Development of a Novel Contrast-Enhanced Ultrasound-Based Nomogram for Superficial Lymphadenopathy Differentiation: Postvascular Phase Value.

OBJECTIVE: The aim of this study was to develop and prospectively validate a prediction model for superficial lymphadenopathy differentiation using Sonazoid contrast-enhanced ultrasound (CEUS) combined with ultrasound (US) and clinical data. METHODS: The training cohort comprised 260 retrospectively enrolled patients with 260 pathological lymph nodes imaged between January and December 2020. Two clinical US-CEUS models were created using multivariable logistic regression analysis and compared using receiver operating characteristic curve analysis: Model 1 included clinical and US characteristics; Model 2 included all confirmed predictors, including CEUS characteristics. Feature contributions were evaluated using the SHapley Additive exPlanations (SHAP) algorithm. Data from 172 patients were prospectively collected between January and May 2021 for model validation. RESULTS: Age, tumor history, long-axis diameter of lymph node, blood flow distribution, echogenic hilus, and the mean postvascular phase intensity (MPI) were identified as independent predictors for malignant lymphadenopathy. The area under the curve (AUC), sensitivity, specificity, and accuracy of MPI alone was 0.858 (95% confidence interval [CI], 0.817-0.891), 86.47%, 74.55%, and 81.2%, respectively. Model 2 had an AUC of 0.919 (95% CI, 0.879-0.949) and good calibration in training and validation cohorts. The incorporation of MPI significantly enhanced diagnostic capability (p < 0.0001 and p = 0.002 for training and validation cohorts, respectively). Decision curve analysis indicated Model 2 as the superior diagnostic tool. SHAP analysis highlighted MPI as the most pivotal feature in the diagnostic process. CONCLUSION: The employment of our straightforward prediction model has the potential to enhance clinical decision-making and mitigate the need for unwarranted biopsies.

Differential diagnosis of cervical lymphadenopathy: Integration of postvascular phase of contrast-enhanced ultrasound and predictive nomogram model.

BACKGROUND: Distinguishing benign from malignant cervical lymph nodes is critical yet challenging. This study evaluates the postvascular phase of contrast-enhanced ultrasound (CEUS) and develops a user-friendly nomogram integrating demographic, conventional ultrasound, and CEUS features for accurate differentiation. METHODS: We retrospectively analyzed 395 cervical lymph nodes from 395 patients between January 2020 and December 2022. The cohort was divided into training and validation sets using stratified random sampling. A predictive model, based on demographic, ultrasound, and CEUS features, was created and internally validated. RESULTS: The training set included 280 patients (130 benign, 150 malignant nodes) and the validation set 115 patients (46 benign, 69 malignant). Relative hypoenhancement in the postvascular phase emerged as a promising indicator for MLN, with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 96.7 %,52.3 %, 70.0 %, 93.2 %, and 76.1 %, respectively in the training set and 95.7 %, 52.2 %, 75.0 %, 88.9 %, and 74.8 % in the validation set. Age over 50 years, history of malignancy, short-axis diameter greater than 1.00 cm, focal hyperechogenicity, ill-defined borders, and centripetal perfusion were also identified as independent MLN indicators. The nomogram prediction model showed outstanding accuracy, with an area under the curve (AUC) of 0.922 (95 % CI: 0.892-0.953) in the training set and 0.914 (95 % CI: 0.864-0.963) in the validation set. CONCLUSION: Relative hypoenhancement in the postvascular phase of CEUS, combined with demographics and ultrasound features, is effective for identifying MLNs. The developed prediction model, with a user-friendly nomogram, can facilitate clinical decision-making.

Cascade Drug Delivery through Tumor Barriers of Pancreatic Cancer via Ultrasound in Combination with Functional Microbubbles.

The abundant desmoplastic stroma and the lack of sufficient targets on pancreatic cancer cells render poor drug penetration and cellular uptake, which significantly compromise the chemotherapy efficacy. Herein, we reported a three-step cascade delivery strategy for selective delivery of paclitaxel (PTX) to achieve a targeted therapy for pancreatic cancer. cRGD and cCLT1 peptides, which could target the integrin and fibronectin, respectively, overexpressed in pancreatic cancer cells and stroma, were decorated on PTX-loaded microbubbles, resulting in the formation of dual-targeting PTX-RCMBs. In this strategy, ultrasound in combination with PTX-RCMBs first enhanced the permeability of tumor vessels via cavitation effects and simultaneously helped the generated PTX-RCNPs penetrate into the stroma. Then, the cCLT1 peptide modified on PTX-RCNPs selectively bound the fibronectin highly expressed in the stroma and later targeted the integrin (α5ß1) on the cell surface. Finally, another targeting cRGD peptide modified on PTX-RCNPs would further promote PTX uptake via targeting the integrin (αvß3) on the cell surface. This strategy significantly increased the delivery of PTX into tumor tissues. Moreover, the in vivo effective accumulation of PTX was monitored by ultrasound and fluorescence bimodal imaging. The tumor growth inhibition was investigated on subcutaneous tumor mouse models with 89.8% growth inhibition rate during 21 days of treatment, showing great potential for improving pancreatic cancer therapy.