A prospective study of two-dimensional ultrasonography combined with shear wave elastography for pregnancy-related diastasis recti abdominis.

Objectives: To compare the inter-rectus distance (IRD), rectus abdominis thickness (RAT), and stiffness in women during pregnancy and postpartum and identify the risk and protective factors affecting diastasis recti abdominis (DRA). Materials and methods: A total of 171 pregnant women who volunteered to participate in this study were recruited. Using an ultrasonographic diagnostic instrument with shear wave elastography function, IRD, RAT and the Young's modulus of the rectus abdominis muscles were measured at 12 weeks, 37 weeks of pregnancy, and 6 weeks postpartum. Results: The IRD at 37 weeks was significantly higher than that at 12 weeks and then decreased at 6 weeks postpartum, but it was still higher than that at 12 weeks (p < 0.001). RAT and Young's modulus decreased significantly at 37 weeks compared with those at 12 weeks and then recovered at 6 weeks postpartum, but they were lower than those at 12 weeks (p < 0.001). IRD at 12 weeks was significantly higher in multiparae than in primiparae (p < 0.001). Moreover, positive correlation between the RAT and Young's modulus of rectus abdominis muscles at 12 and 37 weeks of gestation and 6 weeks postpartum (p < 0.001) was observed. Multiple linear regression analysis showed that the regression equation was significant (f = 24.856, p < 001). Conclusion: Our study identified differences in IRD, thickness and stiffness of the rectus abdominis muscle between early and advanced pregnancy and the postpartum period. The risk and protective factors of DRA may guide pregnant women's protection and treatment.

Prediction of local tumor progression after microwave ablation for early-stage hepatocellular carcinoma with machine learning.

Objectives: Local tumor progression (LTP) is a major constraint for achieving technical success in microwave ablation (MWA) for the treatment of early-stage hepatocellular carcinoma (EHCC). This study aims to develop machine learning (ML)-based predictive models for LTP after initial MWA in EHCC. Materials and Methods: A total of 607 treatment-naïve EHCC patients (mean ± standard deviation [SD] age, 57.4 ± 10.8 years) with 934 tumors according to the Milan criteria who subsequently underwent MWA between August 2009 and January 2016 were enrolled. During the same period, 299 patients were assigned to the external validation datasets. To identify risk factors of LTP after MWA, clinicopathological data and ablation parameters were collected. Predictive models were developed according to 21 variables using four ML algorithms and evaluated based on the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CIs). Results: After a median follow-up time of 28.7 months (range, 7.6-110.5 months), 6.9% (42/607) of patients had confirmed LTP in the training dataset. The tumor size and number were significantly related to LTP. The AUCs of the four models ranged from 0.791 to 0.898. The best performance (AUC: 0.898, 95% CI: [0.842 0.954]; SD: 0.028) occurred when nine variables were introduced to the CatBoost algorithm. According to the feature selection algorithms, the top six predictors were tumor number, albumin and alpha-fetoprotein, tumor size, age, and international normalized ratio. Conclusions: Out of the four ML models, the CatBoost model performed best, and reasonable and precise ablation protocols will significantly reduce LTP.

Diagnostic value of thyroid micronodules with high b-value diffusion weighted imaging: Comparative study with high-resolution ultrasound.

PURPOSE: This study aims to compare the diagnostic performance of two imaging methods for thyroid nodules ≤1.0 cm and reduce unnecessary overdiagnosis. METHODS: A retrospective study was conducted on 80 patients with pathologically confirmed solitary thyroid micronodules underwent both high-resolution ultrasound (HRUS) and High b-value (2000 s/mm2) diffusion weighted imaging (DWI). Intra- and interobserver agreement (Intraclass correlation coefficient) was followed by Kruskal-Wallis test to detect whether the quantitative apparent diffusion coefficient (ADC) and thyroid nodule subgroups were related. Cohen's kappa analysis was applied to assess the interobserver consistency of DWI and HRUS characteristics. The receiver operating characteristic curves were adopted for evaluating the diagnostic performance of thyroid malignancy. The sensitivity, specificity, and accuracy of the two imaging methods were compared using the McNemar's test and Kappa test. RESULTS: A total of 80 patients were included, consisting of 43 malignant and 37 benign micronodules. The sensitivity, specificity and accuracy of DWI combined with rADC (ADCmin to ADCn ratio) for the diagnosis of thyroid micronodules were 83.7%, 89.2% and 86.3%, respectively. The area under the curve (AUC) was 0.91 (95% confidence interval [CI]: 0.84-0.97). The sensitivity, specificity and accuracy of HRUS diagnosis were 100%, 62.16% and 82.5%, respectively. CONCLUSION: High b-value DWI is superior to HRUS for evaluating the diagnostic performance of solid thyroid micronodules. DWI and its ADC quantitative analysis could be added to the evaluation of thyroid micronodules to improve the specificity of diagnosis, reduce overdiagnosis and avoid unnecessary biopsies or surgeries.