OBJECTIVE: To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes.
METHODS: After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC).
RESULTS: Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value < 0.05) using the Gradient Boosting classifier.
CONCLUSIONS: Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.

OBJECTIVE: Ultrasound (US) technology has recently made advances that have led to the development of modalities including elastography and contrast-enhanced ultrasound. The use of different US modalities in combination may increase the accuracy of PCa diagnosis. This study aims to assess the diagnostic accuracy of multiparametric ultrasound (mpUS) in the PCa diagnosis.
METHODS: Through September 2023, we searched through Cochrane CENTRAL, PubMed, Embase, Scopus, Web of Science, ClinicalTrial.gov, and Google Scholar for relevant studies. We used standard methods recommended for meta-analyses of diagnostic evaluation. We plot the SROC curve, which stands for summary receiver operating characteristic. To determine how confounding factors affected the results, meta-regression analysis was used.
RESULTS: Finally, 1004 patients from 8 studies that were included in this research were examined. The diagnostic odds ratio for PCa was 20 (95% confidence interval (CI), 8-49) and the pooled estimates of mpUS for diagnosis were as follows: sensitivity, 0.88 (95% CI, 0.81-0.93); specificity, 0.72 (95% CI, 0.59-0.83); positive predictive value, 0.75 (95% CI, 0.63-0.87); and negative predictive value, 0.82 (95% CI, 0.71-0.93). The area under the SROC curve was 0.89 (95% CI, 0.86-0.92). There was a significant heterogeneity among the studies (p < 0.01). According to meta-regression, both the sensitivity and specificity of mpUS in the diagnosis of clinically significant PCa (csPCa) were inferior to any PCa.
CONCLUSIONS: The diagnostic accuracy of mpUS in the diagnosis of PCa is moderate, but the accuracy in the diagnosis of csPCa is significantly lower than any PCa. More relevant research is needed in the future.
UNASSIGNED: This study provides urologists and sonographers with useful data by summarizing the accuracy of multiparametric ultrasound in the detection of prostate cancer.
CONCLUSIONS: • Recent studies focused on the role of multiparametric ultrasound in the diagnosis of prostate cancer. • This meta-analysis revealed that multiparametric ultrasound has moderate diagnostic accuracy for prostate cancer. • The diagnostic accuracy of multiparametric ultrasound in the diagnosis of clinically significant prostate cancer is significantly lower than any prostate cancer.

Hepatic acute graft-versus-host disease (aGVHD) is a serious complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT) and is one of the leading causes of early non-recurrent death. The current diagnosis is based mainly based on clinical diagnosis, and there is a lack of non-invasive quantitative diagnosis methods. We propose a multiparametric ultrasound (MPUS) imaging method and explore its effectiveness in evaluating hepatic aGVHD.
In this study, 48 female Wistar rats were used as receptors and 12 male Fischer 344 rats were used as donors for allo-HSCT to establish aGVHD models. After transplantation, 8 rats were randomly selected for ultrasonic examination weekly, including color Doppler ultrasound, contrast-enhanced ultrasound (CEUS) and shear wave dispersion (SWD) imaging. The values of nine ultrasonic parameters were obtained. Hepatic aGVHD was subsequently diagnosed by histopathological analysis. A classification model for predicting hepatic aGVHD was established using principal component analysis and support vector machines.
According to the pathological results, the transplanted rats were categorized into the hepatic aGVHD and non-GVHD (nGVHD) groups. All parameters obtained by MPUS differed statistically between the two groups. The first three contributing percentages of principal component analysis results were resistivity index, peak intensity and shear wave dispersion slope, respectively. The accuracy of classifying aGVHD and nGVHD using support vector machines reached 100%. The accuracy of the multiparameter classifier was significantly higher than that of the single parameter.
The MPUS imaging method has proven to be useful in detecting hepatic aGVHD.

OBJECTIVE: To evaluate the combined efficacy of multiparametric ultrasonography (mpUS) and multiparametric magnetic resonance imaging/transrectal ultrasound (mpMRI-TRUS) fusion for detecting clinically significant prostate cancer (csPCa).
METHODS: From November 2019 to September 2021, biopsy-naïve patients underwent mpMRI-TRUS fusion imaging combined with mpUS-guided targeted biopsies (TB) and systematic biopsies (SB). To further evaluate the additional diagnostic value of mpUS, the imaging features of 202 focus obtained from fusion imaging were assessed. The diagnostic accuracies of mpMRI-TRUS fusion imaging and the combination of mpMRI-TRUS fusion imaging with mpUS for csPCa were comparatively evaluated.
RESULTS: A total of 202 prostate lesions (160 patients) were included in the final analysis, of which 105 were csPCa, 16 were ciPCa, and 81 were noncancerous. The median patient age was 69 (65-73) years and the median tPSA was 22.07 (11.22-62.80) ng/mL. For csPCa, the detection rate of TB was higher than that of SB (50.0% vs. 45.5%, p < 0.05). The imaging characteristics of mpUS in the PCa and non-PCa groups were significantly different (p < 0.001). When compared with mpMRI-TRUS fusion imaging, the positive predictive value, false positive rate, and area under the curve (AUC) of csPCa diagnosis by mpMRI-TRUS fusion imaging combined with mpUS increased by 11.30%, decreased by 19.58%, and increased from 0.719 to 0.770 (p < 0.05), respectively.
CONCLUSIONS: TB can improve the detection rate of csPCa and hence can be effectively used in the diagnosis and risk assessment of csPCa. The mpUS-enriched valuable diagnostic information for mpMRI-TRUS fusion imaging and their combination showed a higher diagnostic value for csPCa, which can guide subsequent clinical treatment.

Non-alcoholic fatty liver disease (NAFLD) affects almost one quarter of adults worldwide, and its progressive subtype, non-alcoholic steatohepatitis can progress to advanced fibrosis/cirrhosis and even hepatocellular carcinoma. It is critical to screen and grade NAFLD patients for management decisions to rationalize the utilization of medical resources. Conventional ultrasound is widely applied for NAFLD screening, however, some inherent weaknesses hinder its utility. This limitation has spurred the development of acoustic parameters-based quantitative ultrasound techniques that allow a more accurate evaluation of the histological features of NAFLD (e.g. steatosis, necroinflammation, fibrosis/cirrhosis). Herein, this paper reviews the research advances in emerging ultrasound techniques for screening and surveillance across NAFLD spectrum and summarize their principles, feasibility, accuracy, reproducibility, and limitations of each technique. The challenges and future directions are also discussed to advance clinical practice.

UNASSIGNED: To evaluate the potential of a clinical-based model, a multiparametric ultrasound-based radiomics model, and a clinical-radiomics combined model for predicting prostate cancer (PCa).
UNASSIGNED: A total of 112 patients with prostate lesions were included in this retrospective study. Among them, 58 patients had no prostate cancer detected by biopsy and 54 patients had prostate cancer. Clinical risk factors related to PCa (age, prostate volume, serum PSA, etc.) were collected in all patients. Prior to surgery, patients received transrectal ultrasound (TRUS), shear-wave elastography (SWE) and TRUS-guided prostate biopsy. We used the five-fold cross-validation method to verify the results of training and validation sets of different models. The images were manually delineated and registered. All modes of ultrasound radiomics were retrieved. Machine learning used the pathology of \"12+X\" biopsy as a reference to draw the benign and malignant regions of interest (ROI) through the application of LASSO regression. Three models were developed to predict the PCa: a clinical model, a multiparametric ultrasound-based radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared by receiver operating characteristic curve (ROC) analysis and decision curve.
UNASSIGNED: The multiparametric ultrasound radiomics reached area under the curve (AUC) of 0.85 for predicting PCa, meanwhile, AUC of B-mode radiomics and SWE radiomics were 0.74 and 0.80, respectively. Additionally, the clinical-radiomics combined model (AUC: 0.90) achieved greater predictive efficacy than the radiomics model (AUC: 0.85) and clinical model (AUC: 0.84). The decision curve analysis also showed that the combined model had higher net benefits in a wide range of high risk threshold than either the radiomics model or the clinical model.
UNASSIGNED: Clinical-radiomics combined model can improve the accuracy of PCa predictions both in terms of diagnostic performance and clinical net benefit, compared with evaluating only clinical risk factors or radiomics score associated with PCa.