Sentinel lymph node metastasis (SLNM) is a crucial predictor for breast cancer treatment and survival. This study was designed to propose deep learning (DL) models based on grayscale ultrasound, color Doppler flow imaging (CDFI), and elastography images, and to evaluate how DL radiomics can be used to classify SLNM in breast cancer. Clinical and ultrasound data of 317 patients diagnosed with breast cancer at the Second Affiliated Hospital of Nanchang University were collected from January 2018 to December 2021 and randomly divided into training and internal validation cohorts at a ratio of 7:3. An external validation cohort comprising data from Nanchang Third Hospital with 42 patients collected. Three DL models, namely DL-grayscale, DL-CDFI, and DL-elastography, were proposed to predict SLNM by analyzing grayscale ultrasound, CDFI, and elastography images. Three DL models were compared and evaluated to assess diagnostic performance based on the area under the curve (AUC). The AUCs of the DL-grayscale were 0.855 and 0.788 in the internal and external validation cohorts, respectively. For the DL-CDFI model, the AUCs were 0.761 and 0.728, respectively. The diagnostic performance of DL-elastography was superior to that of the DL-grayscale and DL-CDFI. The AUC of the DL-elastography model was 0.879 in the internal validation cohort, with a classification accuracy of 86.13%, sensitivity of 91.60%, and specificity of 82.79%. The generalization capability of DL-elastography remained high in the external cohort, with an AUC of 0.876, and an accuracy of 85.00%. DL radiomics can be used to classify SLNM in breast cancer using ultrasound images. The proposed DL-elastography model based on elastography images achieved the best diagnostic performance and holds good potential for the management of patients with SLNM.

Left atrial strain measured by 4D Auto LAQ echocardiography is significantly correlated with high risk of thromboembolism in patients with non-valvular atrial fibrillation Lili Chen, Chunquan Zhang, Jiangtao Wang, Liangyun Guo, Xiaolin Wang, Fengzhen Liu, Xia Li, Yu Zhao

A multiparametric US predictive model combining attenuation coefficient, alanine aminotransferase level, and international normalized ratio showed good ability to predict metabolic dysfunction–asso... Background Noninvasive evaluation of metabolic dysfunction–associated fatty liver disease (MAFLD) with multiparametric US is essential, but multicenter studies are lacking. Purpose To evaluate the ...

The comovirus Bean pod mottle virus (BPMV) is widespread in the soybean-growing regions in the United States. It has a bipartite genome consisting of RNA1 and RNA2, which are encapsidated separately. We previously have reported the occurrence in nature of two distinct subgroups of BPMV strains (subgroups I and II), as well as reassortants between the two subgroups. Here, we report the isolation and molecular characterization of naturally occurring partial diploid reassortant strains, which are diploid for RNA1 and haploid for RNA2. Whereas the RNA1s of the partial diploids are derived from two distinct strain subgroups (I and II), the RNA2 is derived from either subgroup I or II. The partial diploid strains induced strikingly severe symptoms on soybean, which could be explained based on the presence of two distinct RNA1s in the same plant. This conclusion was supported by the finding that pseudo-recombinants constructed with two diverse RNA1s induced very severe symptoms on soybean that mimicked those produced by the naturally occurring partial diploids. No enhancement of symptom severity was observed with pseudorecombinants constructed with closely related RNA1s. Likewise, no enhancement of symptom severity was noted with pseudo-recombinants that are diploid for RNA2 and haploid for RNA1. The potential role of genetic reassortment in the epidemiology and pathogenesis of BPMV is discussed.