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PURPOSE: To evaluate a computer-aided diagnosis (CAD) technique in predicting malignancy for cytologically indeterminate thyroid nodules (TNs) as compared with different experienced radiologists. METHOD: 436 patients with 436 cytologically indeterminate TNs on fine-needle aspiration cytology (FNAC) were included and all were confirmed by surgical pathology. They were retrospectively analyzed with respect to ultrasound (US) characteristics using a commercially available CAD system (AmCAD-UT; AmCad BioMed, Taiwan, China) and reviewed by one junior and one senior radiologists.The CAD system and different experienced radiologists stratified the risk of malignancy using ACR TI-RADS category. The diagnostic performance by different experienced radiologists independently and after consulting the CAD (different experienced radiologists + CAD) and by the CAD alone were compared. RESULTS: The different experienced radiologists showed significantly higher specificities than the CAD system alone. The combination of radiologist and CAD system showed improved diagnostic performance with an AUC (Area under the curve) of 0.740 in the senior radiologist and 0.677 in the junior radiologist, as compared with CAD (AUC: 0.585) alone (all P < 0.05). The combination of senior radiologist and CAD system had the highest diagnostic performance (AUC: 0.740) and specificity (68.9%) compared to the others (all P < 0.05). CONCLUSION: The CAD system may play the potential role as a decision-making assistant alongside radiologists for differential diagnosis of TNs with indeterminate cytology. [ABSTRACT FROM AUTHOR]

Objectives: To assess the respective diagnostic value of Sonazoid™ and SonoVue® for characterizing FLLs as benign or malignant and the corresponding safety. Methods: This prospective Phase 3 study was conducted at 17 centres in China and Korea (May 2014 to April 2015); 424 patients (20 to 80 years) with at least 1 untreated focal liver lesion (FLL) (< 10 cm in diameter) underwent a contrast-enhanced ultrasound (CEUS) examination (218 received Sonazoid of 0.12 μL microbubbles/kg; 206 received SonoVue of 2.4 mL). Three independent blinded readers evaluated pre- and post-contrast images characterising the FLLs as benign or malignant. Results: Sonazoid-enhanced and SonoVue-enhanced ultrasound provided a statistically significant improvement in specificity for all 3 readers comparing to unenhanced ultrasound (for Sonazoid: p = 0.0093, < 0.0001, 0.0011; for SonoVue: p = 0.002, 0.03, 0.12, respectively). Difference in accuracy improvement between the 2 groups was within the pre-specified non-inferiority margin of 20% for all 3 readers (6.1%, 95% CI: − 5.0 to 17.2; − 7.5%, 95% CI: − 18.4 to 3.5; − 0.3%, 95% CI: − 11.3 to 10.7). The diagnostic confidence level for all 3 readers increased with post-contrast images relative to pre-contrast images. Both contrast agents were well tolerated. Conclusion: Results showed a similar efficacy for Sonazoid™ and SonoVue® in diagnosing FLLs as benign or malignant, and underlined the benefit of CEUS imaging over unenhanced ultrasound imaging in reaching a confident diagnosis without having to refer patients for additional imaging exams. [ABSTRACT FROM AUTHOR]

Purpose. To investigate the relationship between uric acid and renal microvascular perfusion in diabetic kidney disease (DKD) using contrast-enhanced ultrasound (CEUS) method. Materials and Methods. 79 DKD patients and 26 healthy volunteers were enrolled. Renal function and urine protein markers were tested. DKD patients were subdivided into two groups including a normal serum uric acid (SUA) group and a high SUA group. Contrast-enhanced ultrasound (CEUS) was performed, and low acoustic power contrast-specific imaging was used for quantitative analysis. Results. Normal controls (NCs) had the highest levels of AUC, AUC1, and AUC2. Compared to the normal SUA DKD group, high SUA DKD patients had significantly higher IMAX, AUC, and AUC1 (P<0.05). DKD patients with low urinary uric acid (UUA) excretion had significantly higher AUC2 compared to DKD patients with normal UUA (P<0.05). Conclusion. Hyperuricemia in DKD patients was associated with a renal ultrasound image suggestive of microvascular hyperperfusion. The CEUS parameter AUC1 holds promise as an indicator for renal microvascular hyperperfusion, while AUC2 might be a useful indicator of declining glomerular filtration rate in DKD patients with decreased excretion of uric acid. [ABSTRACT FROM AUTHOR]

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., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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)., Competing Interests: Declaration of Competing Interest T. Wu is an employee of GE Healthcare. D. Li is an employee of XiaoBaiShiJi Network Technical Co., Ltd. They do not hold any other financial interests in these companies. These companies had no role in the study design, data collection, analysis, patient recruitment or writing of the manuscript, the decision to submit the manuscript for publication or any aspect pertinent to the study. No products from this company were used in this study. The other authors declare that they have no conflicts of interest., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: The risk stratification system of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) for thyroid nodules is affected by low diagnostic specificity. Machine learning (ML) methods can optimize the diagnostic performance in medical image analysis. However, it is unknown which ML-based diagnostic pattern is more effective in improving diagnostic performance for thyroid nodules and reducing nodule biopsies. Therefore, we compared ML-assisted visual approaches and radiomics approaches with ACR TI-RADS in diagnostic performance and unnecessary fine-needle aspiration biopsy (FNAB) rate for thyroid nodules. Methods: This retrospective study evaluated a data set of ultrasound (US) and shear wave elastography (SWE) images in patients with biopsy-proven thyroid nodules (≥1 cm) from the Shanghai Tenth People's Hospital (743 nodules in 720 patients from September 2017 to January 2019) and an independent test data set from the Ma'anshan People's Hospital (106 nodules in 102 patients from February 2019 to April 2019). Six US features and five SWE parameters from the radiologists' interpretation were used for building the ML-assisted visual approaches. The radiomics features extracted from the US and SWE images were used with ML methods for developing the radiomics approaches. The diagnostic performance for differentiating thyroid nodules and the unnecessary FNAB rate of the ML-assisted visual approaches and the radiomics approaches were compared with ACR TI-RADS. Results: The ML-assisted US visual approach had the best diagnostic performance than the US radiomics approach and ACR TI-RADS (area under the curve [AUC]: 0.900 vs. 0.789 vs. 0.689 for the validation data set, 0.917 vs. 0.770 vs. 0.681 for the test data set). After adding SWE, the ML-assisted visual approach had a better diagnostic performance than US alone (AUC: 0.951 vs. 0.900 for the validation data set, 0.953 vs. 0.917 for the test data set). When applying the ML-assisted US+SWE visual approach, the unnecessary FNAB rate decreased from 30.0% to 4.5% in the validation data set and from 37.7% to 4.7% in the test data set in comparison to ACR TI-RADS. Conclusions: The ML-assisted dual modalities visual approach can assist radiologists to diagnose thyroid nodules more effectively and considerably reduce the unnecessary FNAB rate in the clinical management of thyroid nodules.