Echocardiography Diagnosis of Mitral Valve Aneurysm Complicated with Infective Endocarditis.

Mitral valve aneurysm (MVA) is a relatively rare but life-threatening condition that may occur as a complication of an infective endocarditis (IE) involved aortic valve. Rupture of the valve aneurysm is one of the most serious complications, which could result in severe mitral regurgitation and cause rapid hemodynamic deterioration, especially in heart failure patients. Timely diagnosis using echocardiography and appropriate treatments, such as invasive surgical repair or replacement of the valve, can effectively prevent catastrophic complications. Here, we present a 57-year-old male patient with MVA after IE and emphasize the key role of echocardiography in the early diagnosis and management of these kinds of conditions.

Ultrasound features combined with tumor-infiltrating lymphocytes for prediction of pathological response to neoadjuvant chemotherapy in breast cancer.

AIM: Neoadjuvant chemotherapy (NAC) plays an important role in the treatment and prognosis of breast cancer. The early identification of patients who can truly benefit from preoperative NAC is crucial in clinical practice. The purpose of this study was to explore whether the ultrasound features and clinical characteristics combined with tumor-infiltrating lymphocyte(TIL) levels can improve the performance of predicting NAC efficacy in breast cancer patients. MATERIAL AND METHODS: In this retrospective study, 202 invasive breast cancer patients who underwent NAC followed by surgery were included. The baseline ultrasound features were reviewed by two radiologists. Miller-Payne Grading (MPG) was used to assess pathological response, and MPG 4-5 was defined as major histologic responders (MHR). Multivariable logistic regression analysis was used to evaluate independent predictors for MHR and build the prediction models. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the models. RESULTS: Of the 202 patients, 104 patients achieved MHR and 98 patients achieved non-MHR. Multivariate logistic regression analysis showed the US size (p=0.042), molecular subtypes (p=0.001), TIL levels (p<0.001), shape (p=0.030), and posterior features (p=0.018) were independent predictors for MHR. The model combined the US features, clinical characteristics, and TIL levels had a better performance with an area under the curve (AUC) of 0.811, a sensitivity of 0.663, and a specificity of 0.847. CONCLUSION: The model combined US features, clinical characteristics, and TIL levels had a better performance in predicting pathological response to NAC in breast cancer.

Ultrasound contrast-enhanced patterns of sentinel lymph nodes: predictive value for nodal status and metastatic burden in early breast cancer.

Background: In the post-Z0011 era, sentinel lymph node (SLN) status and metastatic burden determine whether axillary management entails conservative sentinel lymph node biopsy (SLNB) or radical axillary lymph node dissection (ALND) in breast cancer patients. However, SLN status and metastatic burden cannot be evaluated preoperatively in clinical practice. This study explored the predictive value of contrast-enhanced ultrasound (CEUS) patterns of SLN to assess the nodal status and metastatic burden in early breast cancer patients. Methods: A retrospective study was conducted on 88 consecutive patients who were diagnosed with clinical T1-2N0 breast cancer between December 2020 and November 2021 at the Lanzhou University Second Hospital and scheduled for SLNB. Preoperative CEUS was performed to confirm the location and enhancement pattern of the SLN, and the conventional ultrasonic characteristics of the primary breast lesions and SLN were recorded. Intraoperative localized SLN and postoperative pathological results were used as the gold standard for comparison with preoperative ultrasound findings. Results: CEUS successfully identified at least 1 SLN in 88 patients, with a total of 118 SLNs identified in the entire cohort. Univariate analysis showed that lesion size, blood flow grade, SLN longitudinal diameter, cortical thickness, and enhancement pattern were significant predictive features of SLN metastasis. Further multiple regression analysis indicated that the enhancement pattern of the SLN was an independent risk factor for SLN metastasis, with a sensitivity and a specificity of 84.2% (32/38) and 80.0% (40/50), respectively. Meanwhile, the SLN enhancement pattern could predict the lymph node metastasis burden (P<0.001). In patients presenting with a type I (homogeneous enhancement) or type II (heterogeneous enhancement) SLN, 91.5% (65/71) had ≤2 positive SLNs, whereas in patients with a type III (no enhancement) SLN, 70.6% (12/17) had >2 metastatic nodes. Conclusions: The contrast-enhanced pattern of the SLN is an independent risk factor for SLN status. Patients presenting with a type I or type II SLN enhanced pattern are unlikely to have high-burden metastases detected at their final surgical treatment and omission of ALND may be appropriate.

Predictive value of combining clinicopathological, multimodal ultrasonic characteristics in axillary lymph nodal metastasis burden of patients with cT1-2N0 breast cancer.

OBJECTIVE: To improving diagnosis of axillary lymph node metastasis (ALNM), we assessed the value of combining clinicopathological, conventional ultrasound, SWE features in the cT1-2N0 breast cancer patients. METHODS: Retrospective analysis of 285 patients with cT1-2N0 breast cancer who underwent preoperative ultrasound examination of the lesion and axillary, with shear wave elastography (SWE) of the lesions. According to the postoperative pathological results, they were divided into ≤2 metastatic ALNs group (low nodal burden, LNB) and > 2 metastatic ALNs group (high nodal burden, HNB). Binary logistic regression analysis was used to screen independent risk factors and establish prediction models. The best cut-off value of continuous variables is determined by the receiver operating characteristic curve, and the performance of the prediction model is evaluated. RESULTS: Presence of lymphovascular invasion (OR = 7.966, P = 0.010), tumor size (OR = 2.485, P = 0.019), Emean of intratumor (OR = 0.939, P = 0.002) and cortical thickness of lymph node (OR = 9.277, P < 0.001) were independent risk predictors for HNB of cT1-2N0 Group. The predictive model of combined method had better performance in predicting HNB of cT1-2N0 compared with models based on SWE and conventional ultrasound alone (area under the curve: 0.824 vs 0.658, P < 0.001; 0.824 vs 0.789, P = 0.035). CONCLUSIONS: The predictive models of combined method obtained from significant clinicopathological and ultrasonographic features can potentially improve the diagnosis and individual treatment of ALNM in patients with cT1-2N0 breast cancer.

Ultrasound-based radiomics: current status, challenges and future opportunities.

Ultrasound (US) imaging is part of conventional medical imaging in clinical practice that is low-cost, non-ionizing, portable and capable of real-time image acquisition and display. However, in certain cases, US has limited sensitivity and specificity in differentiating between malignant and benign lesions. Ultrasound-based radiomics, as a new branch of radiomics, can provide additional features such as heterogeneity of lesions that are invisible to the naked eye, alone or in combination with demographic, histological, genomic or proteomic data, thereby improving the accuracy of US in diagnosis of disease. This article provides an introduction to ultrasound-based radiomics, covering its workflow, the application of machine learning, and current research status. Current limitations of radiomics, such as consistency of image acquisition, parameter variations, and difficulty in calibrating quantitative methods in ultrasound, will also be covered.