Association of interleukin 10 rs1800896 polymorphism with susceptibility to breast cancer: a meta-analysis.

Objective: To evaluate the correlation between interleukin 10 (IL-10) -1082A/G polymorphism (rs1800896) and breast cancers by performing a meta-analysis. Methods: The Embase and Medline databases were searched through 1 September 2018 to identify qualified articles. Odds ratios (OR) and corresponding 95% confidence intervals (CIs) were applied to evaluate associations. Results: In total, 14 case-control studies, including 5320 cases and 5727 controls, were analyzed. We detected significant associations between the IL10 -1082 G/G genotype and risk of breast cancer (AA + AG vs. GG: OR = 0.88, 95% CI = 0.80-0.97). Subgroup analyses confirmed a significant association in Caucasian populations (OR = 0.89, 95% CI = 0.80-0.99), in population-based case-control studies (OR = 0.87, 95% CI = 0.78-0.96), and in studies with ≥500 subjects (OR = 0.88, 95% CI = 0.79-0.99) under the recessive model (AA + AG vs. GG). No associations were found in Asian populations. Conclusions: The IL10 -1082A/G polymorphism is associated with an increased risk of breast cancer. The association between IL10 -1082 G/G genotype and increased risk of breast cancer is more significant in Caucasians, in population-based studies, and in larger studies

Development and validation of a dynamic nomogram based on conventional ultrasound and contrast-enhanced ultrasound for stratifying the risk of central lymph node metastasis in papillary thyroid carcinoma preoperatively

PurposeThe aim of this study was to develop and validate a dynamic nomogram by combining conventional ultrasound (US) and contrast-enhanced US (CEUS) to preoperatively evaluate the probability of central lymph node metastases (CLNMs) for patients with papillary thyroid carcinoma (PTC).MethodsA total of 216 patients with PTC confirmed pathologically were included in this retrospective and prospective study, and they were divided into the training and validation cohorts, respectively. Each cohort was divided into the CLNM (+) and CLNM (−) groups. The least absolute shrinkage and selection operator (LASSO) regression method was applied to select the most useful predictive features for CLNM in the training cohort, and these features were incorporated into a multivariate logistic regression analysis to develop the nomogram. The nomogram’s discrimination, calibration, and clinical usefulness were assessed in the training and validation cohorts.ResultsIn the training and validation cohorts, the dynamic nomogram (https://clnmpredictionmodel.shinyapps.io/PTCCLNM/) had an area under the receiver operator characteristic curve (AUC) of 0.844 (95% CI, 0.755–0.905) and 0.827 (95% CI, 0.747–0.906), respectively. The Hosmer–Lemeshow test and calibration curve showed that the nomogram had good calibration (p = 0.385, p = 0.285). Decision curve analysis (DCA) showed that the nomogram has more predictive value of CLNM than US or CEUS features alone in a wide range of high-risk threshold. A Nomo-score of 0.428 as the cutoff value had a good performance to stratify high-risk and low-risk groups.ConclusionA dynamic nomogram combining US and CEUS features can be applied to risk stratification of CLNM in patients with PTC in clinical practice

Risk factors for postpartum stress urinary incontinence: a prospective study

Abstract Purpose Postpartum stress urinary incontinence (SUI) is a common occurrence in women, and it has a profound effect on women’s health and quality of life. This study aimed to investigate the risk factors for postpartum SUI and the relative importance of each factor, including pelvic floor ultrasound measurement data and clinical data. Method Pregnant women who delivered in our hospital from March 2021 to January 2022 were selected as the study population. The clinical and anatomical Data from women with SUI and those without SUI were collected and analyzed. The clinical and anatomical risk factors associated with postpartum SUI were identified using univariate and multivariate analyses. Results A total of 255 participants were recruited. Logistic regression analysis indicated that age (OR:1.215, 95% CI:1.097–1.346, P < 0.001), vaginal delivery (OR:3.05, 95% CI:1.328–7.016, P < 0.009), parity (OR:3.059, 95% CI:1.506–6.216, P < 0.002), bladder neck descent (OR:4.159, 95% CI: 2.010–8.605, P < 0.001), the angle of the internal urethral orifice funnel (OR:1.133, 95% CI:1.091–1.176, P < 0.001) were important independent risk factors for postpartum SUI (all P < 0.05). The AUC was 0.883 (95% CI: 0.839–0.926) in the model. Conclusions Age, vaginal delivery, parity, bladder neck descent and the angle of the internal urethral orifice funnel are independent risk factors for postpartum SUI. To prevent the occurrence of postpartum SUI, high-risk factors of postpartum SUI should be identified as early as possible during pregnancy and after delivery, and postpartum pelvic floor rehabilitation training should be promoted

An AI model of sonographer's evaluation+ S-Detect + elastography + clinical information improves the preoperative identification of benign and malignant breast masses

PURPOSE: The purpose of the study was to build an AI model with selected preoperative clinical features to further improve the accuracy of the assessment of benign and malignant breast nodules. METHODS: Patients who underwent ultrasound, strain elastography, and S-Detect before ultrasound-guided biopsy or surgical excision were enrolled. The diagnosis model was built using a logistic regression model. The diagnostic performances of different models were evaluated and compared. RESULTS: A total of 179 lesions (101 benign and 78 malignant) were included. The whole dataset consisted of a training set (145 patients) and an independent test set (34 patients). The AI models constructed based on clinical features, ultrasound features, and strain elastography to predict and classify benign and malignant breast nodules had ROC AUCs of 0.87, 0.81, and 0.79 in the test set. The AUCs of the sonographer and S-Detect were 0.75 and 0.82, respectively, in the test set. The AUC of the combined AI model with the best performance was 0.89 in the test set. The combined AI model showed a better specificity of 0.92 than the other models. The sonographer's assessment showed better sensitivity (0.97 in the test set). CONCLUSION: The combined AI model could improve the preoperative identification of benign and malignant breast masses and may reduce unnecessary breast biopsies