A multi-parameter intrahepatic cholangiocarcinoma scoring system based on modified contrast-enhanced ultrasound LI-RADS M criteria for differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma.

PURPOSE: To develop a multi-parameter intrahepatic cholangiocarcinoma (ICC) scoring system and compare its diagnostic performance with contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system M (LR-M) criteria for differentiating ICC from hepatocellular carcinoma (HCC). METHODS: This retrospective study enrolled 62 high-risk patients with ICCs and 62 high-risk patients with matched HCCs between January 2022 and December 2022 from two institutions. The CEUS LR-M criteria was modified by adjusting the early wash-out onset (within 45 s) and the marked wash-out (within 3 min). Then, a multi-parameter ICC scoring system was established based on clinical features, B-mode ultrasound features, and modified LR-M criteria. RESULT: We found that elevated CA 19-9 (OR=12.647), lesion boundary (OR=11.601), peripheral rim-like arterial phase hyperenhancement (OR=23.654), early wash-out onset (OR=7.211), and marked wash-out (OR=19.605) were positive predictors of ICC, whereas elevated alpha-fetoprotein (OR=0.078) was a negative predictor. Based on these findings, an ICC scoring system was established. Compared with the modified LR-M and LR-M criteria, the ICC scoring system showed the highest area under the curve (0.911 vs. 0.831 and 0.750, both p<0.05) and specificity (0.935 vs. 0.774 and 0.565, both p<0.05). Moreover, the numbers of HCCs categorized as LR-M decreased from 27 (43.5%) to 14 (22.6%) and 4 (6.5%) using the modified LR-M criteria and ICC scoring system, respectively. CONCLUSION: The modified LR-M criteria-based multi-parameter ICC scoring system had the highest specificity for diagnosing ICC and reduced the number of HCC cases diagnosed as LR-M category.

Quantitative analysis of pre-treatment dynamic contrast-enhanced ultrasound for assessing the response of colorectal liver metastases to chemotherapy plus targeted therapy: a dual-institutional study.

OBJECTIVES: To investigate the clinical value of pre-treatment quantitative contrast-enhanced ultrasound (CEUS) in assessing the response of colorectal liver metastases (CRLM) to chemotherapy plus targeted therapy. METHODS: This study retrospectively enrolled 50 CRLM patients from the Zhongshan Hospital, Fudan University as the training cohort and 14 patients from Shanghai Tenth People's Hospital as the testing cohort. Patients underwent the CEUS examination before receiving chemotherapy (CAPOX, FOLFOX, FOLFIRI, or FOLFOXIRI) plus targeted therapy (Bevacizumab or Cetuximab). The therapy response was determined according to Response Evaluation Criteria in Solid Tumors version 1.1 based on pre-treatment CT and 3-month follow-up CT after therapy. Dynamic analysis was performed by VueBox® software. Time-intensity curves with quantitative perfusion parameters were obtained. In the training cohort, univariable and multivariable logistic regression analyses were used to develop the predictive model of therapy response. The predictive performance of the developed model was validated in the testing cohort. RESULTS: After the logistic regression analyses, the peak enhancement (PE) (odds ratio = 1.640; 95% confidence intervals [CI] 1.022-2.633) and time to peak (TTP) (odds ratio = 0.495; 95% CI 0.246-0.996) were determined as independent predictive factors. PE and TTP generated from VueBox® were not affected by ultrasound instruments and contrast agent dosage in therapy response evaluation (P > 0.05). The logistic regression model achieved satisfactory prediction performance (area under the curve: 0.923 in the training cohort and 0.854 in the testing cohort). CONCLUSION: CEUS with dynamic quantitative perfusion analysis, which presents high consistency, has potential practical value in predicting the response of CRLM to chemotherapy plus targeted therapy.

Response Evaluation Using Contrast-Enhanced Ultrasound for Unresectable Advanced Hepatocellular Carcinoma Treated With Tyrosine Kinase Inhibitors Plus Anti-PD-1 Antibody Therapy.

OBJECTIVE: The aim of the work described here was to evaluate the role of contrast-enhanced ultrasound (CEUS) in response evaluation for unresectable advanced hepatocellular carcinoma (HCC) treated with tyrosine kinase inhibitors (TKIs) plus anti-programmed cell death protein-1 (PD-1) antibody therapy. METHODS: A prospective cohort of consecutive patients with HCC who received combined TKI/anti-PD-1 antibody treatment for unresectable HCC between January 2022 and October 2022 was included in this study. The patients underwent unenhanced ultrasound (US) and CEUS examinations before treatment and at follow-up. Changes in the largest diameters of the target tumor on unenhanced US and the largest diameters of the enhancing target tumors on CEUS were evaluated. Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 with unenhanced US and magnetic resonance imaging/computed tomography (MRI/CT) and modified RECIST (mRECIST) with CEUS and CEMRI/CT were used to assess treatment response. RESULTS: A total of 24 HCC patients (23 men and 1 woman; mean age: 56.5 ± 8.5 y; Barcelona Clinic Liver Cancer stage C, 62.5%; 29 intrahepatic target tumors) were studied. Calculations of degree of necrosis in the target tumors revealed no significant differences between CEUS and CEMRI/CT (44.5 ± 36.2% vs. 45.3 ± 36.8%, p = 0.862). As for the differentiation of responders from non-responders, the agreement between RECIST version 1.1 of unenhanced US and mRECIST-CEUS was poor (κ coefficient = 0.233). Meanwhile, there was a high degree of concordance between mRECIST-CEUS and mRECIST-CEMRI/CT (κ coefficient = 0.812). CONCLUSION: CEUS proved to be superior to baseline US and is comparable to CEMRI/CT in defining treatment outcome for combined TKI/anti-PD-1 antibody therapy.

Risk stratification of gallbladder masses by machine learning-based ultrasound radiomics models: a prospective and multi-institutional study.

OBJECTIVE: This study aimed to evaluate the diagnostic performance of machine learning (ML)-based ultrasound (US) radiomics models for risk stratification of gallbladder (GB) masses. METHODS: We prospectively examined 640 pathologically confirmed GB masses obtained from 640 patients between August 2019 and October 2022 at four institutions. Radiomics features were extracted from grayscale US images and germane features were selected. Subsequently, 11 ML algorithms were separately used with the selected features to construct optimum US radiomics models for risk stratification of the GB masses. Furthermore, we compared the diagnostic performance of these models with the conventional US and contrast-enhanced US (CEUS) models. RESULTS: The optimal XGBoost-based US radiomics model for discriminating neoplastic from non-neoplastic GB lesions showed higher diagnostic performance in terms of areas under the curves (AUCs) than the conventional US model (0.822-0.853 vs. 0.642-0.706, p < 0.05) and potentially decreased unnecessary cholecystectomy rate in a speculative comparison with performing cholecystectomy for lesions sized over 10 mm (2.7-13.8% vs. 53.6-64.9%, p < 0.05) in the validation and test sets. The AUCs of the XGBoost-based US radiomics model for discriminating carcinomas from benign GB lesions were higher than the conventional US model (0.904-0.979 vs. 0.706-0.766, p < 0.05). The XGBoost-US radiomics model performed better than the CEUS model in discriminating GB carcinomas (AUC: 0.995 vs. 0.902, p = 0.011). CONCLUSIONS: The proposed ML-based US radiomics models possess the potential capacity for risk stratification of GB masses and may reduce the unnecessary cholecystectomy rate and use of CEUS. CLINICAL RELEVANCE STATEMENT: The machine learning-based ultrasound radiomics models have potential for risk stratification of gallbladder masses and may potentially reduce unnecessary cholecystectomies. KEY POINTS: • The XGBoost-based US radiomics models are useful for the risk stratification of GB masses. • The XGBoost-based US radiomics model is superior to the conventional US model for discriminating neoplastic from non-neoplastic GB lesions and may potentially decrease unnecessary cholecystectomy rate for lesions sized over 10 mm in comparison with the current consensus guideline. • The XGBoost-based US radiomics model could overmatch CEUS model in discriminating GB carcinomas from benign GB lesions.

Three-dimensional convolutional neural network model to identify clinically significant prostate cancer in transrectal ultrasound videos: a prospective, multi-institutional, diagnostic study.

Background: Identifying patients with clinically significant prostate cancer (csPCa) before biopsy helps reduce unnecessary biopsies and improve patient prognosis. The diagnostic performance of traditional transrectal ultrasound (TRUS) for csPCa is relatively limited. This study was aimed to develop a high-performance convolutional neural network (CNN) model (P-Net) based on a TRUS video of the entire prostate and investigate its efficacy in identifying csPCa. Methods: Between January 2021 and December 2022, this study prospectively evaluated 832 patients from four centres who underwent prostate biopsy and/or radical prostatectomy. All patients had a standardised TRUS video of the whole prostate. A two-dimensional CNN (2D P-Net) and three-dimensional CNN (3D P-Net) were constructed using the training cohort (559 patients) and tested on the internal validation cohort (140 patients) as well as on the external validation cohort (133 patients). The performance of 2D P-Net and 3D P-Net in predicting csPCa was assessed in terms of the area under the receiver operating characteristic curve (AUC), biopsy rate, and unnecessary biopsy rate, and compared with the TRUS 5-point Likert score system as well as multiparametric magnetic resonance imaging (mp-MRI) prostate imaging reporting and data system (PI-RADS) v2.1. Decision curve analyses (DCAs) were used to determine the net benefits associated with their use. The study is registered at https://www.chictr.org.cn with the unique identifier ChiCTR2200064545. Findings: The diagnostic performance of 3D P-Net (AUC: 0.85-0.89) was superior to TRUS 5-point Likert score system (AUC: 0.71-0.78, P = 0.003-0.040), and similar to mp-MRI PI-RADS v2.1 score system interpreted by experienced radiologists (AUC: 0.83-0.86, P = 0.460-0.732) and 2D P-Net (AUC: 0.79-0.86, P = 0.066-0.678) in the internal and external validation cohorts. The biopsy rate decreased from 40.3% (TRUS 5-point Likert score system) and 47.6% (mp-MRI PI-RADS v2.1 score system) to 35.5% (2D P-Net) and 34.0% (3D P-Net). The unnecessary biopsy rate decreased from 38.1% (TRUS 5-point Likert score system) and 35.2% (mp-MRI PI-RADS v2.1 score system) to 32.0% (2D P-Net) and 25.8% (3D P-Net). 3D P-Net yielded the highest net benefit according to the DCAs. Interpretation: 3D P-Net based on a prostate grayscale TRUS video achieved satisfactory performance in identifying csPCa and potentially reducing unnecessary biopsies. More studies to determine how AI models better integrate into routine practice and randomized controlled trials to show the values of these models in real clinical applications are warranted. Funding: The National Natural Science Foundation of China (Grants 82202174 and 82202153), the Science and Technology Commission of Shanghai Municipality (Grants 18441905500 and 19DZ2251100), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), Shanghai Science and Technology Innovation Action Plan (21Y11911200), and Fundamental Research Funds for the Central Universities (ZD-11-202151), Scientific Research and Development Fund of Zhongshan Hospital of Fudan University (Grant 2022ZSQD07).

Two-dimensional shear wave elastography with two different systems for the diagnosis of breast lesions.

BACKGROUND: Two-dimensional (2D) - shear wave elastography (SWE) has made promising advances in the diagnostic of breast lesions. However, few studies have assessed whether the diagnostic effectiveness of different platforms employing 2D-SWE is equal or different. OBJECTIVE: To compare the diagnostic effectiveness of 2D-SWE techniques from two different systems in differentiating malignant breast lesions from benign ones. METHODS: A total of 84 breast lesions were retrospectively analyzed by experienced radiologists using 2D-SWE on two ultrasound systems, i.e. system-1 (LOGIQ E9 system, GE Healthcare, Wauwatosa, WI, USA), and system-2 (Aixplorer US system, SuperSonic Imagine, Aix-en-Provence, France). Qualitative and quantitative parameters including color sign, the maximum elasticity modulus values (E-max), the mean elasticity modulus values (E-mean) and standard deviation (E-sd) of elasticity modulus values in two 2D-SWE systems were analyzed. The diagnostic performance between system-1 and system-2 were evaluated in terms of the areas under the receiver operating characteristic curves (AUROCs). RESULTS: Among the 84 lesions in this study, 66 (78.6%) were benign and 18 (21.4%) were malignant. E-max in system-1 showed the best diagnostic performance with a cut-off value of 174.5 kPa with the associated sensitivity and specificity of 100.0% and 80.3% respectively. Meanwhile, E-sd in system-2 displayed the best diagnostic performance with a cut-off value of 12.7 kPa, with the associated sensitivity and specificity of 94.4% and 80.3% respectively. The diagnostic performance of the two 2D-SWE systems was not statistically different according to receiver operating characteristic curve (ROC) analysis of E-max, E-mean, and E-sd. CONCLUSION: For identifying breast lesions, system-1 and system-2 appear to be similar in diagnostic performance. However, different cut-off values for different parameters might be selected to obtain the best diagnostic performance for the two 2D-SWE systems.

Clinical application of a 5G-based telerobotic ultrasound system for thyroid examination on a rural island: a prospective study.

PURPOSE: To evaluate the feasibility of a 5G-based telerobotic ultrasound (US) system for thyroid examination on a rural island. METHODS: From September 2020 to March 2021, this prospectively study enrolled a total of 139 patients (average age, 58.6 ± 12.7 years) included 33 males and 106 females, who underwent 5G-based telerobotic thyroid US examination by a tele-doctor at Shanghai Tenth People's Hospital and a conventional thyroid US examination at Chongming Second People's Hospital 84 km away. The clinical feasibility of 5G-based telerobotic US for thyroid examination were evaluated in terms of safety, duration, US image quality, diagnostic results, and questionnaire survey. RESULTS: 92.8% of patients had no examination-related complaints. The average duration of the 5G-based telerobotic US examination was similar as that of conventional US examination (5.57 ± 2.20 min vs. 5.23 ± 2.1 min, P = 0.164). The image quality of telerobotic US correlated well with that of conventional US (4.63 ± 0.60 vs. 4.65 ± 0.61, P = 0.102). There was no significant difference between two types of US examination methods for the diameter measurement of the thyroid, cervical lymph nodes, and thyroid nodules. Two lymphadenopathies and 20 diffuse thyroid diseases were detected in two types of US methods. 124 thyroid nodules were detected by telerobotic US and 127 thyroid nodules were detected by conventional US. Among them, 122 were the same thyroid nodules. In addition, there were good consistency in the US features (component, echogenicity, shape, and calcification) and ACR TI-RADS category of the same thyroid nodules between telerobotic and conventional US examinations (ICC = 0.788-0.863). 85.6% of patients accepted the telerobotic US, and 87.1% were willing to pay extra fee for the telerobotic US. CONCLUSION: The 5G-based telerobotic US system can be a routine diagnostic tool for thyroid examination for patients on a rural island.

Quantitative assessment of crystal dissolution in gout during urate-lowering therapy with computer-aided MicroPure imaging: a cohort study.

BACKGROUND: To evaluate whether MicroPure imaging, an ultrasound (US) image-processing technique with computer-aided analysis, can quantitatively detect crystal dissolution during urate-lowering therapy (ULT) in gout. METHODS: This was a prospective study of gout patients requiring ULT. The first metatarsophalangeal joints were examined using US and MicroPure before and after 3 months of ULT. Elementary lesions of gout, including the double contour sign (DCS), aggregates, tophi, erosion, and other US features were recorded at baseline and 3 months. MicroPure imaging features were automatically calculated by a self-developed software. Patients were divided into goal-achieved and goal-not-achieved groups according to their urate levels at 3 months. The US and MicroPure imaging features of the two groups were analyzed at baseline and 3 months. RESULTS: A total of 55 consecutive patients were enrolled (25: goal-achieved group; 30: goal-not-achieved group). US findings demonstrated that the power Doppler signal grade decreased at 3 months, regardless of the group (both P<0.05). From baseline to 3 months, tophi size and the DCS reduced in the goal-achieved group (both P<0.05), while the US aggregate features showed no difference (P=0.250). However, on the MicroPure imaging, the number and density of aggregates at 3 months decreased in the goal-achieved group (both P<0.05). There were no significant changes at 3 months in any of the MicroPure imaging features in the goal-not-achieved group (all P>0.05). CONCLUSIONS: In comparison with B-mode US, computer-aided MicroPure imaging can sensitively and quantitatively detect aggregate dissolution during effective ULT after only 3 months of treatment.

Decoding the molecular subtypes of breast cancer seen on multimodal ultrasound images using an assembled convolutional neural network model: A prospective and multicentre study.

BACKGROUND: Preoperative determination of breast cancer molecular subtypes facilitates individualized treatment plan-making and improves patient prognosis. We aimed to develop an assembled convolutional neural network (ACNN) model for the preoperative prediction of molecular subtypes using multimodal ultrasound (US) images. METHODS: This multicentre study prospectively evaluated a dataset of greyscale US, colour Doppler flow imaging (CDFI), and shear-wave elastography (SWE) images in 807 patients with 818 breast cancers from November 2016 to February 2021. The St. Gallen molecular subtypes of breast cancer were confirmed by postoperative immunohistochemical examination. The monomodal ACNN model based on greyscale US images, the dual-modal ACNN model based on greyscale US and CDFI images, and the multimodal ACNN model based on greyscale US and CDFI as well as SWE images were constructed in the training cohort. The performances of three ACNN models in predicting four- and five-classification molecular subtypes and identifying triple negative from non-triple negative subtypes were assessed and compared. The performance of the multimodal ACNN was also compared with preoperative core needle biopsy (CNB). FINDING: The performance of the multimodal ACNN model (macroaverage area under the curve [AUC]: 0.89-0.96) was superior to that of the dual-modal ACNN model (macroaverage AUC: 0.81-0.84) and the monomodal ACNN model (macroaverage AUC: 0.73-0.75) in predicting four-classification breast cancer molecular subtypes, which was also better than that of preoperative CNB (AUC: 0.89-0.99 vs. 0.67-0.82, p < 0.05). In addition, the multimodal ACNN model outperformed the other two ACNN models in predicting five-classification molecular subtypes (AUC: 0.87-0.94 vs. 0.78-0.81 vs. 0.71-0.78) and identifying triple negative from non-triple negative breast cancers (AUC: 0.934-0.970 vs. 0.688-0.830 vs. 0.536-0.650, p < 0.05). Moreover, the multimodal ACNN model obtained satisfactory prediction performance for both T1 and non-T1 lesions (AUC: 0.957-0.958 and 0.932-0.985). INTERPRETATION: The multimodal US-based ACNN model is a potential noninvasive decision-making method for the management of patients with breast cancer in clinical practice. FUNDING: This work was supported in part by the National Natural Science Foundation of China (Grants 81725008 and 81927801), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), and the Science and Technology Commission of Shanghai Municipality (Grants 19441903200, 19DZ2251100, and 21Y11910800).

Radiofrequency-Sensitive Longitudinal Relaxation Tuning Strategy Enabling the Visualization of Radiofrequency Ablation Intensified by Magnetic Composite.

As a minimally invasive heat source, radiofrequency (RF) ablation still encounters potential damages to the surrounding normal tissues because of heat diffusion, high power, and long time. With a comprehensive understanding of the current state of the art on RF ablation, a magnetic composite using porous hollow iron oxide nanoparticles (HIONs) as carriers to load dl-menthol (DLM) has been engineered. This composite involves two protocols for enhancing RF ablation, that is, HION-mediated magnetothermal conversion in RF field and RF solidoid vaporation (RSV)-augmented inertial cavitation, respectively. A combined effect based on two protocols is found to improve energy transformation, and further, along with hydrophobic DLM-impeded heat diffusion, improve the energy utilization efficiency and significantly facilitate ex vivo and in vivo RF ablation. More significantly, in vitro and in vivo RSV processes and RSV-augmented inertial cavitation for RF ablation can be monitored by T1-weighted magnetic resonance imaging (MRI) via an RF-sensitive longitudinal relaxation tuning strategy because the RSV process can deplete DLM and make HION carriers permeable to water molecules, consequently improving the longitudinal relaxation rate of HIONs and enhancing T1-weighted MRI. Therefore, this RF-sensitive magnetic composite holds a great potential in lowering the power and time of RF ablation and improving its therapeutic safety.

IOUS and CE-IOUS during hepatic resection for patients with hepatocellular carcinoma in liver cirrhosis1.

PURPOSE: To retrospectively evaluate the role of intraoperative ultrasonography (IOUS) and contrast-enhanced IOUS (CE-IOUS) for the patients with hepatocellular carcinoma (HCC) undergoing hepatic resection (HR). METHODS: Twenty-one consecutive patients who had undergone HR for HCC were included in this study. The patients were subject to preoperative imaging modalities including preoperative ultrasonography (Pre-US) and preoperative contrast-enhanced ultrasonography (Pre-CEUS). All the patients then underwent intraoperative ultrasonography (IOUS) and contrast-enhanced intraoperative ultrasonography (CE-IOUS) during surgery. The visualization of primary HCC and additional lesions of all patients were analyzed. RESULTS: Twenty-one HCCs were detected during Pre-US and the remaining six lesions (28.6%) were detected during IOUS and CE-IOUS. Thus the treatment plan was changed in 28.6% of patients. Twenty-one HCCs (diameter, 0.6-3.0 cm; mean±SD, 1.98±0.85 cm) were measured on Pre-US and remeasured on IOUS (diameter, 0.9-3.3 cm; mean±SD, 2.19±0.84 cm) (p < 0.001). The 6 additional lesions consisted of three moderately differentiated HCCs, one cholangiocarcinoma (ICC), and two high-grade dysplastic nodules (DNs). The mean maximal diameter of the 6 additional lesions was 0.83 cm (range: 0.6-1.1 cm). The malignancy associated features such as capsule interruption, echo heterogeneity, hypo-echoic rim, and a nodule in nodule pattern were more often depicted on IOUS than on Pre-US (all p < 0.01). On CEUS, 19 (90.5%) of 21 HCCs were hyper-enhanced in the arterial phase and washed out from the portal phase to the late phase; the remaining two (9.5%) were hypoenhanced. On CE-IOUS, tumor vasculatures were classified as four patterns: 11 (52.4%) exhibited netlike pattern, 7 (33.3%) annular pattern, 2 (9.5%) mixed pattern, and 1 (4.8%) radial pattern. 3 mHCCs and 2 DNs of six additional nodules had similar greyscale imagining features on IOUS, but they showed different enhancement patterns on CE-IOUS. The ICC showed slightly heterogeneous enhancement during the arterial phase and hypo-enhancement during the portal phase. CONCLUSIONS: IOUS detects more lesions and the treatment plan is changed in 28.6% of patients. HCCs were larger on IOUS than on Pre-US. The typical imaging features of HCCs were better depicted on IOUS in comparison with Pre-US. CE-IOUS can catch the details of microcirculation perfusion of HCCs more sensitively than CEUS. Both IOUS and CE-IOUS were able to provide more decision information during surgery.