Plant mass variations of Leymus chinensis (Poaceae) and their relationships with environmental factors on a large-scale gradient, northeastern China.

Body size (or mass) variations and their relationships with environmental variability have been well documented for many species at the local scale, while the effects of climate, combined with soil nutrients, on plant mass in large-scale gradient remain unclear. Herein, detailed surveys were conducted to investigate plant mass (PM, aboveground mass per plant) variations of Leymus chinensis and their relationship with environmental factors (e.g., climate, soil nutrient, and microbial diversity) at 18 wild sites along a large-scale gradient from 114 to 124° E in northeastern China. Based on site-by-site analyses, the plant mass of the species varied significantly from east to west along the gradient. It initially increased, peaking at middle sites, and then dropped with the increase of drought in both dry and rainy seasons. Plant mass at the eastern end was almost equal to that at the western end and was equivalent to 1/2 and 1/3 of middle sites. The average plant mass in the rainy season was about 50% greater than that in the dry season (F 1,1078 = 489.80, p < .001). The effects of environmental variables on plant mass differed in dry and rainy seasons. Mean annual temperature and temperature seasonality were the critical restrictions of plant mass in the dry season, while temperature and precipitation seasonality and soil resources (total C, Mn, Zn) had significant impacts in the rainy season (p < .05). In general, plant mass had not dropped linearly with the increase of drought along large-scale gradient, suggesting that precipitation decrease was not the critical restriction regulating the growth and settlement of the species.

The Value of Follow-Up Liver Stiffness Changes Measured by Virtual Touch Quantification Elastography for Predicting Recurrence of Gastroesophageal Varices after Endoscopic Injection Sclerotherapy on Cirrhotic Patients.

Background and Aims: Recurrence of gastroesophageal varices (GEVs) after sclerotherapy is a public health problem. However, mass screening of recurrence of GEVs through gastroscopy is a high-cost procedure. We aim to evaluate the changes in liver stiffness (LS) over time after endoscopic injection sclerotherapy (EIS) and determine its value in predicting the recurrence of GEVs. Methods: One hundred and thirty-five patients with GEVs who underwent EIS treatment were included in this study. The patients were divided into two groups, namely, the nonrecurrence and recurrence groups, based on endoscopic findings at 6 months after discharge. LS measurements were obtained on five occasions. Repeated measure analysis of variance was employed to assess LS differences at different time points and compare them between the two groups. Results: The LS values during the 6-month postdischarge period were consistently higher than the baseline value (measured on the day of hospitalization). The recurrence group demonstrated sustained elevated LS levels throughout the 6-month follow-up period, while the nonrecurrence group showed a gradual decline in LS. The difference in LS trend between the two groups was statistically significant (P = 0.04). The area under the curve (AUC) values for LS differences were 0.806, with a corresponding 95% confidence interval (CI) of 0.640-0.918 and a cut-off value of 0.556, indicating their potential utility in predicting GEV recurrence. Conclusions: Longitudinal assessment of LS values in post-EIS patients can provide valuable information for predicting the recurrence of GEVs.

Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma.

BACKGROUND: Patients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC. This study aimed to demonstrate the utilization of six machine learning (ML)-based prognostic models to predict overall survival of patients with AFP-positive HCC. METHODS: Data on patients with AFP-positive HCC were extracted from the Surveillance, Epidemiology, and End Results database. Six ML algorithms (extreme gradient boosting [XGBoost], logistic regression [LR], support vector machine [SVM], random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3]) were used to develop the prognostic models of patients with AFP-positive HCC at one year, three years, and five years. Area under the receiver operating characteristic curve (AUC), confusion matrix, calibration curves, and decision curve analysis (DCA) were used to evaluate the model. RESULTS: A total of 2,038 patients with AFP-positive HCC were included for analysis. The 1-, 3-, and 5-year overall survival rates were 60.7%, 28.9%, and 14.3%, respectively. Seventeen features regarding demographics and clinicopathology were included in six ML algorithms to generate a prognostic model. The XGBoost model showed the best performance in predicting survival at 1-year (train set: AUC = 0.771; test set: AUC = 0.782), 3-year (train set: AUC = 0.763; test set: AUC = 0.749) and 5-year (train set: AUC = 0.807; test set: AUC = 0.740). Furthermore, for 1-, 3-, and 5-year survival prediction, the accuracy in the training and test sets was 0.709 and 0.726, 0.721 and 0.726, and 0.778 and 0.784 for the XGBoost model, respectively. Calibration curves and DCA exhibited good predictive performance as well. CONCLUSIONS: The XGBoost model exhibited good predictive performance, which may provide physicians with an effective tool for early medical intervention and improve the survival of patients.

Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study.

RATIONALE AND OBJECTIVES: The aim of this study was to develop a deep learning radiomics nomogram (DLRN) based on B-mode ultrasound (BMUS) and color doppler flow imaging (CDFI) images for preoperative assessment of lymphovascular invasion (LVI) status in invasive breast cancer (IBC). MATERIALS AND METHODS: In this multicenter, retrospective study, 832 pathologically confirmed IBC patients were recruited from eight hospitals. The samples were divided into training, internal test, and external test sets. Deep learning and handcrafted radiomics features reflecting tumor phenotypes on BMUS and CDFI images were extracted. The BMUS score and CDFI score were calculated after radiomics feature selection. Subsequently, a DLRN was developed based on the scores and independent clinic-ultrasonic risk variables. The performance of the DLRN was evaluated for calibration, discrimination, and clinical usefulness. RESULTS: The DLRN predicted the LVI with accuracy, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI 0.90-0.95), 0.91 (95% CI 0.87-0.95), and 0.91 (95% CI 0.86-0.94) in the training, internal test, and external test sets, respectively, with good calibration. The DLRN demonstrated superior performance compared to the clinical model and single scores across all three sets (p < 0.05). Decision curve analysis and clinical impact curve confirmed the clinical utility of the model. Furthermore, significant enhancements in net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indicated that the two scores could serve as highly valuable biomarkers for assessing LVI. CONCLUSION: The DLRN exhibited strong predictive value for LVI in IBC, providing valuable information for individualized treatment decisions.

The value of low-intensity pulsed ultrasound in reducing ovarian injury caused by chemotherapy in mice.

BACKGROUND: Ovarian damage and follicle loss are major side effects of chemotherapy in young female patients with cancer. However, effective strategies to prevent these injuries are still lacking. The purpose of this study was to verify low-intensity pulsed ultrasound (LIPUS) can reduce ovarian injury caused by chemotherapy and to explore its underlying mechanisms in mice model. METHODS: The mice were randomly divided into the Control group, Cisplatin group, and Cisplatin + LIPUS group. The Cisplatin group and Cisplatin + LIPUS group were intraperitoneally injected with cisplatin every other day for a total of 10 injections, and the Control group was injected with saline. On the second day of each injection, the Cisplatin + LIPUS group received irradiation, whereas the other two groups received sham irradiation. We used a variety of biotechnologies to detect the differences in follicle count, granulosa cell apoptosis, fibrosis, transcriptome level, oxidative damage, and inflammation in differently treated mice. RESULT: LIPUS was able to reduce primordial follicle pool depletion induced by cisplatin and inhibit the apoptosis of granulosa cells. Transcriptomic results confirmed that LIPUS can reduce ovarian tissue injury. We demonstrated that LIPUS can relieve ovarian fibrosis by inhibiting TGF-ß1/Smads pathway. Meanwhile, it can reduce the oxidative damage and reduced the mRNA levels of proinflammatory cytokines caused by chemotherapy. CONCLUSION: LIPUS can reduce the toxic effects of chemotherapy drugs on ovaries, inhibit ovarian fibrosis, reduce the inflammatory response, and redcue the oxidative damage, reduce follicle depletion and to maintain the number of follicle pools.

Predicting Ki-67 expression in hepatocellular carcinoma: nomogram based on clinical factors and contrast-enhanced ultrasound radiomics signatures.

PURPOSE: To develop a contrast-enhanced ultrasound (CEUS) clinic-radiomics nomogram for individualized assessment of Ki-67 expression in hepatocellular carcinoma (HCC). METHODS: A retrospective cohort comprising 310 HCC individuals who underwent preoperative CEUS (using SonoVue) at three different centers was partitioned into a training set, a validation set, and an external test set. Radiomics signatures indicating the phenotypes of the Ki-67 were extracted from multiphase CEUS images. The radiomics score (Rad-score) was calculated accordingly after feature selection and the radiomics model was constructed. A clinic-radiomics nomogram was established utilizing multiphase CEUS Rad-score and clinical risk factors. A clinical model only incorporated clinical factors was also developed for comparison. Regarding clinical utility, calibration, and discrimination, the predictive efficiency of the clinic-radiomics nomogram was evaluated. RESULTS: Seven radiomics signatures from multiphase CEUS images were selected to calculate the Rad-score. The clinic-radiomics nomogram, comprising the Rad-score and clinical risk factors, indicated a good calibration and demonstrated a better discriminatory capacity compared to the clinical model (AUCs: 0.870 vs 0.797, 0.872 vs 0.755, 0.856 vs 0.749 in the training, validation, and external test set, respectively) and the radiomics model (AUCs: 0.870 vs 0.752, 0.872 vs 0.733, 0.856 vs 0.729 in the training, validation, and external test set, respectively). Furthermore, both the clinical impact curve and the decision curve analysis displayed good clinical application of the nomogram. CONCLUSION: The clinic-radiomics nomogram constructed from multiphase CEUS images and clinical risk parameters can distinguish Ki-67 expression in HCC patients and offer useful insights to guide subsequent personalized treatment.

The role of multimodal ultrasound in diagnosis of fetal bowel dilatation and prediction of adverse neonatal outcomes: A study of 86 cases in a series of 43,562 births.

Objective: To investigate the diagnostic utility of multimodal ultrasound for fetal bowel dilatation (FBD) in different parts of the bowel and to examine its prognostic potential in FBD. Methods: This retrospective study analyzed 86 fetuses with a dilated bowel identified via ultrasound in a 10-month postnatal follow-up. Both two- and three dimensional (2D and 3D, respectively) ultrasound volume imaging were used to characterize dilation across different bowel sections. The optimal intestinal diameter cut-off values for pathological bowel dilatation were determined and a predictive model for neonatal surgery was developed. Results: The 86 cases of dilatation were distributed as follows: duodenal (n = 36); jejunum/ileum (n = 35); and colonic (n = 15). Duodenal dilatations presented the earliest during pregnancy compared to the other 2 groups (24.4 versus [vs.] 29 vs. 33.7 weeks respectively; p < 0.05). Cases with small intestinal dilatation were delivered earlier than those with colonic dilatation (p < 0.05). Infants with duodenal dilatation had the lowest birth weight and the highest rate of multi-system abnormalities (30.6% vs. 5.7% vs. 20%; p < 0.001). More than one-half of the multi-system abnormalities had chromosomal abnormalities (multiple, 54% vs. single, 12.5%; p = 0.015). There were 2 stillbirths, 24 induced labors, 44 postnatal surgeries, and 18 normal cases after birth. In predicting adverse neonatal outcomes of jejunum/ileum dilatation using a cut-off value of 15.5 mm small intestine diameter, sensitivity was 81.5%, specificity was 62.5%, and the area under the receiver operating characteristic curve (AUC) was 0.762 (p < 0.05). For colonic dilatation, using a cut-off value of 21.5 mm colon diameter: sensitivity was 83.3%, specificity was 77.8%, and AUC was 0.861 (p < 0.05). In detecting jejunum/ileum and colonic obstruction, 3D ultrasound demonstrated significantly better diagnostic efficiency than 2D ultrasound (p < 0.05). Using the backward stepwise selection method, a predictive model for neonatal surgery in patients with jejunum/ileum and colonic dilatation was established: logit (P) = -1.58 + (2.32 × polyhydramnios) +(2.0 × ascites) +(1.14 × hyperechogenic bowel). The AUC for the prediction model was 0.874 (p < 0.05), with 76% sensitivity and 94.1% specificity. Conclusions: Duodenal dilatation occurred earlier, with a higher incidence of chromosomal abnormalities and multi-system abnormalities than dilatation of other parts of the bowel. 3D ultrasound played an important role in the detection of jejunum/ileum and colon obstructions. Clinical signs, including polyhydramnios, ascites, and strong echoes in the intestine, can be used to predict neonatal surgery.

Non-invasive prediction of the chronic degree of lupus nephropathy based on ultrasound radiomics.

OBJECTIVE: Through machine learning (ML) analysis of the radiomics features of ultrasound extracted from patients with lupus nephritis (LN), this attempt was made to non-invasively predict the chronicity index (CI)of LN. METHODS: A retrospective collection of 136 patients with LN who had renal biopsy was retrospectively collected, and the patients were randomly divided into training set and validation set according to 7:3. Radiomics features are extracted from ultrasound images, independent factors are obtained by using LASSO dimensionality reduction, and then seven ML models were used to establish predictive models. At the same time, a clinical model and an US model were established. The diagnostic efficacy of the model is evaluated by analysis of the receiver operating characteristics (ROC) curve, accuracy, specificity, and sensitivity. The performance of the seven machine learning models was compared with each other and with clinical and US models. RESULTS: A total of 1314 radiomics features are extracted from ultrasound images, and 5 features are finally screened out by LASSO for model construction, and the average ROC of the seven ML is 0.683, among which the Xgboost model performed the best, and the AUC in the test set is 0.826 (95% CI: 0.681-0.936). For the same test set, the AUC of clinical model constructed based on eGFR is 0.560 (95% CI: 0.357-0.761), and the AUC of US model constructed based on Ultrasound parameters is 0.679 (95% CI: 0.489-0.853). The Xgboost model is significantly more efficient than the clinical and US models. CONCLUSION: ML model based on ultrasound radiomics features can accurately predict the chronic degree of LN, which can provide a valuable reference for clinicians in the treatment strategy of LN patients.

Potential added value of computed tomography radiomics to multimodal prediction models for benign and malignant breast tumors.

Background: Early diagnosis is crucial to the treatment of breast cancer, but conventional imaging detection is challenging. Radiomics has the potential to improve early diagnostic efficacy in a noninvasive manner. This study examined whether integrating computed tomography (CT) radiomics information based on ultrasound (US) models can improve the efficacy of breast cancer prediction. Methods: We retrospectively analyzed 420 patients with pathologically confirmed benign or malignant breast tumors. Clinical data and examination images were collected, and the population was divided into training (n=294) and validation (n=126) groups at a ratio of 7:3. The region of interest (ROI) was manually segmented along the tumor boundary using MaZda software, and the features of each ROI was extracted. After dimension reduction and screening, the best features were retained. Subsequently, random forest (RF), support vector machines, and K-nearest neighbor classifiers were used to establish prediction models in an US and combined-methods group. Results: Finally, 8 of the 379 features were retained in the US group. Random forest was found to be the best model, and the area under the curve (AUC) of the training and validation groups was 0.90 [95% confidence interval (CI): 0.852-0.942] and 0.85 (95% CI: 0.775-0.930), respectively. Meanwhile, 12 of the 750 features were retained in the combined group. In this regard, random forest proved to be the best model, and the AUC of the training and validation group was 0.95 (95% CI: 0.918-0.981) and 0.92 (95% CI: 0.866-0.969), respectively. The calibration curve showed a good fit of the model. The decision curve showed that the clinical net benefit of the combined group was far greater than that of any single examination, and the prediction model of the combined group exhibited a degree of practical clinical value. Conclusions: The combined model based on US and CT images has potential application value in the prognostic prediction of benign and malignant breast diseases.

Quantitative Evaluation of the Lumbar Multifidus Muscle by Shear Wave Dispersion in Healthy Adults: A Preliminary Study.

OBJECTIVES: To investigate the application value of shear wave dispersion (SWD) in healthy adults with the lumbar multifidus muscle (LMM), to determine the range of normal reference values, and to analyze the influences of factors on the parameter. METHODS: Ninety-five healthy volunteers participated in the study, from whom 2-dimensional, shear wave elastography (SWE), and SWD images of the bilateral LMM were acquired in three positions (prone, standing, and anterior flexion). Subcutaneous fat thickness (SFH), SWE velocity, and SWD slope were measured accordingly for analyses. RESULTS: The mean SWD slope of the bilateral LMM in the prone position was as follows: left: 14.8 ± 3.1 (m/second)/kHz (female) and 13.0 ± 2.5 (m/second)/kHz (male); right: 14.8 ± 3.7 (m/second)/kHz (female) and 14.2 ± 3.4 (m/second)/kHz (male). In the prone position, there was a weak negative correlation between the bilateral LMM SWD slope of activity level 2 and level 1 (ß = -1.5 (2 versus 1, left), -1.9 (2 versus 1, right), all P < .05), and between the left SWD slope of activity level 3 and level 1 (ß = -2.3 [3 versus 1, left], P < .05). The correlation between SWE velocity and SWD slope value changed with the position: there was a weak positive correlation in the prone position (r = 0.3 [left], 0.37 [right], both P < .05), and a moderate positive correlation in the standing and anterior flexed positions (r = 0.49-0.74, both P < .001). SFH was moderately negatively correlated with bilateral SWD slope values in the anterior flexion (left: r = -0.4, P = .01; right: r = -0.7, P < .01). CONCLUSIONS: SWD imaging can be used as an adjunct tool to aid in the assessment of viscosity in LMM. Further, activity level, and position are influencing factors that should be considered in clinical practice.

Association between body roundness index and risk of ultrasound-defined non-alcoholic fatty liver disease.

Objectives: While several indicators have been studied, the association of body roundness index (BRI) with non-alcoholic fatty liver disease (NAFLD) remains unclear. We aimed to explore the association between BRI and ultrasound-defined NAFLD. Methods: The sample dataset was extracted from the National Health and Nutrition Examination Survey (NHANES) during the period of 2017-2018. The diagnosis of NAFLD was determined based on the controlled attenuated parameter (CAP≥248 dB/m) score of liver ultrasound transient elastography (LUTE). Participants with excessive alcohol use and viral hepatitis were excluded. To delve deeper into the relationship, Multivariable logistic regression with adjustment for confounding variables and smoothing curve analysis was used to investigate the association and nonlinear relationships between BRI and NAFLD. Results: Among 4210 individuals aged 20 years or older included in the study, 28.2 % had NAFLD. Compared to the first tertile, BRI notably increased the risk of NAFLD 3.53-fold [95 % confidence interval (CI) = 2.73-4.57] in the second tertile and 7.00-fold (95%CI = 5.29-9.27) in the third tertile after adjusting for multiple covariates (P for trend <0.001). Furthermore, when BRI was treated as a continuous variable, one unit of increment in BRI was associated with 41 % higher odds of NAFLD [adjusted odds ratio (aOR) = 1.41; 95%CI = 1.34-1.48; P < 0.001]. The associations of BRI with NAFLD persisted in all subgroup analyses. A smoothing curve fitting demonstrated that the relationship between BRI and NAFLD was a nonlinear connection. The risk of NAFLD increased significantly when BRI was lower than 4.82, after which the curve showed a modest ascent. Conclusion: Higher BRI was consistently associated with an increased risk of NAFLD in US adults. BRI is a risk factor for NAFLD, and there is an imperative to give more attention to lowering the BRI.

A Transvaginal Ultrasound-Based Deep Learning Model for the Noninvasive Diagnosis of Myometrial Invasion in Patients with Endometrial Cancer: Comparison with Radiologists.

RATIONALE AND OBJECTIVES: This study aimed to determine the feasibility of using the deep learning (DL) method to determine the degree (whether myometrial invasion [MI] >50%) of MI in patients with endometrial cancer (EC) based on ultrasound (US) images. MATERIALS AND METHODS: From September 2017 to April 2023, 1289 US images of 604 patients with EC who underwent surgical resection at center 1, center 2 or center 3 were obtained and divided into a training set and an internal validation set. Ninety-five patients from center 4 and center 5 were randomly selected as the external testing set according to the same criteria as those for the primary cohort. This study evaluated three DL models trained on the training set and tested them on the validation and testing sets. The models' performance was analyzed based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), and the performance of the models was subsequently compared with that of 15 radiologists. RESULTS: In the final clinical diagnosis of MI in patients with EC, EfficientNet-B6 showed the best performance in the testing set in terms of area under the curve (AUC) [0.814, 95% CI (0.746-0.882]; accuracy [0.802, 95% CI (0.733-0.855]; sensitivity [0.623]; specificity [0.879]; positive likelihood ratio (PLR) [6.750]; and negative likelihood ratio (NLR) [0.389]. The diagnostic efficacy of EfficientNet-B6 was significantly better than that of the 15 radiologists, with an average diagnostic accuracy of 0.681, average AUC of 0.678, AUC of the best performance of 0.739, accuracy of 0.716, sensitivity of 0.806, specificity 0.672, PLR2.457, and NLR 0.289. CONCLUSION: Based on the preoperative US images of patients with EC, the DL model can accurately determine the degree of endometrial MI; the performance of this model is significantly better than that of radiologists, and it can effectively assist in clinical treatment decisions.

Patterns of weight change during adulthood and incidence of nephrolithiasis: a population-based study.

BACKGROUND: There is some evidence to suggest that there may be a link between body mass index (BMI) and the development of kidney stones, it remains unclear whether weight change was associated with the presence of kidney stone. AIMS: The objective of this study was to investigate the potential association between changes patterns in weight during adulthood and the incidence of kidney stone. METHODS: This study included 14472 participants aged 30-75 years, whose BMI was recorded at both baseline and 10 years prior to the survey. We categorized individuals into five weight change patterns: stable healthy, non-obesity to obesity, obesity to non-obesity, stable obesity, and maximum overweight. Odds ratios (OR) and 95% confidence intervals (CI) relating weight change to incident kidney stone were calculated using logistic regression models adjusting for covariates. The non-linear association between absolute weight change was investigated using the restricted cubic spline (RCS) regression. The supposed population attributable fraction (PAF) for the weight change patterns was calculated. RESULTS: After adjusting for all confounders, participants changing from non-obesity to obesity, obesity to non-obesity, and stable obesity had significantly higher risks of kidney stone than those with healthy weight during adulthood (OR = 1.59, 95% CI:1.18-2.13; OR = 1.78, 95% CI: 1.47-2.16; OR = 1.80, 95% CI: 1.48-2.19, respectively). A U-shaped association was observed, and the risk of kidney stone was lowest in participants with stable healthy BMI. If the population had maintained a healthy BMI, a 28.7% (95% CI: 18.6%-37.5%) lower incidence of kidney stones was observed. CONCLUSIONS: This study found that changes in weight during adulthood are linked to the risk of developing kidney stones. Maintaining healthy weight during adulthood is important for reducing the risk of developing kidney stones.

Role of Shear Wave Elastography in the Diagnosis of Peyronie Disease.

OBJECTIVES: The present study aims to explore the role of shear wave elastography (SWE) in the diagnosis of Peyronie disease (PD). METHODS: A total of 59 PD patients and 59 age-matched healthy adult men were included in this study. The B-mode ultrasound (US) and SWE were performed for all subjects, and the Young modulus (YM) values of the corresponding regions of the penis in the PD and control groups were recorded and compared. RESULTS: The mean age of the included PD patients and age-matched controls was 53.81 years (SD 9.52, range 32-73). On B-mode US evaluation, 41 (69.5%) of 59 included PD patients were found to have penile plaques, and the remaining 18 (30.5%) patients had no evidence of penile plaque. After evaluation using SWE, the YM values in the penile plaque region of these 41 patients with penile dysplasia were found to be significantly higher (60.29 kPa ± 19.95) than those outside the plaque (in the same patient) (21.05 kPa ± 4.58) and in the same penile region of the control group (20.59 kPa ± 4.65) (P < .001). In the remaining 18 PD patients, the results showed that the YM value of the abnormal penile region in the PD patients (56.67 kPa ± 13.52) was significantly higher than the YM value outside the abnormal penile region in the same patients (22.79 kPa ± 4.31) and in the same penile region in the control group (19.87 kPa ± 3.48) (P < .001; P < .001). CONCLUSIONS: In conclusion, this study showed that SWE as a non-invasive technique is useful in identifying and differentiating penile plaques in PD patients and is a simple, rapid and complementary method to B-mode US.

Development of a novel combined nomogram model integrating deep learning radiomics to diagnose IgA nephropathy clinically.

This study aimed to develop and validate a combined nomogram model based on superb microvascular imaging (SMI)-based deep learning (DL), radiomics characteristics, and clinical factors for noninvasive differentiation between immunoglobulin A nephropathy (IgAN) and non-IgAN.We prospectively enrolled patients with chronic kidney disease who underwent renal biopsy from May 2022 to December 2022 and performed an ultrasound and SMI the day before renal biopsy. The selected patients were randomly divided into training and testing cohorts in a 7:3 ratio. We extracted DL and radiometric features from the two-dimensional ultrasound and SMI images. A combined nomograph model was developed by combining the predictive probability of DL with clinical factors using multivariate logistic regression analysis. The proposed model's utility was evaluated using receiver operating characteristics, calibration, and decision curve analysis. In this study, 120 patients with primary glomerular disease were included, including 84 in the training and 36 in the test cohorts. In the testing cohort, the ROC of the radiomics model was 0.816 (95% CI:0.663-0.968), and the ROC of the DL model was 0.844 (95% CI:0.717-0.971). The nomogram model combined with independent clinical risk factors (IgA and hematuria) showed strong discrimination, with an ROC of 0.884 (95% CI:0.773-0.996) in the testing cohort. Decision curve analysis verified the clinical practicability of the combined nomogram. The combined nomogram model based on SMI can accurately and noninvasively distinguish IgAN from non-IgAN and help physicians make clearer patient treatment plans.

Clinical value of grayscale ultrasound combined with real-time shear wave elastography nomogram in risk prediction of thyroid cancer.

OBJECTIVES: This study constructed a nomogram based on grayscale ultrasound features and real-time shear wave elastography (SWE) parameters to predict thyroid cancer. METHODS: Clinical data of 217 thyroid nodules of 201 patients who underwent grayscale ultrasound, real-time SWE, and thyroid function laboratory examination in Ma'anshan People's Hospital from January 2019 to December 2020 were retrospectively analyzed. The subjects were divided into a benign nodule group (106 nodules) and a malignant nodule group (111 nodules). The differences in grayscale ultrasound features, quantitative parameters of real-time SWE, and laboratory results of thyroid function between benign and malignant thyroid nodules were analyzed. We used a chi-square test for categorical variables and a t-test for continuous variables. Then, the independent risk factors for thyroid cancer were analyzed using multivariate logistic regression. Based on the independent risk factors, a nomogram for predicting thyroid cancer risk was constructed using the RMS package of the R software. RESULTS: Multivariate logistic regression showed that the grayscale ultrasound features of thyroid nodules were the shape, margin, echogenicity, and echogenic foci of the nodules,the maximum Young's modulus (SWE-max) of thyroid nodules, and the ratio of thyroid nodule and peripheral gland (SWE-ratio) measured by real-time SWE were independent risk factors for thyroid cancer (all p < 0.05), and the other variables had no statistical difference (p > 0.05). Based on the shape (OR = 5.160, 95% CI: 2.252-11.825), the margin (OR = 9.647, 95% CI: 2.048-45.443), the echogenicity (OR = 6.512, 95% CI: 1.729-24.524), the echogenic foci (OR = 2.049, 95% CI: 1.118-3.756), and the maximum Young's modulus (SWE-max) (OR = 1.296, 95% CI: 1.140-1.473), the SWE-ratio (OR = 2.001, 95% CI: 1.403-2.854) of the thyroid nodule to peripheral gland was used to establish the related nomogram prediction model. The bootstrap self-sampling method was used to verify the model. The consistency index (C-index) was 0.979, ROC curve was used to analyze the nomogram scores of all patients, and the AUC of nomogram prediction of thyroid cancer was 0.976, indicating that the nomogram model had high accuracy in the risk prediction of thyroid cancer. CONCLUSIONS: The nomogram model of grayscale ultrasound features combined with SWE parameters can accurately predict thyroid cancer.

An overview of ultrasound-derived radiomics and deep learning in liver.

Over the past few years, developments in artificial intelligence (AI), especially in radiomics and deep learning, have enabled the extraction of pathophysiology-related information from varied medical imaging and are progressively transforming medical practice. AI applications are extending into domains previously thought to be accessible only to human experts. Recent research has demonstrated that ultrasound-derived radiomics and deep learning represent an enticing opportunity to benefit preoperative evaluation and prognostic monitoring of diffuse and focal liver disease. This review summarizes the application of radiomics and deep learning in ultrasound liver imaging, including identifying focal liver lesions and staging of liver fibrosis, as well as the evaluation of pathobiological properties of malignant tumors and the assessment of recurrence and prognosis. Besides, we identify important hurdles that must be overcome while also discussing the challenges and opportunities of radiomics and deep learning in clinical applications.

A sonogram radiomics model for differentiating granulomatous lobular mastitis from invasive breast cancer: a multicenter study.

PURPOSE: To construct a nomogram based on sonogram features and radiomics features to differentiate granulomatous lobular mastitis (GLM) from invasive breast cancer (IBC). MATERIALS AND METHODS: A retrospective collection of 213 GLMs and 472 IBCs from three centers was divided into a training set, an internal validation set, and an external validation set. A radiomics model was built based on radiomics features, and the RAD score of the lesion was calculated. The sonogram radiomics model was constructed using ultrasound features and RAD scores. Finally, the diagnostic efficacy of the three sonographers with different levels of experience before and after combining the RAD score was assessed in the external validation set. RESULTS: The RAD score, lesion diameter, orientation, echogenicity, and tubular extension showed significant differences in GLM and IBC (p < 0.05). The sonogram radiomics model based on these factors achieved optimal performance, and its area under the curve (AUC) was 0.907, 0.872, and 0.888 in the training, internal, and external validation sets, respectively. The AUCs before and after combining the RAD scores were 0.714, 0.750, and 0.830 and 0.834, 0.853, and 0.878, respectively, for sonographers with different levels of experience. The diagnostic efficacy was comparable for all sonographers when combined with the RAD score (p > 0.05). CONCLUSION: Radiomics features effectively enhance the ability of sonographers to discriminate between GLM and IBC and reduce interobserver variation. The nomogram combining ultrasound features and radiomics features show promising diagnostic efficacy and can be used to identify GLM and IBC in a noninvasive approach.