from January 2017 to August 2021 were retrospectively analyzed and randomly divided into a training set (n=434) and a validation set (n=186). Radiomic features of the lesions were extracted from ABVS, B-ultrasound, and strain elastography (SE) images, respectively. These were then filtered by Gradient Boosted Decision Tree (GBDT) and multiple logistic regression. The ABVS model is based on coronal plane features for the breast, B+SE model is based on features of B-ultrasound and SE, and the multimodal model is based on features of three examinations. The evaluation of the predicted performance of the three models used the receiver operating characteristic (ROC) and decision curve analysis (DCA).
Results
The area under the curve, accuracy, specificity, and sensitivity of the multimodal model in the training set are 0.975 (95% CI:0.959–0.991),93.78%, 92.02%, and 96.49%, respectively, and 0.946 (95% CI:0.913 −0.978), 87.63%, 83.93%, and 93.24% in the validation set, respectively. The multimodal model outperformed the ABVS model and B+SE model in both the training (
P
< 0.001,
P
= 0.002, respectively) and validation sets (
P
< 0.001,
P
= 0.034, respectively).
Conclusion
Radiomics from the coronal plane of the breast lesion provide valuable information for identification. A multimodal model combination with radiomics from ABVS, B-ultrasound, and SE could improve the diagnostic efficacy of breast masses.
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