Autonomous robotic ultrasound scanning system: a key to enhancing image analysis reproducibility and observer consistency in ultrasound imaging.

Purpose: This study aims to develop an autonomous robotic ultrasound scanning system (auto-RUSS) pipeline, comparing its reproducibility and observer consistency in image analysis with physicians of varying levels of expertise.

Design/methodology/approach: An auto-RUSS was engineered using a 7-degree-of-freedom robotic arm, with real-time regulation based on force control and ultrasound visual servoing. Two phantoms were employed for the human-machine comparative experiment, involving three groups: auto-RUSS, non-expert (4 junior physicians), and expert (4 senior physicians).

Prediction of testicular histology in azoospermia patients through deep learning-enabled two-dimensional grayscale ultrasound.

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model.

Ultrasomics differentiation of malignant and benign focal liver lesions based on contrast-enhanced ultrasound.

Objectives: To establish a nomogram for differentiating malignant and benign focal liver lesions (FLLs) using ultrasomics features derived from contrast-enhanced ultrasound (CEUS).

Methods: 527 patients were retrospectively enrolled. On the training cohort, ultrasomics features were extracted from CEUS and b-mode ultrasound (BUS).

US-based Sequential Algorithm Integrating an AI Model for Advanced Liver Fibrosis Screening.

Background Noninvasive tests can be used to screen patients with chronic liver disease for advanced liver fibrosis; however, the use of single tests may not be adequate. Purpose To construct sequential clinical algorithms that include a US deep learning (DL) model and compare their ability to predict advanced liver fibrosis with that of other noninvasive tests. Materials and Methods This retrospective study included adult patients with a history of chronic liver disease or unexplained abnormal liver function test results who underwent B-mode US of the liver between January 2014 and September 2022 at three health care facilities.

Collaborative Enhancement of Consistency and Accuracy in US Diagnosis of Thyroid Nodules Using Large Language Models.

Background Large language models (LLMs) hold substantial promise for medical imaging interpretation. However, there is a lack of studies on their feasibility in handling reasoning questions associated with medical diagnosis. Purpose To investigate the viability of leveraging three publicly available LLMs to enhance consistency and diagnostic accuracy in medical imaging based on standardized reporting, with pathology as the reference standard.

Complementary Role of CEUS and CT/MR LI-RADS for Diagnosis of Recurrent HCC.

Purpose: We retrospectively compared the diagnostic performance of contrast-enhanced ultrasonography (CEUS) and contrast-enhanced computer tomography-magnetic resonance imaging (CT/MRI) for recurrent hepatocellular carcinoma (HCC) after curative treatment.

Materials And Methods: After curative treatment with 421 ultrasound (US) detected lesions, 303 HCC patients underwent both CEUS and CT/MRI. Each lesion was assigned a Liver Imaging Reporting and Data System (LI-RADS) category according to CEUS and CT/MRI LI-RADS.

Development and Assessment of Nomogram Based on AFP Response for Patients with Unresectable Hepatocellular Carcinoma Treated with Immune Checkpoint Inhibitors.

Background: Immune checkpoint inhibitors (ICIs) have been increasingly used to treat hepatocellular carcinoma (HCC). Prognostic biomarkers are an unmet need. We aimed to develop a prognostic nomogram for patients with unresectable HCC receiving ICIs therapy.

US LI-RADS in surveillance for recurrent hepatocellular carcinoma after curative treatment.

Objectives: To investigate the performance of US LI-RADS in surveillance for recurrent hepatocellular carcinoma (RHCC) after curative treatment.

Materials And Methods: This study enrolled 644 patients between January 2018 and August 2018 as a derivation cohort, and 397 patients from September 2018 to December 2018 as a validation cohort. The US surveillance after HCC curative treatment was performed.

Surveillance for malignant progression of LI-RADS version 2017 category 3/4 nodules using contrast-enhanced ultrasound.

Objectives: To identify the risk factors for predicting the malignant progression of LR-3/4 observations on the baseline and contrast-enhanced ultrasound (CEUS).

Methods: In total, 245 liver nodules assigned to LR-3/4 in 192 patients from January 2010 to December 2016 were followed up by baseline US and CEUS. The differences in the rate and time of progression to hepatocellular carcinoma (HCC) among subcategories (defined as P1-P7) of LR-3/4 in CEUS Liver Imaging Reporting and Data System (LI-RADS) were analyzed.

Integration of Artificial Intelligence Decision Aids to Reduce Workload and Enhance Efficiency in Thyroid Nodule Management.

Importance: To optimize the integration of artificial intelligence (AI) decision aids and reduce workload in thyroid nodule management, it is critical to incorporate personalized AI into the decision-making processes of radiologists with varying levels of expertise.

Objective: To develop an optimized integration of AI decision aids for reducing radiologists' workload while maintaining diagnostic performance compared with traditional AI-assisted strategy.

Design, Setting, And Participants: In this diagnostic study, a retrospective set of 1754 ultrasonographic images of 1048 patients with 1754 thyroid nodules from July 1, 2018, to July 31, 2019, was used to build an optimized strategy based on how 16 junior and senior radiologists incorporated AI-assisted diagnosis results with different image features.

Strategy for Accurate Diagnosis by Contrast-Enhanced Ultrasound of Focal Liver Lesions in Patients Not at High Risk for Hepatocellular Carcinoma: A Preliminary Study.

Objective: To develop an effective strategy for accurate diagnosis of focal liver lesions (FLLs) in patients with non-high risk for hepatocellular carcinoma (HCC).

Methods: From January 2012 to December 2015, consecutive patients with non-high risk for HCC who underwent contrast-enhanced ultrasound (CEUS) were included in this retrospective double-reader study. All patients were stratified into 2 different risks (intermediate, low-risk) groups according to criteria based on clinical characteristics, known as clinical risk stratification criteria.

Shear-wave elastography combined with contrast-enhanced ultrasound algorithm for noninvasive characterization of focal liver lesions.

Purpose: To establish shear-wave elastography (SWE) combined with contrast-enhanced ultrasound (CEUS) algorithm (SCCA) and improve the diagnostic performance in differentiating focal liver lesions (FLLs).

Material And Methods: We retrospectively selected patients with FLLs between January 2018 and December 2019 at the First Affiliated Hospital of Sun Yat-sen University. Histopathology was used as a standard criterion except for hemangiomas and focal nodular hyperplasia.

Identification of the origin of tumor in vein: comparison between CEUS LI-RADS v2017 and v2016 for patients at high risk.

Objectives: To compare the diagnostic performance of the Contrast-Enhanced Ultrasound (CEUS) Liver Imaging Report and Data System (LI-RADS) v2016 and v2017 in identifying the origin of tumor in vein (TIV).

Methods: From April 2014 to December 2018, focal liver lesions (FLLs) accompanied by TIV formation in patients at high risk for hepatocellular carcinoma (HCC) were enrolled. Histologic evaluation or composite imaging reference standard were served as the reference standard.

Circulating tumour cell counts and ultrasomics signature-based nomogram for preoperative prediction of early recurrence of hepatocellular carcinoma after radical treatment.

Methods: Between December 2017 and December 2018, 153 HCC patients (134 males and 19 females; mean age, 56.0 ± 10.2 years; range, 28-78 years) treated with radical therapy were enrolled in our retrospective study and were divided into a training cohort ( = 107) and a validation cohort ( = 46).

Preoperative prognostic value of alfa-fetoprotein density in patients with hepatocellular carcinoma undergoing radiofrequency ablation.

Objectives: To examine the prognostic value of preoperative alfa-fetoprotein (AFP) density and other clinical factors in patients undergoing percutaneous radiofrequency ablation (RFA) of hepatocellular carcinoma (HCC).

Methods: From January 2010 to December 2018, a total of 543 patients undergoing RFA for HCC meeting the Milan criteria were included at our institution. AFP density was calculated as absolute AFP pre-ablation divided by the total volume of all HCC lesions.

Application of the CT/MRI LI-RADS Treatment Response Algorithm to Contrast-Enhanced Ultrasound: A Feasibility Study.

Purpose: The contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) treatment response algorithm (TRA) is still in development. The aim of this study was to explore whether the CT/MRI LI-RADS TRA features were applicable to CEUS in evaluating the liver locoregional therapy (LRT) response.

Patients And Methods: This study was a retrospective review of a prospectively maintained database of patients with hepatocellular carcinoma undergoing ablation between July 2017 and December 2018.

Reproducibility of radiomics features from ultrasound images: influence of image acquisition and processing.

Objectives: To systematically assess the reproducibility of radiomics features from ultrasound (US) images during image acquisition and processing.

Materials And Methods: A standardized phantom was scanned to obtain US images. Reproducibility of radiomics features from US images, also known as ultrasomics features, was explored via (a) intra-US machine: changing the US acquisition parameters including gain, focus, and frequency; (b) inter-US machine: comparing three different scanners; (c) changing segmentation locations; and (d) inter-platform: comparing features extracted by the Ultrasomics and PyRadiomics algorithm platforms.

Differentiation between combined hepatocellular carcinoma and hepatocellular carcinoma: comparison of diagnostic performance between ultrasomics-based model and CEUS LI-RADS v2017.

Background: The imaging findings of combined hepatocellular cholangiocarcinoma (CHC) may be similar to those of hepatocellular carcinoma (HCC). CEUS LI-RADS may not perform well in distinguishing CHC from HCC. Studies have shown that radiomics has an excellent imaging analysis ability.

An assessment of liver lesions using a combination of CEUS LI-RADS and AFP.

Purpose: To improve noninvasive diagnosis of HCC using a combination of CE US LI-RADS and alpha-fetoprotein (AFP).

Methods: 757 solitary liver nodules from 757 patients at risk of HCC with CE US and serum AFP test were categorized as LR-1 to LR-5 through LR-M according to CE US LI-RADS version 2017. In LR-3, LR-4, and LR-M nodules, those with AFP > 200 ng/ml were reclassified as mLR-5.

LR-M Observations on Contrast-Enhanced Ultrasound: Detection of Hepatocellular Carcinoma Using Additional Features in Comparison With Current LI-RADS Criteria.

Contrast-enhanced ultrasound (CEUS) LI-RADS assigns category LR-M to observations that are definitely or probably malignant but that on imaging are not specific for hepatocellular carcinoma (HCC). A high percentage of LR-M observations represent HCC. The purpose of this study was to retrospectively evaluate the utility of additional features, beyond conventional LI-RADS major features, for detecting HCC among LR-M observations on CEUS.

Can monodisperse microbubble-based three-dimensional contrast-enhanced ultrasound reduce quantitative heterogeneity? An in vitro study.

Background: Heterogeneity within the tumor may cause large heterogeneity in quantitative perfusion parameters. Three-dimensional contrast-enhanced ultrasound (3D-CEUS) can show the spatial relationship of vascular structure after post-acquisition reconstruction and monodisperse bubbles can resonate the ultrasound pulse, resulting in the increase in sensitivity of CEUS imaging.

Objectives: To evaluate whether the combination of 3D-CEUS and monodisperse microbubbles could reduce the heterogeneity of quantitative CEUS.

Contrast-enhanced US diagnostic algorithm of hepatocellular carcinoma in patients with occult hepatitis B.

Purpose: To assess the diagnostic performance of contrast-enhanced (CE) US Liver Imaging Reporting and Data System (LI-RADS) version 2017 and propose a diagnostic algorithm in diagnosing hepatocellular carcinoma (HCC) in patients with occult HBV infection (OBI).

Methods: 251 OBI patients with 251 newly diagnosed focal liver lesions were retrospectively enrolled. Each nodule was evaluated according to CEUS LI-RADS.

Contrast-enhanced ultrasound-based ultrasomics score: a potential biomarker for predicting early recurrence of hepatocellular carcinoma after resection or ablation.

Objectives: This study aimed to construct a prediction model based on contrast-enhanced ultrasound (CEUS) ultrasomics features and investigate its efficacy in predicting early recurrence (ER) of primary hepatocellular carcinoma (HCC) after resection or ablation.

Methods: This study retrospectively included 215 patients with primary HCC, who were divided into a developmental cohort ( = 139) and a test cohort ( = 76). Four representative images-grayscale ultrasound, arterial phase, portal venous phase and delayed phase-were extracted from each CEUS video.