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NBs combined with ultrasound demonstrated the ability to enhance the targeting of anticancer agents and improve the efficacy.

Purpose Early and accurate preoperative diagnosis of central lymph node metastasis (CLNM) is crucial to improve surgical management of patients with clinical lymph node-negative papillary thyroid carcinoma (PTC). Towards improving diagnosis of CLNM, we assessed the value of combining preoperative clinical characteristics, conventional ultrasound, and contrast-enhanced ultrasound (CEUS) in preoperative prediction of CLNM of different sized PTCs. Patients and Methods Patients were divided according to tumor size: a PTC group (>10 mm) and a papillary thyroid microcarcinoma (PTMC) group (≤10 mm). We retrospectively analyzed the clinical and ultrasonographic features of 120 PTC patients and 165 PTMC patients. Multivariate logistic regression analysis was used to screen independent risk factors and establish prediction models. Receiver operating characteristic curves were used to determine the best cut-off values for continuous variables and assess the performance of prediction models. Results Independent risk predictors of CLNM for the PTC group were extrathyroidal extension in CEUS (OR=7.923), tumor size >14 mm (OR=5.491), and multifocality (OR=3.235). For the PTMC group, the independent risk factors were the distance from the thyroid capsule =0 mm (OR=4.629), male (OR=3.315), tumor size >5 mm (OR=3.304), and microcalcification (OR=2.560). The predictive model of combined method had better performance in predicting CLNM of PTC compared with models based on CEUS and conventional ultrasound alone (area under the curve: 0.832 vs 0.739, P =0.0011; 0.832 vs 0.678, P =0.0012). For PTMC, comparing with CEUS, the combined method and conventional ultrasound performed better than CEUS alone in predicting CLNM (area under the curve: 0.783 vs 0.636, P =0.0016; 0.738 vs 0.636, P =0.0196). Conclusion The predictive models of combined method obtained from significant preoperative clinical and ultrasonographic features can potentially improve the preoperative diagnosis and individual treatment of CLNM in patients with PTC and PTMC. CEUS may be helpful in predicting CLNM of PTC, but CEUS would be ineffective in predicting CLNM of PTMC.

PR or irregular surface plaques were associated with artery-to-artery embolism. Superior location of plaques was associated with PAI. HR-MRI provides insights into intracranial atherosclerosis in vivo, predictive of infarction patterns.

Background Foetal vein of Galen aneurysmal malformation (VGAM) is a very rare congenital malformation of the cerebral blood vessels. We sought to evaluate the diagnostic value of ultrasound in combination with magnetic resonance imaging (MRI) in foetal VGAM. Case presentation Prenatal ultrasound combined with MRI diagnosed five cases of VGAM. Two dimensional ultrasound images were used to find the echo-free cystic structure below the thalamus and above the cerebellum with five cases. Colour blood flow showed dilated VGAM in five cases, while the arteriovenous spectrum was explored in two cases and foetal heart failure was found in other three cases. MRI was manifested as a dilated VGAM found at the midline of the brain, demonstrating widening or dilation of the straight sinus in four cases, ventricular dilatation in one case, brain parenchyma bleeding in two cases, and grey matter softening in one case. One infant died on the day of its birth, while the other four infants died within one month to six months after birth. Conclusions Ultrasound combined with MRI can more accurately and comprehensively observe the pathological characteristics of VGAM, diagnose related complications early and determine its prognosis.

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 contrastenhanced 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.

Background Accurate diagnosis of unexplained cervical lymphadenopathy (CLA) using medical images heavily relies on the experience of radiologists, which is even worse for CLA patients in underdeveloped countries and regions, because of lack of expertise and reliable medical history. This study aimed to develop a deep learning (DL) radiomics model based on B-mode and color Doppler ultrasound images for assisting radiologists to improve their diagnoses of the etiology of unexplained CLA. Methods Patients with unexplained CLA who received ultrasound examinations from three hospitals located in underdeveloped areas of China were retrospectively enrolled. They were all pathologically confirmed with reactive hyperplasia, tuberculous lymphadenitis, lymphoma, or metastatic carcinoma. By mimicking the diagnosis logic of radiologists, three DL sub-models were developed to achieve the primary diagnosis of benign and malignant, the secondary diagnosis of reactive hyperplasia and tuberculous lymphadenitis in benign candidates, and of lymphoma and metastatic carcinoma in malignant candidates, respectively. Then, a CLA hierarchical diagnostic model (CLA-HDM) integrating all sub-models was proposed to classify the specific etiology of each unexplained CLA. The assistant effectiveness of CLA-HDM was assessed by comparing six radiologists between without and with using the DL-based classification and heatmap guidance. Results A total of 763 patients with unexplained CLA were enrolled and were split into the training cohort (n=395), internal testing cohort (n=171), and external testing cohorts 1 (n=105) and 2 (n=92). The CLA-HDM for diagnosing four common etiologies of unexplained CLA achieved AUCs of 0.873 (95% CI: 0.838–0.908), 0.837 (95% CI: 0.789–0.889), and 0.840 (95% CI: 0.789–0.898) in the three testing cohorts, respectively, which was systematically more accurate than all the participating radiologists. With its assistance, the accuracy, sensitivity, and specificity of six radiologists with different levels of experience were generally improved, reducing the false-negative rate of 2.2–10% and the false-positive rate of 0.7–3.1%. Conclusions Multi-cohort testing demonstrated our DL model integrating dual-modality ultrasound images achieved accurate diagnosis of unexplained CLA. With its assistance, the gap between radiologists with different levels of experience was narrowed, which is potentially of great significance for benefiting CLA patients in underdeveloped countries and regions worldwide.