Search

Abstract The diagnostic accuracy of clinically significant prostate cancer (csPCa) of Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) is limited by subjectivity in result interpretation and the false positive results from certain similar anatomic structures. We aimed to establish a new model combining quantitative contrast-enhanced ultrasound, PI-RADSv2, clinical parameters to optimize the PI-RADSv2-based model. The analysis was conducted based on a data set of 151 patients from 2019 to 2022, multiple regression analysis showed that prostate specific antigen density, age, PI-RADSv2, quantitative parameters (rush time, wash-out area under the curve) were independent predictors. Based on these predictors, we established a new predictive model, the AUCs of the model were 0.910 and 0.879 in training and validation cohort, which were higher than those of PI-RADSv2-based model (0.865 and 0.821 in training and validation cohort). Net Reclassification Index analysis indicated that the new predictive model improved the classification of patients. Decision curve analysis showed that in most risk probabilities, the new predictive model improved the clinical utility of PI-RADSv2-based model. Generally, this new predictive model showed that quantitative parameters from contrast enhanced ultrasound could help to improve the diagnostic performance of PI-RADSv2 based model in detecting csPCa.

Abstract Ovarian cancer, a group of heterogeneous diseases, presents with extensive characteristics with the highest mortality among gynecological malignancies. Accurate and early diagnosis of ovarian cancer is of great significance. Here, we present OvcaFinder, an interpretable model constructed from ultrasound images-based deep learning (DL) predictions, Ovarian–Adnexal Reporting and Data System scores from radiologists, and routine clinical variables. OvcaFinder outperforms the clinical model and the DL model with area under the curves (AUCs) of 0.978, and 0.947 in the internal and external test datasets, respectively. OvcaFinder assistance led to improved AUCs of radiologists and inter-reader agreement. The average AUCs were improved from 0.927 to 0.977 and from 0.904 to 0.941, and the false positive rates were decreased by 13.4% and 8.3% in the internal and external test datasets, respectively. This highlights the potential of OvcaFinder to improve the diagnostic accuracy, and consistency of radiologists in identifying ovarian cancer.

Abstract Background Primary prostate cancer with metastasis has a poor prognosis, so assessing its risk of metastasis is essential. Methods This study combined comprehensive ultrasound features with tissue proteomic analysis to obtain biomarkers and practical diagnostic image features that signify prostate cancer metastasis. Results In this study, 17 ultrasound image features of benign prostatic hyperplasia (BPH), primary prostate cancer without metastasis (PPCWOM), and primary prostate cancer with metastasis (PPCWM) were comprehensively analyzed and combined with the corresponding tissue proteome data to perform weighted gene co-expression network analysis (WGCNA), which resulted in two modules highly correlated with the ultrasound phenotype. We screened proteins with temporal expression trends based on the progression of the disease from BPH to PPCWOM and ultimately to PPCWM from two modules and obtained a protein that can promote prostate cancer metastasis. Subsequently, four ultrasound image features significantly associated with the metastatic biomarker HNRNPC (Heterogeneous nuclear ribonucleoprotein C) were identified by analyzing the correlation between the protein and ultrasound image features. The biomarker HNRNPC showed a significant difference in the five-year survival rate of prostate cancer patients (p

Objective Gastric cancer (GC), one of the highest venous thromboembolism (VTE) incidence rates in cancer, contributes to considerable morbidity, mortality, and, prominently, extra cost. However, up to now, there is not a high-quality VTE model to steadily predict the risk for VTE in China. Consequently, setting up a prediction model to predict the VTE risk is imperative. Methods Data from 3,092 patients from December 15, 2017, to December 31, 2022, were retrospectively analyzed. Multiple logistic regression analysis was performed to assess risk factors for GC, and a nomogram was constructed based on screened risk factors. A receiver operating curve (ROC) and calibration plot was created to evaluate the accuracy of the nomogram. Results The risk factors of suffering from VTE were older age (OR = 1.02, 95% CI [1.00–1.04]), Karnofsky Performance Status (KPS) ≥ 70 (OR = 0.45, 95% CI [0.25–0.83]), Blood transfusion (OR = 2.37, 95% CI [1.47–3.84]), advanced clinical stage (OR = 3.98, 95% CI [1.59–9.99]), central venous catheterization (CVC) (OR = 4.27, 95% CI [2.03–8.99]), operation (OR = 2.72, 95% CI [1.55–4.77]), fibrinogen degradation product (FDP) >5 µg/mL (OR = 1.92, 95% CI [1.13–3.25]), and D-dimer > 0.5 mg/L (OR = 2.50, 95% CI [1.19–5.28]). The area under the ROC curve (AUC) was 0.82 in the training set and 0.85 in the validation set. Conclusion Our prediction model can accurately predict the risk of the appearance of VTE in gastric cancer patients and can be used as a robust and efficient tool for evaluating the possibility of VTE.

Background Ovarian cancer seriously threatens the lives and health of women, and early diagnosis and treatment are still challenging. Pre-targeting is a promising strategy to improve the treatment efficacy of ovarian cancer and the results of ultrasound imaging. Purpose To explore the effects of a pre-targeting strategy using streptavidin (SA) and paclitaxel (PTX)-loaded phase-shifting poly lactic-co-glycolic acid (PLGA) nanoparticles with perfluoro-n-pentane (PTX-PLGA-SA/PFPs) on the treatment and ultrasound imaging of ovarian cancer. Methods PTX-PLGA/PFPs were prepared with a single emulsion (O/W) solvent evaporation method and SA was attached using carbodiimide. The encapsulation efficiency of PTX and the release characteristics were assessed with high performance liquid chromatography. The phase-change characteristics of the PTX-PLGA-SA/PFPs were investigated. The anti-carcinoembryonic antigen (CEA) antibody (Ab) was covalently attached to PTX-PLGA/PFPs via carbodiimide to create PTX-PLGA-Ab/PFPs. The targeting efficiency of the nanoparticles and the viability of ovarian cancer SKOV3 cells were evaluated in each group using a microscope, flow cytometry, and cell counting kit 8 assays. Results THE PTX-PLGA-SA/PFPs were spheres with a size of 383.0 ± 75.59 nm. The encapsulation efficiency and loading capability of the nanoparticles for PTX were 71.56 ± 6.51% and 6.57 ± 0.61%, respectively. PTX was burst-released up to 70% in 2–3 d. When irradiated at 7.5 W for 3 min, the PTX-PLGA-SA/PFPs visibly enhanced the ultrasonography images (P

In order to better monitor non-alcoholic fatty liver disease (NAFLD) patients at higher risk for HCC, there is a need for non-invasive diagnostic approaches to screen for the presence of advanced fibrosis in these patients. The aim of this systematic review and meta-analysis will be to evaluate the diagnostic efficacy of ARFI elastography in detecting hepatic fibrosis in NAFLD patients.Relevant studies were identified from systematic searches of several major electronic databases (PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials). The primary outcomes were the summary sensitivity, summary specificity, the diagnostic odds ratio, and the summary receiver operating characteristic curve (SROC) of ARFI elastography in detecting significant fibrosis (defined as 4>F≥2) in NAFLD patients. Study quality was assessed using the Quality Assessment of Studies of Diagnostic Accuracy included in Systematic Review (QUADAS-2).The summary sensitivity and specificity of ARFI in detecting significant fibrosis were 80.2% (95% confidence interval (CI): 0.758-0.842; p = 0.0000) and 85.2% (95% CI: 0.808-0.890), p = 0.1617), respectively. The pooled diagnostic odds ratio of ARFI in detecting significant fibrosis was 30.13 (95% CI: 12.08-75; chi-squared = 14.59, p = 0.0237). The area under the SROC curve (AUC) was 0.898 (standard error (SE): 0.031) with a Q* index of 0.830 (SE: 0.033).ARFI elastography appears to be modestly accurate in detecting significant fibrosis in NAFLD patients. Future studies in this field should provide head-to-head comparisons of ARFI elastography versus other elastographic imaging modalities in NAFLD patients.