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Objective: The study aimed to investigate the predictive value of dynamic contrast-enhanced ultrasound (DCE-US) in differentiating small-duct (SD) and large-duct (LD) types of intrahepatic cholangiocarcinoma (ICC). Methods: This study retrospectively enrolled 110 patients with pathologically confirmed ICC lesions who were subject to preoperative contrast-enhanced ultrasound (CEUS) examinations between January 2022 and February 2023. Patients were further classified according to the subtype: SD-type and LD-type, and an optimal predictive model was established and validated using the above pilot cohort. The test cohort, consisting of 48 patients prospectively enrolled from March 2023 to September 2023, was evaluated. Results: In the pilot cohort, compared with SD-type ICCs, more LD-type ICCs showed elevated carcinoembryonic antigen (p < 0.001), carbohydrate antigen 19-9 (p = 0.004), ill-defined margin (p = 0.018), intrahepatic bile duct dilation (p < 0.001). Among DCE-US quantitative parameters, the wash-out area under the curve (WoAUC), wash-in and wash-out area under the curve (WiWoAUC), and fall time (FT) at the margin of lesions were higher in the SD-type group (all p < 0.05). Meanwhile, the mean transit time (mTT) and wash-out rate (WoR) at the margin of the lesion were higher in the LD-type group (p = 0.041 and 0.007, respectively). Logistic regression analysis showed that intrahepatic bile duct dilation, mTT, and WoR were significant predictive factors for predicting ICC subtypes, and the AUC of the predictive model achieved 0.833 in the test cohort. Conclusions: Preoperative DCE-US has the potential to become a novel complementary method for predicting the pathological subtype of ICC. Critical relevance statement: DCE-US has the potential to assess the subtypes of ICC lesions quantitatively and preoperatively, which allows for more accurate and objective differential diagnoses, and more appropriate treatments and follow-up or additional examination strategies for the two subtypes. Key Points: Preoperative determination of intrahepatic cholangiocarcinoma (ICC) subtype aids in surgical decision-making. Quantitative parameters from dynamic contrast-enhanced US (DCE-US) allow for the prediction of the ICC subtype. DCE-US-based imaging has the potential to become a novel complementary method for predicting ICC subtypes. [ABSTRACT FROM AUTHOR]

Purpose: To explore the feasibility of a 5G-based telerobotic ultrasound (US) system for providing qualified abdominal US services on a rural island. Methods: This prospective study involved two medical centers (the tele-radiologist site's hospital and the patient site's hospital) separated by 72 km. Patients underwent 5G-based telerobotic US by tele-radiologists and conventional US by on-site radiologists from September 2020 to March 2021. The clinical feasibility and diagnostic performance of the 5G-based telerobotic abdominal US examination were assessed based on safety, duration, image quality, diagnostic findings, and questionnaires. Results: A total of 401 patients (217 women and 184 men; mean age, 54.96 ± 15.43 years) were enrolled. A total of 90.1% of patients indicated no discomfort with the telerobotic US examination. For the examination duration, telerobotic US took longer than conventional US (12.54 ± 3.20 min vs. 7.23 ± 2.10 min, p = 0.001). For image quality scores, the results of the two methods were similar (4.54 ± 0.63 vs. 4.57 ± 0.61, p = 0.112). No significant differences were found between the two methods in measurements for the aorta, portal vein, gallbladder, kidney (longitudinal diameter), prostate, and uterus; however, telerobotic US underestimated the transverse diameter of the kidney (p < 0.05). A total of 504 positive results, including 31 different diseases, were detected. Among them, 455 cases were identified by the two methods; 17 cases were identified by telerobotic US only; and 32 cases were identified by conventional US only. There was good consistency in the diagnosis of 29 types of disease between the two methods (κ = 0.773–1.000). Furthermore, more than 90% of patients accepted the telerobotic US examination and agreed to pay additional fees in future. Conclusion: The 5G-based telerobotic US system can expand access to abdominal US services for patients in rural areas, thereby reducing health care disparities. [ABSTRACT FROM AUTHOR]

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. [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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The relation between vitamin D receptor (VDR) gene polymorphisms and ankylosing spondylitis (AS) remains unclear. A systematic review and meta-analysis were conducted using six databases, including PubMed, Web of Science, EMBASE, CNKI, Wanfang and Cochrane Library. The selection of each study was based on inclusion and exclusion criteria. The Newcastle–Ottawa Scale was applied to assess the quality of the included studies, while the strength was evaluated by odds ratios and 95% confidence intervals. The following contrasts were used: allele contrast (H vs h), homozygous contrast (HH vs hh), heterozygous contrast (Hh vs hh), dominant contrast (HH + Hh vs hh) and recessive contrast (HH vs Hh + hh). For the BsmI-rs1544410 polymorphism, three studies were included of 782 cases and 863 controls. The data showed a significant relationship under allele contrast H vs h (OR = 1.66, 95% CI 1.20–2.30 (P = 0.002)). For the TaqI-rs731236 polymorphism, 675 cases and 697 controls were included in two studies. The data showed a significant relationship under allele contrast H vs h (OR = 1.57, 95% CI 1.11–2.21 (P < 0.05)), homozygous contrast Hh vs hh (OR = 1.65, 95% CI 1.12–2.43 (P < 0.05)), and recessive contrast HH + Hh vs hh (OR = 1.66, 95% CI 1.13–2.43 (P < 0.05)). There were significant relationships between VDR gene BsmI-rs1544410 and TaqI-rs731236 polymorphisms and AS, while no associations were found between FokI-rs2228570 and ApaI-rs7975232 polymorphisms and AS. In the future, additional studies with larger case numbers are need. [ABSTRACT FROM AUTHOR]

Objectives: Peripheral blood immune cell profiling of atopic dermatitis patients before and after treatment by single-cell RNA sequencing technique has not been reported. To study the immune Cell Profiling of Atopic Dermatitis Patients Before and After Treatment with Halometasone Cream Wet-Wrap Therapy. Methods: We used single cell sequencing to detect the proportion change and gene expression change of immune cells in 2 patients before and after treatment, and then used real-time PCR to confirm the mRNA level of differential genes. Results: In this study, scRNA-seq in two patients with severe AD before and after halometasone cream wet-wrap therapy showed that in the mild severity of AD after treatment, Th2 cells were significantly decreased (41.2% vs 13.4%), Th1 and Th17 cells were increased (23.3% vs 43.7%, 2.3% vs 4.8% respectively). The proportion of Th22 cells did not change much (1.3% vs 1.9%). Tregs were significantly increased also (1.5% vs 5.0%). In the regulatory T cells, the expression of IL-27, PD-1, CD103, CTLA-4, ZNF-66, IL-β, CD7 gene was specifically increased after treatment, and CD39, P21, TOX2, CD151, CD79A, S100A12, TRAP1 gene was specifically decreased after treatment. In the TH2 cells, the expression of CD27, CD68, EZH1, RAD1, EGFR, CCR10, BCL11A, KLF4 gene was specifically increased after treatment and CCL26, CD180, IL-31, CCL22, LEF1, OX40 gene was specifically decreased after treatment. Conclusions: These genes may be new target for further study. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]