Self-supplying Cu2+ and H2O2 synergistically enhancing disulfiram-mediated melanoma chemotherapy.

Disulfiram (DSF) can target and kill cancer cells by disrupting cellular degradation of extruded proteins and has therefore received particular attention for its tumor chemotherapeutic potential. However, the uncontrollable Cu2+/DSF ratio reduces the efficacy of DSF-mediated chemotherapy. Herein, self-supplying Cu2+ and oxidative stress synergistically enhanced DSF-mediated chemotherapy is proposed for melanoma-based on PVP-coated CuO2 nanodots (CPNDs). Once ingested, DSF is broken down to diethyldithiocarbamate (DTC), which is delivered into a tumor via the circulation. Under the acidic tumor microenvironment, CPNDs produce sufficient Cu2+ and H2O2. DTC readily chelates Cu2+ ions to generate CuET, which shows antitumor efficacy. CuET-mediated chemotherapy can be enhanced by H2O2. Sufficient Cu2+ generation can guarantee the maximum efficacy of DSF-mediated chemotherapy. Furthermore, released Cu2+ can be reduced to Cu+ by glutathione (GSH) and O2- in tumor cells, and Cu+ can react with H2O2 to generate toxic hydroxyl radicals (·OH) via a Fenton-like reaction, promoting the efficacy of CuET. Therefore, this study hypothesizes that employing CPNDs instead of Cu2+ ions could enhance DSF-mediated melanoma chemotherapy, providing a simple but efficient strategy for achieving chemotherapeutic efficacy.

A lung disease diagnosis algorithm based on 2D spectral features of ultrasound RF signals.

Lung diseases are commonly diagnosed based on clinical pathological indications criteria and radiological imaging tools (e.g., X-rays and CT). During a pandemic like COVID-19, the use of ultrasound imaging devices has broadened for emergency examinations by taking their unique advantages such as portability, real-time detection, easy operation and no radiation. This provides a rapid, safe, and cost-effective imaging modality for screening lung diseases. However, the current pulmonary ultrasound diagnosis mainly relies on the subjective assessments of sonographers, which has high requirements for the operator's professional ability and clinical experience. In this study, we proposed an objective and quantifiable algorithm for the diagnosis of lung diseases that utilizes two-dimensional (2D) spectral features of ultrasound radiofrequency (RF) signals. The ultrasound data samples consisted of a set of RF signal frames, which were collected by professional sonographers. In each case, a region of interest of uniform size was delineated along the pleural line. The standard deviation curve of the 2D spatial spectrum was calculated and smoothed. A linear fit was applied to the high-frequency segment of the processed data curve, and the slope of the fitted line was defined as the frequency spectrum standard deviation slope (FSSDS). Based on the current data, the method exhibited a superior diagnostic sensitivity of 98% and an accuracy of 91% for the identification of lung diseases. The area under the curve obtained by the current method exceeded the results obtained that interpreted by professional sonographers, which indicated that the current method could provide strong support for the clinical ultrasound diagnosis of lung diseases.

Predicting the malignancy of extremity soft-tissue tumors by an ultrasound-based radiomics signature.

BACKGROUND: Accurate differentiation of extremity soft-tissue tumors (ESTTs) is important for treatment planning. PURPOSE: To develop and validate an ultrasound (US) image-based radiomics signature to predict ESTTs malignancy. MATERIAL AND METHODS: A dataset of US images from 108 ESTTs were retrospectively enrolled and divided into the training cohort (78 ESTTs) and validation cohort (30 ESTTs). A total of 1037 radiomics features were extracted from each US image. The most useful predictive radiomics features were selected by the maximum relevance and minimum redundancy method, least absolute shrinkage, and selection operator algorithm in the training cohort. A US-based radiomics signature was built based on these selected radiomics features. In addition, a conventional radiologic model based on the US features from the interpretation of two experienced radiologists was developed by a multivariate logistic regression algorithm. The diagnostic performances of the selected radiomics features, the US-based radiomics signature, and the conventional radiologic model for differentiating ESTTs were evaluated and compared in the validation cohort. RESULTS: In the validation cohort, the area under the curve (AUC), sensitivity, and specificity of the US-based radiomics signature for predicting ESTTs malignancy were 0.866, 84.2%, and 81.8%, respectively. The US-based radiomics signature had better diagnostic predictability for predicting ESTT malignancy than the best single radiomics feature and the conventional radiologic model (AUC = 0.866 vs. 0.719 vs. 0.681 for the validation cohort, all P <0.05). CONCLUSION: The US-based radiomics signature could provide a potential imaging biomarker to accurately predict ESTT malignancy.

Predictors of High Cardiovascular Risk Among Nonobese Patients with Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease in a Chinese Population.

Purpose: This study aims to investigate cardiovascular risk factors in nonobese patients with type 2 diabetes (T2DM) and non-alcoholic fatty liver disease (NAFLD) and to determine whether they might be used to predict high-risk individuals effectively. Patients and Methods: This cross-sectional study included 245 nonobese patients with T2DM who underwent FibroTouch in the National Metabolic Management Center of the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University from January 2021 to December 2022. All individuals were divided into NAFLD and non-NAFLD groups. Patients with NAFLD were further grouped by UAP tertiles (T1, T2 and T3). We created a Cardiovascular Score (total scale: 0-5 points; ≥3 points was defined as high-risk individual) based on baPWV, carotid ultrasound, and urinary microalbumin creatinine ratio (UA/CR) to assess the risk of cardiovascular disease in non-obese T2DM patients with NAFLD. Risk factors were evaluated using univariate and multivariate analysis. The performance of risk factors was compared according to the area under the receiver operating characteristic (ROC) curve. Results: Atherogenic index of plasma (AIP), atherosclerosis index (AI), prevalence of hypertension, body mass index (BMI) and homeostatic model assessment of insulin resistance (HOMA-IR) were higher in the NAFLD group compared to the non-NAFLD group. In T3 group, AIP, AI, BMI and HOMA-IR were higher than those of T1 group. Multivariate logistic regression showed that age, systolic blood pressure, low-density lipoprotein-cholesterol (LDL-C) and AIP were risk factors for cardiovascular disease among nonobese patients with T2DM and NAFLD. The area under the ROC curve for age, systolic blood pressure, LDL-C and AIP were 0.705, 0.688, 0.738 and 0.642, respectively. The area under the ROC curve was 0.895 when combining them. Conclusion: Age, systolic blood pressure, AIP and LDL-C are all independent risk factors for cardiovascular disease in non-obese individuals with T2DM and NAFLD, which can be combined to identify high-risk populations and carry out intervention.

Comparison of the diagnostic effectiveness of ultrasound imaging coupled with three mathematical models for discriminating thyroid nodules.

BACKGROUND: The overlapping nature of thyroid lesions visualized on ultrasound (US) images could result in misdiagnosis and missed diagnoses in clinical practice. PURPOSE: To compare the diagnostic effectiveness of US coupled with three mathematical models, namely logistic regression (Logistics), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM), in discriminating between malignant and benign thyroid nodules. MATERIAL AND METHODS: A total of 588 thyroid nodules (287 benign and 301 malignant) were collected, among which 80% were utilized for constructing the mathematical models and the remaining 20% were used for internal validation. In addition, an external validation cohort comprising 160 nodules (80 benign and 80 malignant) was employed to validate the accuracy of these mathematical models. RESULTS: Our study demonstrated that all three models exhibited effective predictive capabilities for distinguishing between benign and malignant nodules, whose diagnostic effectiveness surpassed that of the TI-RADS classification, particularly in terms of true negative diagnoses. SVM achieved a higher diagnostic rate for malignant thyroid nodules (93.8%) compared to Logistics (91.5%) and PLS-DA (91.6%). PLS-DA exhibited higher diagnostic rates for benign thyroid nodules (91.9%) compared to Logistics (86.7%) and SVM (88.7%). Both the area under the receiver operating characteristic curve (AUC) values of PLS-DA (0.917) and SVM (0.913) were higher than that of Logistics (0.891). CONCLUSION: Our findings indicate that SVM had significantly higher rates of true positive diagnoses and PLS-DA exhibited significantly higher rates of true negative diagnoses. All three models outperformed the TI-RADS classification in discriminating between malignant and benign thyroid nodules.

Percutaneous nephrolithotomy guided by 5G-powered robot-assisted teleultrasound diagnosis system: first clinical experience with a novel tele-assistance approach (IDEAL stage 1).

BACKGROUND: To demonstrate the technical feasibility of percutaneous nephrolithotomy (PCNL) guided by 5G-powered robot-assisted teleultrasound diagnosis system (RTDS) in a complex kidney-stone (CKS) cohort and present our preliminary outcomes. PCNL is highly skill-required, which hinders it popularization in primary medical units of remote regions. We designed an innovative tele-assistance approach to make PCNL easy to be operated by inexperienced surgeons. METHODS: This was a prospective proof-of-concept study (IDEAL phase 1) on intraoperative tele-assistance provided by online urological experts via a 5G-powered RTDS. Total 15 CKS patients accepted this technology. Online experts manipulated a simulated probe to assist unskilled local operators by driving a patient-side robot-probe to guide and monitor the steps of access establishment and finding residual stones. RESULTS: Median total delay was 177ms despite one-way network-connecting distance > 5,800 km. No perceptible delay of audio-visual communication, driving robot-arm or dynamic ultrasound images was fed back. Successful tele-assistance was obtained in all cases. The first-puncture access-success rate was 78.6% with a one-session SF rate of 71.3% and without complications of grade III-V. CONCLUSIONS: The current technology based on 5G-powered RTDS can provide high-quality intraoperative tele-assistance, which has preliminarily shown satisfactory outcomes and reliable safety. It will break down a personal competence-based barrier to endow PCNL with more popular utilization. TRIAL REGISTRATION: The study was approved by ethics committee of the Xinjiang Kezhou People's Hospital and ethics committee of the First Affiliated Hospital of Nanjing Medical University and was registered on http://www.chictr.org.cn (ChiCTR2200065849, 16/11/2022).

Preoperative Contrast-Enhanced Ultrasound Predicts Microvascular Invasion in Hepatocellular Carcinoma as Accurately as Contrast-Enhanced MR.

OBJECTIVES: Both contrast-enhanced ultrasound (CEUS) and contrast-enhanced magnetic resonance (CEMR) are important imaging methods for hepatocellular carcinoma (HCC). This study aimed to establish a model using preoperative CEUS parameters to predict microvascular invasion (MVI) in HCC, and compare its predictive efficiency with that of CEMR model. METHODS: A total of 93 patients with HCC (39 cases in MVI positive group and 54 cases in MVI negative group) who underwent surgery in our hospital from January 2020 to June 2021 were retrospectively analyzed. Their clinical and imaging data were collected to establish CEUS and CEMR models for predicting MVI. The predictive efficiencies of both models were compared. RESULTS: By the univariate and multivariate regression analyses of patients' clinical information, preoperative CEUS static and dynamic images, we found that serrated edge and time to peak were independent predictors of MVI. The CEUS prediction model achieved a sensitivity of 92.3%, a specificity of 83.3%, and an accuracy of 84.6% (Az: 0.934). By analyzing the clinical and CEMR information, we found that tumor morphology, fast-in and fast-out, peritumoral enhancement, and capsule were independent predictors of MVI. The CEMR prediction model achieved a sensitivity of 97.4%, a specificity of 77.8%, and an accuracy of 83.2% (Az: 0.900). The combination of the two models achieved a sensitivity of 84.6%, a specificity of 87.0%, and an accuracy of 86.2% (Az: 0.884). There was no significant statistical difference in the areas under the ROC curve of the three models. CONCLUSION: The CEUS model and the CEMR model have similar predictive efficiencies for MVI of HCC. CEUS is also an effective method to predict MVI before operation.

The Correlation Between Leg Muscle Mass Index and Non-Alcoholic Fatty Liver Disease in Patients with Type 2 Diabetes Mellitus.

Objective: To analyze the relationship between leg skeletal muscle mass index (LSMI) and non-alcoholic fatty liver disease (NAFLD) in patients with type 2 diabetes mellitus (T2DM) and the ability of LSMI to predict NAFLD. Methods: Two hundred patients with T2DM and NAFLD treated at Changzhou Second People's Hospital Affiliated with Nanjing Medical University and the National Metabolic Management Center from June 2022 to June 2023 were divided into four LSMI quartiles. The clinical information from the four patient groups was compared, and the relationship between type 2 diabetes and LSMI and NAFLD was examined. We used receiver operating characteristic curves to determine how well the LSMI predicts NAFLD in T2DM. Results: The lowest quartile (Q1) had a higher prevalence of NAFLD than group Q4 (P < 0.05). LSMI was negatively associated with body mass index, LS, CAP, and other markers (P < 0.05). Receiver operating characteristic curve analysis LSMI predicted NAFLD with an ideal critical value of 0.64 and an area under the curve of 70.9%. The combined predictive value of the LSMI and the appendicular skeletal muscle mass index was more significant. Conclusion: Reduced LSMI is associated with NAFLD.

The effect of sedentary time on cardiovascular disease risk in middle-aged and elderly patients with type 2 diabetes mellitus.

Sedentary lifestyle has become quite prevalent lately, and it has been associated with cardiovascular diseases (CVDs). CVD is a primary cause of premature death in patients with type 2 diabetes mellitus (T2DM). Some studies have focused on the association between sedentary behavior and blood glucose among T2DM patients. However, the occurrence and development of CVD involves many factors, such as blood glucose, blood lipid and so on. Therefore, we comprehensively examined the association of sedentary time with overall CVD risk and various metabolic risk factors in T2DM patients. A total of 775 middle-aged and elderly patients with T2DM were assessed. Framingham risk equation was employed to assess their overall CVD risk, while the sedentary time was self-reported. Demographic data and anthropometric and cardiac metabolic indicators were separately analyzed for both genders. The median age of the respondents was 55 (range: 45-75) years, and 39.23% were women. The overall risk of CVD in women was lower than that in men. Linear regression analysis revealed that sedentary time was significantly positively correlated with overall CVD risk and triglyceride level, but not with diastolic blood pressure and glycosylated hemoglobin and high-density lipoprotein cholesterol (HDL-C) levels. However, the correlation of sedentary time with fasting blood glucose level, body mass index, total cholesterol, and low-density lipoprotein cholesterol was only detected in women. In middle-aged and elderly patients with T2DM, prolonged sedentary time may increase the triglyceride levels and the overall risk of CVD. The adverse effects of sedentary time on fasting blood glucose, body mass index, total cholesterol, and low-density lipoprotein cholesterol may exhibit sex-based differences, as they were detected only in women.

Decreased serum musclin concentration is independently associated with the high prevalence of sarcopenia in Chinese middle-elderly patients with type 2 diabetes mellitus.

INTRODUCTION AND AIMS: Sarcopenia is a complication of diabetes mellitus, which can increase hospitalization and lead to poor outcomes. The present study investigated the relationship between the serum musclin concentration and the sarcopenia morbidity in Chinese middle-elderly patients with type 2 diabetes mellitus. METHODS: We recruited 220 patients with type 2 diabetes mellitus, all of whom completed gait speed, handgrip strength tests, and whole-body dual-energy x-ray measurements to calculate the appendicular skeletal muscle mass index (ASMI). The patients were divided into sarcopenia (n = 110) and non-sarcopenia groups (n = 110). The serum musclin concentration was measured using an enzyme-linked immunosorbent assay. RESULTS: The serum musclin concentration was significantly lower in the sarcopenia group (712.82 pg/mL) than in the non-sarcopenia group (922.53 pg/mL). The serum musclin concentration positively correlated with the whole-body skeletal mass (r = 0.230; P = 0.001). Sarcopenia morbidity declined as the quartile of serum musclin concentration increased (P = 0.001), and a negative correlation was observed between the serum musclin concentration and the prevalence of sarcopenia (odds ratio = 0.998, P = 0.001). The correlation remained when quartiles were considered. CONCLUSIONS: The serum musclin concentration is an independent protective factor for sarcopenia in Chinese middle-elderly patients with type 2 diabetes mellitus.

Comparison of the diagnostic efficacy of mathematical models in distinguishing ultrasound imaging of breast nodules.

This study compared the diagnostic efficiency of benign and malignant breast nodules using ultrasonographic characteristics coupled with several machine-learning models, including logistic regression (Logistics), partial least squares discriminant analysis (PLS-DA), linear support vector machine (Linear SVM), linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural network (ANN) and random forest (RF). The clinical information and ultrasonographic characteristics of 926 female patients undergoing breast nodule surgery were collected and their relationships were analyzed using Pearson's correlation. The stepwise regression method was used for variable selection and the Monte Carlo cross-validation method was used to randomly divide these nodule cases into training and prediction sets. Our results showed that six independent variables could be used for building models, including age, background echotexture, shape, calcification, resistance index, and axillary lymph node. In the prediction set, Linear SVM had the highest diagnosis rate of benign nodules (0.881), and Logistics, ANN and LDA had the highest diagnosis rate of malignant nodules (0.910~0.912). The area under the ROC curve (AUC) of Linear SVM was the highest (0.890), followed by ANN (0.883), LDA (0.880), Logistics (0.878), RF (0.874), PLS-DA (0.866), and KNN (0.855), all of which were better than that of individual variances. On the whole, the diagnostic efficacy of Linear SVM was better than other methods.

An integrated radiomics nomogram based on conventional ultrasound improves discriminability between fibroadenoma and pure mucinous carcinoma in breast.

Objective: To evaluate the ability of integrated radiomics nomogram based on ultrasound images to distinguish between breast fibroadenoma (FA) and pure mucinous carcinoma (P-MC). Methods: One hundred seventy patients with FA or P-MC (120 in the training set and 50 in the test set) with definite pathological confirmation were retrospectively enrolled. Four hundred sixty-four radiomics features were extracted from conventional ultrasound (CUS) images, and radiomics score (Radscore) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different models were developed by a support vector machine (SVM), and the diagnostic performance of the different models was assessed and validated. A comparison of the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) was performed to evaluate the incremental value of the different models. Results: Finally, 11 radiomics features were selected, and then Radscore was developed based on them, which was higher in P-MC in both cohorts. In the test group, the clinic + CUS + radiomics (Clin + CUS + Radscore) model achieved a significantly higher area under the curve (AUC) value (AUC = 0.86, 95% CI, 0.733-0.942) when compared with the clinic + radiomics (Clin + Radscore) (AUC = 0.76, 95% CI, 0.618-0.869, P > 0.05), clinic + CUS (Clin + CUS) (AUC = 0.76, 95% CI, 0.618-0.869, P< 0.05), Clin (AUC = 0.74, 95% CI, 0.600-0.854, P< 0.05), and Radscore (AUC = 0.64, 95% CI, 0.492-0.771, P< 0.05) models, respectively. The calibration curve and DCA also suggested excellent clinical value of the combined nomogram. Conclusion: The combined Clin + CUS + Radscore model may help improve the differentiation of FA from P-MC.

Association between time in range 70-180 mg/dl in early stage and severity with in patients acute pancreatitis.

BACKGROUND: It is not well understood whether glucose control in the early stage of acute pancreatitis(AP) is related to outcome. This study aimed to investigate the association between blood glucose time in range (TIR) of 70-180 mg/dL in the first 72 h(h) on admission and the progression of AP. METHODS: Individuals admitted with AP to the Gastroenterology Department of the Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University between January 2017 and December 2021 were included and retrospectively evaluated. The percentage of TIR between 70 and 180 mg/dL in the first 72 h was calculated. According to the progress of AP at discharge, patients were divided into mild pancreatitis(MAP), and moderately severe acute pancreatitis (MSAP), or severe acute pancreatitis (SAP) groups. We examined the association between TIR or TIR ≥ 70% and AP severity using logistic regression models stratified by a glycosylated hemoglobin (HbA1c) level of 6.5%. Receiver operating characteristic (ROC) curves were generated to assess the ability of the TIR to predict MSAP or SAP. RESULTS: A total of 298 individuals were included, of whom 35 developed MSAP or SAP. Logistic regression analyses indicated that TIR was independently associated with the incidence of more serious AP (odds ratio [OR] = 0.962, 95% CI = 0.941-0.983, p = 0.001). This association remained significant in individuals with HbA1c levels ≤ 6.5% (OR = 0.928, 95% CI = 0.888-0.969, p = 0.001). A TIR ≥ 70% was independently associated with reduced severity only in people with well-antecedent controls (OR = 0.238; 95% CI = 0.071-0.802; p = 0.020). TIR was not powerful enough to predict the severity of AP in both patients with poor antecedent glucose control (AUC = 0.641) or with HbA1c < 6.5% (AUC = 0.668). CONCLUSIONS: TIR was independently associated with severity in patients with AP, particularly those with good antecedent glucose control.

Association Between Intestinal Prevotella copri Abundance and Glycemic Fluctuation in Patients with Brittle Diabetes.

Objective: Previous studies have demonstrated an association between gut microbiota composition and non-brittle type 2 diabetes (NBT2DM) pathogenesis. However, little is known about the correlation between the abundance of intestinal Prevotella copri and glycemic fluctuations in patients with brittle diabetes mellitus (BDM). In this context, we conducted a case-control study of BDM patients and patients with NBT2DM, aiming to determine and analyze the relationship between the abundance of intestinal Prevotella copri and glycemic fluctuations in patients with BDM. Research Design and methods: We performed a metagenomic analysis of the gut microbiome obtained from fecal samples of 10 BDM patients, and compared their microbial composition and function to NBT2DM patients (1:1 ratio). Then further collected data including age, sex, BMI, glycated hemoglobin (HbA1c), blood lipids, and alpha diversity of the gut microbiota, which were comparable between the BDM and NBT2DM patients by t-test. Results: A significant difference existed in the beta diversity of the gut microbiota between the two groups (PCoA, R2 = 0.254, P = 0.0001). The phylum-level abundance of Bacteroidetes in the gut microbiota of the BDM patients was significantly lower, by 24.9% (P = 0.001), than that of the NBT2DM patients. At the gene level, the abundance of Prevotella copri was obviously reduced, Correlation analysis showed that the Prevotella copri abundance was inversely correlated to the standard deviation of blood glucose (SDBG) (r = -0.477, P = 0.034). Quantitative PCR confirmed that the abundance of Prevotella copri in the BDM patients in the validation cohort was significantly lower than that in NBT2DM patients, and was negatively correlated with SDBG (r = -0.318, P = 0.043). Glycemic variability in BDM was inversely correlated with the abundance of intestinal Prevotella copri. Conclusion: The decreased abundance of Prevotella copri in patients with BDM may be associated with glycemic fluctuation.

The role of ultrasound quantitative parameters in the assessment of acute radiodermatitis after breast-conserving surgery.

This study aimed to assess the severity of acute radiodermatitis (ARD) by ultrasound quantitative parameters and to try to identify the influencing factors of skin toxicity. A total of 55 patients who underwent radiotherapy after unilateral breast-conserving surgery (BCS) were included in the study. The irradiated side of the breast was used as the research object and the quantitative ultrasound parameters (skin thickness, shear wave elasticity) were evaluated before radiotherapy, every week during radiotherapy. Two weeks after radiotherapy, the patients were divided into two groups, according to the World Health Organization scoring standard: mild (0-2 grade) and severe (3-4 grade). The differences in the parameters between the groups and the changes during radiotherapy were compared, and the relationship between these parameters and the severity of ARD was analyzed. In addition, some clinical factors that may affect ARD were also included in our study. Ninety-eight percent of patients developed different degrees of ARD, and Group 2 accounted for ~31%. At the end of 5 weeks of radiotherapy, the difference in thickness between the two groups was statistically significant (P < 0.05). There was no significant change in the elastic modulus of breast skin between the two groups (P > 0.05). Body mass index >25 kg/m2, breast thickness ≥18 mm, skin basic elastic modulus <23 kPa and skin thickness increment >0.3 mm were considered to be associated with severe skin reactions (P < 0.05). Ultrasound can be a useful tool for the non-invasive and objective assessment of skin changes during radiotherapy, documenting quantitative changes in the skin of breast cancer patients following BCS undergoing radiotherapy.

A multiparametric clinic-ultrasomics nomogram for predicting extremity soft-tissue tumor malignancy: a combined retrospective and prospective bicentric study.

OBJECTIVE: We aimed at building and testing a multiparametric clinic-ultrasomics nomogram for prediction of malignant extremity soft-tissue tumors (ESTTs). MATERIALS AND METHODS: This combined retrospective and prospective bicentric study assessed the performance of the multiparametric clinic-ultrasomics nomogram to predict the malignancy of ESTTs, when compared with a conventional clinic-radiologic nomogram. A dataset of grayscale ultrasound (US), color Doppler flow imaging (CDFI), and elastography images for 209 ESTTs were retrospectively enrolled from one hospital, and divided into the training and validation cohorts. A multiparametric ultrasomics signature was built based on multimodal ultrasomic features extracted from the grayscale US, CDFI, and elastography images of ESTTs in the training cohort. Another conventional radiologic score was built based on multimodal US features as interpreted by two experienced radiologists. Two nomograms that integrated clinical risk factors and the multiparameter ultrasomics signature or conventional radiologic score were respectively developed. Performance of the two nomograms was validated in the retrospective validation cohort, and tested in a prospective dataset of 51 ESTTs from the second hospital. RESULTS: The multiparametric ultrasomics signature was built based on seven grayscale ultrasomic features, three CDFI ultrasomic features, and one elastography ultrasomic feature. The conventional radiologic score was built based on five multimodal US characteristics. Predictive performance of the multiparametric clinic-ultrasomics nomogram was superior to that of the conventional clinic-radiologic nomogram in the training (area under the receiver operating characteristic curve [AUC] 0.970 vs. 0.890, p = 0.006), validation (AUC: 0.946 vs. 0.828, p = 0.047) and test (AUC: 0.934 vs. 0.842, p = 0.040) cohorts, respectively. Decision curve analysis of combined training, validation and test cohorts revealed that the multiparametric clinic-ultrasomics nomogram had a higher overall net benefit than the conventional clinic-radiologic model. CONCLUSION: The multiparametric clinic-ultrasomics nomogram can accurately predict the malignancy of ESTTs.

Association of body composition with bone mineral density and fractures in Chinese male type 2 diabetes mellitus.

The association between body composition and bone health in men over 50 years with type 2 diabetes mellitus remains unclear. We aimed to investigate how fat and lean mass affect bone health in male patients with diabetes over 50 years. A total of 233 hospitalized male type 2 diabetes mellitus patients with aged 50 to 78 years were enrolled. Lean mass, fat mass and bone mineral density (BMD) were estimated. The clinical fractures were also assessed. Glycosylated hemoglobin, bone turnover markers, and biochemical parameters were measured. The normal BMD group had a higher lean mass index (LMI) and fat mass index (FMI) and lower levels of bone turnover markers. glycosylated hemoglobin was negatively correlated with LMI (r = -0.224, P = .001) and FMI (r = -0.158, P = .02). In partial correlation adjusted for age and body weight, FMI was negatively correlated (r = -0.135, P = .045) with lumbar spine, while LMI was still positively correlated with lumbar spine (R = 0.133, P = .048) and total hip (R = 0.145, P = .031). In multiple regression analysis, LMI was consistently associated with BMD at the spine (ß = 0.290, P < .01), hip (ß = 0.293, P < .01), and femoral neck (ß = 0.210, P = .01), whereas FMI was only positively associated with BMD at the femoral neck (ß = 0.162, P = .037). A total of 28 patients diagnosed with diabetic osteoporotic fractures had lower LMI and FMI than their non-fractured counterparts. LMI was negatively associated with fracture, whereas FMI had such an effect only before adjusting for BMD. Lean mass is dominant in maintaining BMD and is a BMD-independent protective factor for diabetic osteoporotic fracture in male patients aged over 50 years. Fat mass in gravity is positively associated with BMD in the femoral neck, which may mediate fracture protection.

Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.

OBJECTIVES: To investigate the predictive performance of the deep learning radiomics (DLR) model integrating pretreatment ultrasound imaging features and clinical characteristics for evaluating therapeutic response after neoadjuvant chemotherapy (NAC) in patients with breast cancer. METHODS: A total of 603 patients who underwent NAC were retrospectively included between January 2018 and June 2021 from three different institutions. Four different deep convolutional neural networks (DCNNs) were trained by pretreatment ultrasound images using annotated training dataset (n = 420) and validated in a testing cohort (n = 183). Comparing the predictive performance of these models, the best one was selected for image-only model structure. Furthermore, the integrated DLR model was constructed based on the image-only model combined with independent clinical-pathologic variables. Areas under the curve (AUCs) of these models and two radiologists were compared by using the DeLong method. RESULTS: As the optimal basic model, Resnet50 achieved an AUC and accuracy of 0.879 and 82.5% in the validation set. The integrated DLR model, yielding the highest classification performance in predicting response to NAC (AUC 0.962 and 0.939 in the training and validation cohort), outperformed the image-only model and the clinical model and also performed better than two radiologists' prediction (all p < 0.05). In addition, predictive efficacy of the radiologists was improved under the assistance of the DLR model significantly. CONCLUSION: The pretreatment US-based DLR model could hold promise as a clinical guidance for predicting NAC response of patients with breast cancer, thereby providing benefit of timely treatment strategy adjustment to potential poor NAC responders. KEY POINTS: • Multicenter retrospective study showed that deep learning radiomics (DLR) model based on pretreatment ultrasound image and clinical parameter achieved satisfactory prediction of tumor response to neoadjuvant chemotherapy (NAC) in breast cancer. • The integrated DLR model could become an effective tool to guide clinicians in identifying potential poor pathological responders before chemotherapy. • The predictive efficacy of the radiologists was improved under the assistance of the DLR model.

Quantitative evaluation of gastrocnemius medialis mass in patients with chronic heart failure by gray-scale ultrasound and shear wave elastography.

Objective: To assess the usefulness of gray-scale ultrasound (US) and shear wave elastography (SWE) in assessing the condition of the skeletal muscles in patients with chronic heart failure (CHF). Methods: We prospectively compared 20 patients with clinically diagnosed CHF and a control population of 20 normal volunteers. The gastrocnemius medialis (GM) of each individual in the rest and the contraction position was assessed using gray-scale US and SWE. The quantitative US parameters including the fascicle length (FL), pinnation angle (PA), echo intensity (EI), and Young's modulus of the muscle were measured. Results: In the CHF group compared with the control group, in the rest position, there was a significant difference in EI, PA, and FL of the GM (P < 0.001), but no statistically significant difference in Young's modulus values (P > 0.05); however, in the contraction position, all parameters were statistically different between the two groups (P < 0.001). In the different subgroups of the CHF group grouped according to New York Heart Association staging (NYHA) or left ventricular ejection fraction (LVEF), there were no significant differences in ultrasound parameters in the rest position. However, during the contraction of GM, the smaller the FL and Young's modulus, the larger the PA and EI with the increase of NYHA grade or the decrease of LVEF (P < 0.001). Conclusion: The gray-scale US and SWE can provide an objective assessment of skeletal muscle status for CHF patients and are expected to be used to guide their early rehabilitation training and improve their prognosis.

Combining primary tumor features derived from conventional and contrast-enhanced ultrasound facilitates the prediction of positive axillary lymph nodes in Breast Imaging Reporting and Data System category 4 malignant breast lesions.

PURPOSE: To determine whether the primary tumor features derived from conventional ultrasound (US) and contrast-enhanced US (CEUS) facilitate the prediction of positive axillary lymph nodes (ALNs) in breast cancer diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4. METHODS: A total of 240 women with breast cancer who underwent preoperative conventional US, strain elastography, and CEUS between September 2016 and December 2019 were included. The multiple parameters of the primary tumor were obtained, and univariate and multivariate analyses were performed to predict positive ALNs. Then three prediction models (conventional US features, CEUS features, and the combined features) were developed, and the diagnostic performance was evaluated with receiver operating characteristic curves. RESULTS: On conventional US, the traits of large size and the non-circumscribed margin of the primary tumor were marked as two independent predictors. On CEUS, the features of vessel perforation or distortion and the enhanced range of the primary tumor were marked as two independent predictors for positive ALNs. Three prediction models were then developed: model A (conventional US features), model B (CEUS features), and model C (model A plus B). Model C yielded the highest area under the curve (AUC) of 0.82 [95% confidence interval (CI), 0.75-0.88] compared with model A (AUC 0.74; 95% CI, 0.68-0.81; P = 0.008) and model B (AUC 0.72; 95% CI, 0.65-0.80; P < 0.001) as per the DeLong test. CONCLUSION: CEUS, as a non-invasive examination technique, can be used to predict ALN metastasis. Combining conventional US and CEUS may produce favorable predictive accuracy for positive ALNs in BI-RADS category 4 breast cancer.