US-guided Percutaneous Radiofrequency Ablation for Secondary Hyperparathyroidism: Long-term Outcomes and Prognostic Factors.

Background Although favorable outcomes have been reported with radiofrequency ablation (RFA) for secondary hyperparathyroidism (SHPT), the long-term efficacy remains insufficiently investigated. Purpose To evaluate the long-term efficacy and safety of US-guided percutaneous RFA in patients with SHPT undergoing dialysis and to identify possible predictors associated with treatment failure. Materials and Methods This retrospective study included consecutive patients with SHPT with at least one enlarged parathyroid gland accessible for RFA who were undergoing dialysis at seven tertiary centers from May 2013 to July 2022. The primary end point was the proportion of patients with parathyroid hormone (PTH) levels less than or equal to 585 pg/mL at the end of follow-up. Secondary end points were the proportion of patients with normal calcium and phosphorus levels, the technical success rate, procedure-related complications, and improvement in self-rated hyperparathyroidism-related symptoms (0-3 ranking scale). The Wilcoxon signed rank test and generalized estimating equation model were used to evaluate treatment outcomes. Univariable and multivariable regression analyses identified variables associated with treatment failure (recurrent or persistent hyperparathyroidism). Results This study included 165 patients (median age, 51 years [IQR, 44-60 years]; 92 female) and 582 glands. RFA effectively reduced PTH, calcium, and phosphorus levels, with targeted ranges achieved in 78.2% (129 of 165), 72.7% (120 of 165), and 60.0% (99 of 165) of patients, respectively, at the end of follow-up (mean, 51 months). For the RFA sessions, the technical success rate was 100% (214 of 214). Median symptom scores (ostealgia, arthralgia, pruritus) decreased (all P < .001). Regarding complications, only hypocalcemia (45.8%, 98 of 214) was common. Treatment failure occurred in 36 patients (recurrent [n = 5] or persistent [n = 31] hyperparathyroidism). The only potential independent predictor of treatment failure was having less than four treated glands (odds ratio, 17.18; 95% CI: 4.34, 67.95; P < .001). Conclusion US-guided percutaneous RFA was effective and safe in the long term as a nonsurgical alternative for patients with SHPT undergoing dialysis; the only potential independent predictor of treatment failure was a lower number (<4) of treated glands. © RSNA, 2024 Supplemental material is available for this article.

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.

The value of shear wave elastography combined with red blood cell distribution width in evaluating arterial erectile dysfunction.

PURPOSE: A retrospective study was conducted to determine the value of shear wave elastography (SWE) and red blood cell distribution width (RDW) in the diagnosis of various forms of erectile dysfunction (ED). METHODS: With the method of Nocturnal Penile Tumescence and Rigidity (NPTR) and the screening method of Color Duplex Doppler Ultrasound (CDDU), hematological data were collected from 131 individuals, among whom 24 are with psychogenic ED, 48 are with non-arterial ED(NAED) and 59 are with arterial ED(AED) with erectile dysfunction. SWE value of penile corpus cavernosum(CCP) and cavernous arterial flow velocity were measured before (flaccid state) and after (erect state) intracavernous injection (ICI) in all patients. RESULTS: Among the AED patients and other types of ED patients, there were statistically significant differences in the abridged five-item International Index of Erectile Function (IIEF-5), red blood cell distribution width-coefficient of variation (RDW-CV), red blood cell distribution width-standard deviation (RDW-SD), and SWE values (all P < 0.01). In the AED patients, the IIEF-5 scores had a significant negative relationship with RDW-CV, RDW-SD, and SWE values, with SWE values having the strongest correlation. (p < 0.001, r=-0.638). CONCLUSION: The combination of RDW level and SWE value demonstrated the greatest performance in diagnosing AED, according to the receiver-operator characteristic(ROC) curve analysis (AUC = 0.870, p < 0.0001, cut-off value of 0.75, sensitivity of 74.6%, specificity of 91.7%).RDW and SWE value may develop into an incredibly simple, practical tool for predicting and diagnosing AED. TRIAL REGISTRATION: retrospectively registered.

Ultrasonic Feature Prediction of Large-Number Central Lymph Node Metastasis in Clinically Node-Negative Solitary Papillary Thyroid Carcinoma.

BACKGROUND: The purpose of this study was to investigate the preoperative prediction of large-number central lymph node metastasis (CLNM) in single thyroid papillary carcinoma (PTC) with negative clinical lymph nodes. METHODS: A total of 634 patients with clinically lymph node-negative single PTC who underwent thyroidectomy and central lymph node dissection at the First Affiliated Hospital of Anhui Medical University and the Nanchong Central Hospital between September 2018 and September 2021 were analyzed retrospectively. According to the CLNM status, the patients were divided into two groups: small-number (≤5 metastatic lymph nodes) and large-number (>5 metastatic lymph nodes). Univariate and multivariate analyses were used to determine the independent predictors of large-number CLNM. Simultaneously, a nomogram based on risk factors was established to predict large-number CLNM. RESULTS: The incidence of large-number CLNM was 7.7%. Univariate and multivariate analyses showed that age, tumor size, and calcification were independent risk factors for predicting large-number CLNM. The combination of the three independent predictors achieved an AUC of 0.806. Based on the identified risk factors that can predict large-number CLNM, a nomogram was developed. The analysis of the calibration map showed that the nomogram had good performance and clinical application. CONCLUSION: In patients with single PTC with negative clinical lymph nodes large-number CLNM is related to age, size, and calcification in patients with a single PTC with negative clinical lymph nodes. Surgeons and radiologists should pay more attention to patients with these risk factors. A nomogram can help guide the surgical decision for PTC.

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.

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.

Noninvasive evaluation of IgA nephropathy fibrosis using Doppler ultrasound.

In this study, we aimed to explore the clinical value of routine color ultrasound parameters in the evaluation of tubular atrophy and interstitial fibrosis (TA/IF) in IgA nephropathy (IgAN). We enrolled 725 patients with IgAN who underwent renal biopsy at the First Affiliated Hospital of Anhui Medical University between January 2019 and May 2022. Examinations were performed to measure the routine ultrasound renal parameters and renal biopsy was done within next three days. Univariate and multivariate analyses were used to determine the correlates and the independent predictors of TA/IF. Simultaneously, a nomogram based on risk indicators was created to predict TA/IF. Univariate and multivariate analyses showed that sex (p < 0.001, OR = 2.538, 95%CI: 1.739-3.734), renal length (p < 0.001, OR = 0.927, 95%CI: 0.905-0.95), resistive index of main renal artery (p = 0.037, OR = 1.891, 95%CI: 1.027-3.426), peak systolic velocity of segmental renal artery (p = 0.58, OR = 0.975, 95%CI: 0.399-0.841), and cortex echogenicity (p < 0.001, OR = 3.448, 95%CI: 2.382-5.018) were independent predictors of TA/IF in IgAN nomograms, with a good C-index of 0.765 (95%CI = 0.727-0.803). Analyses of the calibration charts show that nomograms have good performance and clinical applicability. In our study, renal color ultrasound parameters correlated well with TA/IF in IgAN. By establishing a conventional color ultrasound prediction model, we can accurately gauge the extent of TA/IF in patients with IgAN for clinical applications.

Value of contrast-enhanced ultrasonography in radiofrequency ablation of secondary hyperparathyroidism.

OBJECTIVES: The purpose of the current study was to determine the performance of contrast-enhanced ultrasound (CEUS) in the assessment of radiofrequency ablation (RFA) of hyperplastic parathyroid glands due to secondary hyperparathyroidism (SHPT). METHODS: Thirty-two patients, each with ≥4 hyperplastic parathyroid glands due to SHPT, underwent RFA via hydro-dissection. CEUS was performed in each patient before and during RFA. The patients in whom the intact parathyroid hormone (iPTH) level did not decrease to 300 pg/ml were examined by CEUS. The iPTH, serum calcium, and serum phosphorus levels before and after RFA were compared. RESULTS: Ablation was achieved in all patients (131 ablated glands). The volume of the glands was 479.88 ± 549.3mm3. The pre-operative and day 1 post-operative iPTH levels were 2355 ± 1062 and 292.7 ± 306.8 pg/ml, respectively. Three nodules in three patients showed little enhancement on CEUS on post-operative day 1. The iPTH level was <300 pg/mL on post-operative day 1 in 23 patients, which indicated complete ablation; follow-up evaluations were therefore performed. The pre- and post-operative iPTH levels in the 23 patients were 2113 ± 787.2 and 106.2 ± 84.62 pg/ml, respectively (p < 0.05), and the 6- and 12-month post-operative iPTH levels were 111.1 ± 56.57 and 117.6 ± 97.08 pg/ml, respectively (p > 0.05). CONCLUSIONS: CEUS-guided RFA is effective and feasible for the treatment of ≥4 hyperplastic parathyroid glands. CEUS was shown to assist the surgeon before, during, and after RFA. CEUS on post-operative day 2, but not immediately post-operatively, was shown to accurately reflect gland perfusion.

Diagnosis efficacy of CEUS for hepatic inflammatory lesions.

PURPOSE: In this study, the efficacy of US/CEUS and clinicopathologic parameters in differential diagnosis of hepatic inflammatory lesions were evaluated. METHODS: This was a retrospective study in which CEUS imaging was performed on 182 patients. Among these patients, 44 patients had hepatic inflammatory lesions and 138 patients had malignant lesions. The ultrasound (US), CEUS, and clinicopathologic parameters with respect to differential diagnosis of hepatic inflammatory lesions were analyzed. RESULTS: Irregular lesion shape and unclear margin were commonly seen in hepatic inflammatory lesions by US/CEUS examination. Hypoenhancement in arterial phase (AP) and portal venous phase (PVP), and isoenhancement in delayed phase (DP) were more commonly found in inflammatory lesions rather than malignant lesions [9% (4/44), 68% (30/44), and 16% (7/44) vs 2% (3/138), 11% (15/138), 1% (1/138), respectively; P < .05]. The enhancement coverage was also a significant indicator for the differentiation of inflammatory lesions and malignant lesions (P < .05). History of hepatitis or cirrhosis, and higher serum alpha-fetoprotein (AFP) level were indicators for malignant lesions, while liver parasites and higher body temperature were indicators for inflammatory lesions. When the US/CEUS findings were combined with clinicopathologic parameters, the diagnostic accuracy of inflammatory lesions could reach 93.3%, with sensitivity, specificity, positive predictive value, and negative predictive value of 63.64%, 96.03%, 84.85%, and 88.32%, respectively. CONCLUSION: The US/CEUS findings combined with clinical characteristics can accurately differentiate hepatic inflammatory lesions and malignant lesions. The results of study will improve the diagnostic confidence for hepatic inflammatory lesions.

Interpretable Machine Learning Model Based on Superb Microvascular Imaging for Non-Invasive Determination of Crescent Status of IgAN.

Purpose: To assess the crescentic status of IgA nephropathy (IgAN) non-invasively using a superb microvascular imaging (SMI)-based radiomics machine learning (ML) model. Patients and Methods: IgAN patients who underwent renal biopsy from June 2022 to October 2023, with two-dimensional ultrasound (US) and SMI examinations conducted one day prior to the renal biopsy. The patients selected were divided randomly into a training group and a test group in a 7:3 ratio. Radiomic features were extracted from US and SMI images, then radiomic features were constructed and ML models were further established using logistic regression (LR) and extreme gradient boosting (XGBoost)XGBoost to determine the crescentic status. The utility of the proposed model was evaluated using receiver operating characteristics, calibration, and decision curve analysis. The SHapley Additive exPlanations (SHAP) was utilized to explain the best-performing ML model. Results: A total of 147 IgAN patients were included in the study, with 103 in the training group and 44 in the test group .Among them, the US-SMI based XGBoost model achieved the best results, with an the area under the curve (AUC) of 0.839 (95% CI,0.756-0.910) and an accuracy of 78.6% in the training group.In the test group, the AUC was 0.859 (95% CI,0.721-0.964), and the accuracy was 81.8%, significantly surpassing the ML model of a single modality and the clinical model established based on occult blood. Additionally, the decision curve analysis (DCA) demonstrated that the XGBoost model provided a higher overall net benefit in the both groups. Conclusion: The SMI radiomics ML model has the capability to accurately predict the crescentic status of IgAN patients, providing effective assistance for clinical treatment decisions.

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.

An interpretable clinical ultrasound-radiomics combined model for diagnosis of stage I cervical cancer.

Objective: The purpose of this retrospective study was to establish a combined model based on ultrasound (US)-radiomics and clinical factors to predict patients with stage I cervical cancer (CC) before surgery. Materials and methods: A total of 209 CC patients who had cervical lesions found by transvaginal sonography (TVS) from the First Affiliated Hospital of Anhui Medical University were retrospectively reviewed, patients were divided into the training set (n = 146) and internal validation set (n = 63), and 52 CC patients from Anhui Provincial Maternity and Child Health Hospital and Nanchong Central Hospital were taken as the external validation set. The clinical independent predictors were selected by univariate and multivariate logistic regression analyses. US-radiomics features were extracted from US images. After selecting the most significant features by univariate analysis, Spearman's correlation analysis, and the least absolute shrinkage and selection operator (LASSO) algorithm, six machine learning (ML) algorithms were used to build the radiomics model. Next, the ability of the clinical, US-radiomics, and clinical US-radiomics combined model was compared to diagnose stage I CC. Finally, the Shapley additive explanations (SHAP) method was used to explain the contribution of each feature. Results: Long diameter of the cervical lesion (L) and squamous cell carcinoma-associated antigen (SCCa) were independent clinical predictors of stage I CC. The eXtreme Gradient Boosting (Xgboost) model performed the best among the six ML radiomics models, with area under the curve (AUC) values in the training, internal validation, and external validation sets being 0.778, 0.751, and 0.751, respectively. In the final three models, the combined model based on clinical features and rad-score showed good discriminative power, with AUC values in the training, internal validation, and external validation sets being 0.837, 0.828, and 0.839, respectively. The decision curve analysis validated the clinical utility of the combined nomogram. The SHAP algorithm illustrates the contribution of each feature in the combined model. Conclusion: We established an interpretable combined model to predict stage I CC. This non-invasive prediction method may be used for the preoperative identification of patients with stage I CC.

Prediction of Lymph Node Metastasis in Endometrial Cancer Based on Color Doppler Ultrasound Radiomics.

RATIONALE AND OBJECTIVES: To construct a model using radiomics features based on ultrasound images and evaluate the feasibility of noninvasive assessment of lymph node status in endometrial cancer (EC) patients. METHODS: In this multicenter retrospective study, a total of 186 EC patients who underwent hysterectomy and lymph node dissection were included, Pathology confirmed the presence or absence of lymph node metastasis (LNM). The study encompassed patients from seven centers, spanning from September 2018 to November 2023, with 93 patients in each group (with or without LNM). Extracted ultrasound radiomics features from transvaginal ultrasound images, used five machine learning (ML) algorithms to establish US radiomics models, screened clinical features through univariate and multivariate logistic regression to establish a clinical model, and combined clinical and radiomics features to establish a nomogram model. The diagnostic ability of the three models for LNM with EC was compared, and the diagnostic performance and accuracy of the three models were evaluated using receiver operating characteristic curve analysis. RESULTS: Among the five ML models, the XGBoost model performed the best, with AUC values of 0.900 (95% CI, 0.847-0.950) and 0.865 (95% CI, 0.763-0.950) for the training and testing sets, respectively. In the final model, the nomogram based on clinical features and the ultrasound radiomics showed good resolution, with AUC values of 0.919 (95% CI, 0.874-0.964) and 0.884 (0.801-0.967) in the training and testing sets, respectively. The decision curve analysis verified the clinical practicality of the nomogram. CONCLUSION: The ML model based on ultrasound radiomics has potential value in the noninvasive differential diagnosis of LNM in patients with EC. The nomogram constructed by combining ultrasound radiomics and clinical features can provide clinical doctors with more comprehensive and personalized image information, which is highly important for selecting treatment strategies.

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.

A Clinical-radiomic Nomogram for the Non-invasive Evaluation of Glomerular Status in Diabetic Kidney Disease.

OBJECTIVE: Utilizing ultrasound radiomics, we developed a machine learning (ML) model to construct a nomogram for the non-invasive evaluation of glomerular status in diabetic kidney disease (DKD). MATERIALS AND METHODS: Patients with DKD who underwent renal biopsy were retrospectively enrolled between February 2017 and February 2023. The patients were classified into mild or moderate-severe glomerular severity based on pathological findings. All patients were randomly divided into a training (n =79) or testing cohort (n = 35). Radiomic features were extracted from ultrasound images, and a logistic regression ML algorithm was applied to construct an ultrasound radiomic model after selecting the most significant features using univariate analysis and the least absolute shrinkage and selection operator algorithm (LASSO). A clinical model was created following univariate and multivariate logistic regression analyses of the patient's clinical characteristics. Then, the clinical-radiomic model was constructed by combining rad scores and independent clinical characteristics and plotting the nomogram. The receiver operating characteristic curve (ROC) and decision curve analysis (DCA), respectively, were used to evaluate the prediction abilities of the clinical model, ultrasound-radiomics model, and clinical-radiomics model. RESULTS: A total of 114 DKD patients were included in the study, including 43 with mild glomerulopathy and 71 with moderate-severe glomerulopathy. The area under the curve (AUC) for the clinical model based on clinical features and the radiomic model based on 2D ultrasound images in the testing cohort was 0.729 and 0.761, respectively. Further, the AUC for the clinical-radiomic nomogram was constructed by combining clinical features, and the rad score was 0.850 in the testing cohort. The outcomes were better than those of both the radiomic and clinical single-model approaches. CONCLUSION: The nomogram constructed by combining ultrasound radiomics and clinical features has good performance in assessing the glomerular status of patients with DKD and will help clinicians monitor the progression of DKD.

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.

Preoperative Evaluation of Hepatocellular Carcinoma Differentiation Using Contrast-Enhanced Ultrasound-Based Deep-Learning Radiomics Model.

Objective: Distinguishing the degree of differentiation, hepatocellular carcinoma (HCC) has important clinical significance in the therapeutic decision-making and patient prognosis evaluation. Methods: We developed a deep-learning radiomics (DLR) model based on contrast-enhanced ultrasound (CEUS) to evaluate the differentiation of HCC noninvasive. We retrospectively analyzed HCC patients who had undergone resection and CEUS one week preoperatively between November 2015 and August 2022. Enrolled patients were randomly divided into training (n=190) and testing (n=82) cohorts in a 7:3 ratio. The depth of learning and radiological characteristics reflecting the differentiation degree of HCC were extracted, and the least absolute shrinkage and selection operator(LASSO) was used for feature selection to obtain the most valuable features and then build a DLR model based on the useful features. Results: The deep-learning Radiomics model could accurately predict the degree of differentiation of HCC; the area under the curve of the DLR model in the training and testing cohorts was 0.969 and 0.932, respectively. The accuracy, sensitivity, and specificity of the CEUS-based DLR model for predicting the differentiation of HCC were 0.915, 0.938, and 0.900, respectively, in the testing cohort. The decision curve analysis confirmed that the combined model predicted good overall net income for differentiation. Conclusion: The CEUS-based DLR model provides an easy-to-use, visual, and personalized tool for predicting the differentiation of HCC and can help doctors formulate more favorable treatment plans for patients.

Contrast-Enhanced Ultrasound with Deep Learning with Attention Mechanisms for Predicting Microvascular Invasion in Single Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: Prediction of microvascular invasion (MVI) status of hepatocellular carcinoma (HCC) holds clinical significance for decision-making regarding the treatment strategy and evaluation of patient prognosis. We developed a deep learning (DL) model based on contrast-enhanced ultrasound (CEUS) to predict MVI of HCC. MATERIALS AND METHODS: We retrospectively analyzed the data for single primary HCCs that were evaluated with CEUS 1 week before surgical resection from December 2014 to February 2022. The study population was divided into training (n = 198) and test (n = 54) cohorts. In this study, three DL models (Resnet50, Resnet50+BAM, Resnet50+SE) were trained using the training cohort and tested in the test cohort. Tumor characteristics were also evaluated by radiologists, and multivariate regression analysis was performed to determine independent indicators for the development of predictive nomogram models. The performance of the three DL models was compared to that of the MVI prediction model based on radiologist evaluations. RESULTS: The best-performing model, ResNet50+SE model achieved the ROC of 0.856, accuracy of 77.2, specificity of 93.9%, and sensitivity of 52.4% in the test group. The MVI prediction model based on a combination of three independent predictors showed a C-index of 0.729, accuracy of 69.4, specificity of 73.8%, and sensitivity of 62%. CONCLUSION: The DL algorithm can accurately predict MVI of HCC on the basis of CEUS images, to help identify high-risk patients for the assist treatment.

A novel clinical-radiomic nomogram for the crescent status in IgA nephropathy.

Objective: We used machine-learning (ML) models based on ultrasound radiomics to construct a nomogram for noninvasive evaluation of the crescent status in immunoglobulin A (IgA) nephropathy. Methods: Patients with IgA nephropathy diagnosed by renal biopsy (n=567) were divided into training (n=398) and test cohorts (n=169). Ultrasound radiomic features were extracted from ultrasound images. After selecting the most significant features using univariate analysis and the least absolute shrinkage and selection operator algorithm, three ML algorithms were assessed for final radiomic model establishment. Next, clinical, ultrasound radiomic, and combined clinical-radiomic models were compared for their ability to detect IgA crescents. The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis. Results: The average area under the curve (AUC) of the three ML radiomic models was 0.762. The logistic regression model performed best, with AUC values in the training and test cohorts of 0.838 and 0.81, respectively. Among the final models, the combined model based on clinical characteristics and the Rad score showed good discrimination, with AUC values in the training and test cohorts of 0.883 and 0.862, respectively. The decision curve analysis verified the clinical practicability of the combined nomogram. Conclusion: ML classifier based on ultrasound radiomics has a potential value for noninvasive diagnosis of IgA nephropathy with or without crescents. The nomogram constructed by combining ultrasound radiomic and clinical features can provide clinicians with more comprehensive and personalized image information, which is of great significance for selecting treatment strategies.