Conventional Ultrasound Features Research Articles

Clinical and radiomics integrated nomogram for preoperative prediction of tumor-infiltrating lymphocytes in patients with triple-negative breast cancer.

The present study aimed to develop a radiomics nomogram based on conventional ultrasound (CUS) to preoperatively distinguish high tumor-infiltrating lymphocytes (TILs) and low TILs in triple-negative breast cancer (TNBC) patients. In the present study, 145 TNBC patients were retrospectively included. Pathological evaluation of TILs in the hematoxylin and eosin sections was set as the gold standard. The patients were randomly allocated into training dataset and validation dataset with a ratio of 7:3. Clinical features (age and CUS features) and radiomics features were collected. Then, the Rad-score model was constructed after the radiomics feature selection. The clinical features model and clinical features plus Rad-score (Clin+RS) model were built using logistic regression analysis. Furthermore, the performance of the models was evaluated by analyzing the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Univariate analysis and LASSO regression were employed to identify a subset of 25 radiomics features from a pool of 837 radiomics features, followed by the calculation of Rad-score. The Clin+RS integrated model, which combined posterior echo and Rad-score, demonstrated better predictive performance compared to both the Rad-score model and clinical model, achieving AUC values of 0.848 in the training dataset and 0.847 in the validation dataset. The Clin+RS integrated model, incorporating posterior echo and Rad-score, demonstrated an acceptable preoperative evaluation of the TIL level. The Clin+RS integrated nomogram holds tremendous potential for preoperative individualized prediction of the TIL level in TNBC.

Quantitative parameters of contrast-enhanced ultrasound effectively promote the prediction of cervical lymph node metastasis in papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC), the most common endocrine tumor, often spreads to cervical lymph nodes metastasis (CLNM). Preoperative diagnosis of CLNM is important when selecting surgical strategies. Therefore, we aimed to explore the effectiveness of quantitative parameters of contrast-enhanced ultrasound (CEUS) in predicting CLNM in PTC. We retrospectively analyzed 193 patients with PTC undergoing conventional ultrasound (CUS) and CEUS. The CUS features and quantitative parameters of CEUS were evaluated according to PTC size ≤ 10 or > 10 mm, using pathology as the gold standard. For the PTC ≤ 10 mm, microcalcification and multifocality were significantly different between the CLNM (+) and CLNM (-) groups (both P < 0.05). For the PTC > 10 mm, statistical significance was noted between the two groups with respect to the margin, capsule contact, and multifocality (all P < 0.05). For PTC ≤ 10 mm, there was no significant difference between the CLNM (+) and CLNM (-) groups in all quantitative parameters of CEUS (all P > 0.05). However, for PTC > 10 mm, the peak intensity (PI), mean transit time, and slope were significantly associated with CLNM (all P < 0.05). Multivariate analysis showed that PI > 5.8 dB was an independent risk factor for predicting CLNM in patients with PTC > 10 mm (P < 0.05). The area under the curve of PI combined with CUS (0.831) was significantly higher than that of CUS (0.707) or PI (0.703) alone in the receiver operator characteristic curve analysis (P < 0.05). In conclusion, PI has significance in predicting CLNM for PTC > 10 mm; however, it is not helpful for PTC ≤ 10 mm.

A Model Combining Conventional Ultrasound Characteristics, Strain Elastography and Clinicopathological Features to Predict Ki-67 Expression in Small Breast Cancer.

To establish a predictive model incorporating conventional ultrasound, strain elastography and clinicopathological features for Ki-67 expression in small breast cancer (SBC) which defined as maximum diameter less than2 cm. In this retrospective study, 165 SBC patients from our hospital were allocated to a high Ki-67 group (n = 104) and a low Ki-67 group (n = 61). Multivariate regression analysis was performed to identify independent indicators for developing predictive models. The area under the receiver operating characteristic (AUC) curve was also determined to establish the diagnostic performance of different predictive models. The corresponding sensitivities and specificities of different models at the cutoff value were compared. Conventional ultrasound parameters (spiculated margin, absence of posterior shadowing and Adler grade 2-3), strain elastic scores and clinicopathological information (HER2 positive) were significantly correlated with high expression of Ki-67 in SBC (all p < .05). Model 2, which incorporated conventional ultrasound features and strain elastic scores, yielded good diagnostic performance (AUC = 0.774) with better sensitivity than model 1, which only incorporated ultrasound characteristics (78.85%vs. 55.77%, p = .000), with specificities of 77.05% and 62.30% (p = .035), respectively. Model 3, which incorporated conventional ultrasound, strain elastography and clinicopathological features, yielded better performance (AUC = 0.853) than model 1 (AUC = 0.694) and model 2 (AUC = 0.774), and the specificity was higher than model 1 (86.89% vs. 77.05%, p = .001). The predictive model combining conventional ultrasound, strain elastic scores and clinicopathological features could improve the predictive performance of Ki-67 expression in SBC.

Predicting glypican-3 expression in hepatocellular carcinoma: A comprehensive analysis using combined contrast-enhanced ultrasound and clinical factors.

Glypican-3 (GPC3) has emerged as a significant marker for the diagnosis and prognosis of hepatocellular carcinoma (HCC) and has garnered considerable attention as an immunotherapeutic target. In this study, we propose a combination of preoperative contrast-enhanced ultrasound (CEUS) imaging features and clinical factors to predict the positive expression of GPC3 in HCC patients. We retrospectively included 30 cases of GPC3-negative HCC and 115 cases of GPC3-positive HCC patients who underwent conventional ultrasound and CEUS evaluation. We assessed and compared the clinical characteristics, conventional ultrasound features, and CEUS features between the two groups of HCC patients. Based on the clinical and ultrasound features between the two groups, we developed a binary logistic regression model for predicting GPC3-positive HCC. A total of 145 HCC patients were included in this study. Binary logistic regression analysis showed that AFP > 20 ng/mL (OR = 4.047; 95% CI: 1.467-11.16; p = 0.007), arterial phase hyperenhancement (APHE) (OR = 12.557; 95% CI: 3.608-43.706; p < 0.001), and asynchronous perfusion (OR = 4.209; 95% CI: 1.206-14.691; p = 0.024) were predictive factors for GPC3-positive HCC. Receiver operating characteristic (ROC) analysis was conducted to predict GPC3-positive expression. The model combining the three independent predictive factors showed good predictive performance (AUC 0.817, 95% CI: 0.731-0.902, sensitivity: 91.3%, specificity: 60.0%). This combined model demonstrated excellent discriminatory ability to predict GPC3-positive HCC. Preoperative integration of CEUS features and clinical factors can non-invasively and effectively identify GPC3-positive HCC patients, providing valuable assistance in making personalized treatment decisions.

Correlation between conventional ultrasound features combined with contrast-enhanced ultrasound patterns and pathological prognostic factors in malignant non-mass breast lesions.

To investigate the correlation between ultrasound performance and prognostic factors in malignant non-mass breast lesions (NMLs). This study included 106 malignant NMLs in 104 patients. Different US features and contrast enhancement patterns were evaluated. Prognostic factors, including histological types and grades, axillary lymph node and peritumoral lymphovascular status, estrogen and progesterone receptor status and the expression of HER-2 and Ki-67 were determined. A chi-square test and logistic regression analysis were used to analyse possible associations. Lesion size (OR: 3.08, p = 0.033) and posterior echo attenuation (OR: 8.38, p < 0.001) were useful in reflecting malignant NMLs containing an invasive carcinoma component. Posterior echo attenuation (OR: 7.51, p = 0.003) and unclear enhancement margin (OR: 6.50, p = 0.018) were often found in tumors with axillary lymph node metastases. Peritumoural lymphovascular invasion mostly exhibited posterior echo attenuation (OR: 3.84, p = 0.049) and unclear enhancement margin (OR: 8.68, p = 0.042) on ultrasound images. Perfusion defect was a comparatively accurate enhancement indicator for negative ER (OR: 2.57, p = 0.041) and PR (OR: 3.04, p = 0.008) expression. Calcifications (OR: 3.03, p = 0.025) and enlarged enhancement area (OR: 5.36, p = 0.033) imply an increased risk of positive HER-2 expression. Similarly, Calcifications (OR: 4.13, p = 0.003) and enlarged enhancement area (OR: 11.05, p < 0.001) were valid predictors of high Ki-67 proliferation index. Ultrasound performance is valuable for non-invasive prediction of prognostic factors in malignant NMLs.

Study on diagnosing thyroid nodules of ACR TI-RADS 4-5 with multimodal ultrasound radiomics technology.

Explore the feasibility of using the multimodal ultrasound (US) radiomics technology to diagnose American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) 4-5 thyroid nodules. This study prospectively collected the clinical characteristics, conventional, and US elastography images of 100 patients diagnosed with ACR TI-RADS 4-5 nodules from May 2022 to 2023. Independent risk factors for malignant thyroid nodules were extracted and screened using methods such as the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) model, and a multimodal US radiomics combined diagnostic model was established. Using a multifactorial LR analysis and a Rad-score rating, the predictive performance was validated and evaluated, and the final threshold range was determined to assess the clinical net benefit of the model. In the training set, the US radiomics combined predictive model area under curve (AUC = 0.928) had higher diagnostic performance compared with clinical characteristics (AUC = 0.779), conventional US (AUC = 0.794), and US elastography model (AUC = 0.852). In the validation set, the multimodal US radiomics combined diagnostic model (AUC = 0.829) also had higher diagnostic performance compared with clinical characteristics (AUC = 0.799), conventional US (AUC = 0.802), and US elastography model (AUC = 0.718). Multi-modal US radiomics technology can effectively diagnose thyroid nodules of ACR TI-RADS 4-5, and the combination of radiomics signature and conventional US features can further improve the diagnostic performance.

Contrast-Enhanced Ultrasound and Conventional Ultrasound Characteristics of Breast Cancer With Different Molecular Subtypes

BackgroundIdentifying molecular subtypes of breast cancer (BC) is of great significance in selecting optimal treatment strategy. Different molecular subtypes of BC have various vascular distribution characteristics. Contrast-enhanced ultrasound (CEUS) can dynamically display the microcirculation of tumor. This study intends to explore the conventional ultrasound and CEUS characteristics of different molecular subtypes of BC. MethodsDuring this prospective study, 86 patients with BC who were divided into Luminal A (LA), Luminal B (LB), HER2 over-expression (H2), and triple-negative (TN). The CEUS qualitative and quantitative characteristics of BC with different molecular subtypes was explored, as well as the conventional ultrasound features. In addition, the diagnostic efficiency of CEUS quantitative parameters in differentiating molecular subtypes of BC was analyzed. ResultsOur study found that the Adler grade differed significantly among 4 molecular subtypes (P < .05). The enhancement speed, enhancement degree and size after enhancement of 4 molecular subtypes were statistically different (P < .05). The wash in slope (WIS), peak intensity (PI), and wash-in area under the curve (WiAUC) differed significantly among 4 subtypes (P < .05). The diagnostic efficiency of PI was better for detecting LA and H2 subtype with the areas under the receiver operating characteristic curve was 0.778 and 0.734, respectively. ConclusionDifferent molecular subtypes of BC have different CEUS and conventional ultrasound characteristics. CEUS can provide valuable imaging basis for precise clinical diagnosis and individualized therapy of BC with different molecular subtypes.

Ultrasound-based radiomics combined with immune status to predict sentinel lymph node metastasis in primary breast cancer

In the past few years, the axillary lymph node dissection technique has been steadily replaced by sentinel lymph node biopsy for treating and diagnosing breast cancer, thereby minimizing the complications and sequelae of the patients. Nevertheless, sentinel lymph node biopsy still presents limitations, such as high operation requirements, prolonged surgical duration, and adverse reactions to tracer agents. This study developed a novel non-invasive method to predict sentinel lymph node metastasis in breast cancer by analyzing the ultrasound imaging characteristics of the primary tumor, combined with the analysis of peripheral blood T-cell subsets that reflect the immune status of the body. The radiomic features analyzed in this study were extracted from preoperative ultrasound images of 199 solitary breast cancer patients, who were undergoing surgery and were pathologically diagnosed at the Yancheng First People's Hospital. All cases were randomly categorized in a 4:1 ratio to the training (n = 159) and validation (n = 40) cohorts. The extracted radiomics features were subjected to dimensional reduction with the help of the least absolute shrinkage and selection operator technique, resulting in the inclusion of 19 radiomics features. Four classifiers, including naïve Bayesian, logistic regression, classification decision tree, and support vector machine, were utilized to model the radiomics features, conventional ultrasound features, and peripheral blood T cell subsets in the training dataset, and validated using the validation dataset. The best-performing model was chosen for constructing the combined model. The radiomics model constructed using the logistic regression showed the best performance, with the training and validation cohorts showing areas under the curve (AUCs) of 0.77 and 0.68, respectively. The conventional ultrasound and peripheral blood T cell models constructed using the classification decision tree showed the best performance, wherein the training cohort presented AUCs of 0.71 and 0.81, respectively, while the validation cohort presented AUCs of 0.68 and 0.69, respectively. The combined model constructed by logistic regression showed AUCs of 0.91 and 0.79 in the training and validation datasets, respectively. The resulting combined model can be considered a simple, non-invasive method with strong reproducibility and clinical significance. Thus, it can be utilized to predict sentinel lymph node metastasis in breast cancer. Furthermore, the combined model can be effectively used to guide clinical decisions related to the selection of surgical procedures in breast surgery.

Predicting the risk stratification of gastrointestinal stromal tumors using machine learning-based ultrasound radiomics.

This study aimed to use conventional ultrasound features, ultrasound radiomics, and machine learning algorithms to establish a predictive model to assess the risk of post-surgical recurrence of gastrointestinal stromal tumors (GISTs). This retrospective analysis included 230 patients with pathologically diagnosed GISTs. Radiomic features were extracted from manually annotated images. Radiomic features plus conventional ultrasound features were selected using the SelectKbest analysis of variance and stratified tenfold cross-validation recursive elimination methods. Finally, five different machine learning algorithms (logistic regression [LR], support vector machine [SVM], random forest [RF], extreme gradient boosting [XGBoost], and multilayer perceptron [MLP]) were established to predict risk stratification of GISTs. The predictive performance of the established model was mainly evaluated based on the area under the receiver operating characteristic (ROC) curve (AUC) and accuracy, whereas the predictive performance of the optimal machine learning algorithm and a radiologist's subjective assessment were compared using McNemar's test. Seven radiomics features and one conventional ultrasound feature were selected to construct the machine learning models for GIST risk classification. The mentioned five machine learning models were able to predict the malignant potential of GISTs. LR and SVM outperformed other classifiers on the test set, with LR achieving an accuracy of 0.852 (AUC, 0.881; sensitivity, 0.871; specificity, 0.826) and SVM achieving an accuracy of 0.852 (AUC, 0.879; sensitivity, 0.839; specificity, 0.870), and proved significantly better than the radiologist (accuracy, 0.691; sensitivity, 0.645; specificity, 0.813). Machine learning-based ultrasound radiomics features are able to noninvasively predict the biological risk of GISTs.

Preoperative prediction of microvascular invasion in hepatocellular carcinoma using ultrasound features including elasticity.

Microvascular invasion (MVI) is an important predictor of poor prognosis in patients with hepatocellular carcinoma (HCC). Accurate preoperative prediction of MVI in HCC would provide useful information to guide the choice of therapeutic strategy. Shear wave elastography (SWE) plays an important role in hepatic imaging, but its value in the preoperative prediction of MVI in HCC has not yet been proven. To explore the value of conventional ultrasound features and SWE in the preoperative prediction of MVI in HCC. Patients with a postoperative pathological diagnosis of HCC and a definite diagnosis of MVI were enrolled in this study. Conventional ultrasound features and SWE features such as maximal elasticity (Emax) of HCCs and Emax of the periphery of HCCs were acquired before surgery. These features were compared between MVI-positive HCCs and MVI-negative HCCs and between mild MVI HCCs and severe MVI HCCs. This study included 86 MVI-negative HCCs and 102 MVI-positive HCCs, including 54 with mild MVI and 48 with severe MVI. Maximal tumor diameters, surrounding liver tissue, color Doppler flow, Emax of HCCs, and Emax of the periphery of HCCs were significantly different between MVI-positive HCCs and MVI-negative HCCs. In addition, Emax of the periphery of HCCs was significantly different between mild MVI HCCs and severe MVI HCCs. Higher Emax of the periphery of HCCs and larger maximal diameters were independent risk factors for MVI, with odds ratios of 2.820 and 1.021, respectively. HCC size and stiffness of the periphery of HCC are useful ultrasound criteria for predicting positive MVI. Preoperative ultrasound and SWE can provide useful information for the prediction of MVI in HCCs.

Effect of caspase-1 (CASP1) combined with multimodal ultrasound features on the prognosis of breast cancer patients.

Breast cancer (BRCA) is the malignant tumor with the highest incidence rate among women in the world, and its mortality rate ranks second. The purpose of our study is to explore the correlation between caspase-1 (CASP1) and the prognosis of BRCA patients and the potential mechanism of action, and to analyze the clinical value of CASP1 combined with multimodal ultrasound features in early screening and prognosis of BRCA. We analyzed The Cancer Genome Atlas (TCGA) database to confirm that CASP1 was expressed in BRCA patients and determine whether its expression was correlated with patient prognosis. The relationship between CASP1 expression and survival was measured by the clinicopathological parameters. Multivariate analysis was performed using Cox regression, and a nomogram was developed using these results for quality assurance purposes. The correlations between CASP1 and immune cells were investigated using the Tumor Immune Estimation Resource (TIMER) and TCGA databases. Next, we performed gene set enrichment analysis (GSEA) to determine the potential mechanism of action. Finally, to analyze the effect of CASP1 combined with multimodal ultrasonography characteristics on the prognosis of BRCA patients was studied by analyzing the clinical data of patients. CASP1 expression was lower in BRCA tumor tissues than in the surrounding tissues. Patients with high CASP1 expression had better overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) than those with low CASP1 expression. GSEA suggested that CASP1 may affect the cell cycle, immune environment, inflammation, apoptosis, the HIPPOMERLIN pathway, Natural killer (NK) cell regulation of cytotoxicity, p53 expression, the Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway, the mitogen-activated protein kinase (MAPK) pathway, extracellular matrix, etc., thereby influencing the biological events in BRCA. Among conventional ultrasound features and contrast-enhanced ultrasound (CEUS) features, mass margin status and blood flow grade were associated with the expression of CASP1. Meanwhile, patients with poor ultrasound features tended to have low CASP1 expression. CASP1 may be a novel predictive marker for BRCA patients. CASP1 combined with multimodal ultrasound features has good clinical value in the early screening and prognostic prediction of BRCA.

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.

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. 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. 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. 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.

Preoperative strain ultrasound elastography can predict occult central cervical lymph node metastasis in papillary thyroid cancer: a single-center retrospective study.

To determine whether preoperative ultrasound elastography can predict occult central cervical lymph node metastasis (CCLNM) in patients with papillary thyroid cancer. This retrospective study included 541 papillary thyroid cancer patients with clinically negative lymph nodes prior to surgery between July 2019 and December 2021. Based on whether CCLNM was present on postoperative pathology, patients were categorized as CCLNM (+) or CCLNM (-). Preoperative clinical data, conventional ultrasound features, and ultrasound elastography indices were compared between the groups. Univariate and multivariate logistic regression analysis were performed to identify the independent predictors of occult CCLNM. A total of 36.60% (198/541) patients had confirmed CCLNM, while 63.40% (343/541) did not. Tumor location, bilaterality, multifocality, echogenicity, margin, shape, vascularity, capsule contact, extrathyroidal extension, aspect ratio, and shear wave elasticity parameters were comparable between the groups (all P > 0.05). Univariate analysis showed statistically significant differences between the two groups in age, sex, tumor size, calcification, capsule invasion, and strain rates ratio in strain ultrasound elastography (all P < 0.05). In multivariate logistic regression analysis, the independent predictors of occult CCLNM were age (OR = 0.975, 95% CI = 0.959-0.991, P = 0.002), sex (OR = 1.886, 95% CI = 1.220-2.915, P = 0.004), tumor size (OR = 1.054, 95% CI = 1.014-1.097, P = 0.008), and strain rates ratio (OR = 1.178, 95% CI = 1.065-1.304, P = 0.002). Preoperative strain ultrasound elastography can predict presence of occult CCLNM in papillary thyroid cancer patients and help clinicians select the appropriate treatment strategy.

Immunohistochemical Index Prediction of Breast Cancer by Using Hybrid Ultrasound Data.

The immunohistochemical index is significant to help the selection of treatment strategy for breast cancer patients. Existing studies that focus on conventional ultrasound features and certain types of immunohistochemistry expressions are limited to correlation exploration, and only few studies have built predictive models. In this study, a Tri-Branch deep learning network is built for prediction of the immunohistochemical HER2 using the hybrid ultrasound data, instead of relying on the invasive and biopsy-based histopathological examination. Specifically, the deep learning model uses the cross-model attention and the interactive learning approaches to obtain the strong complementarity of hybrid data comprising B-mode US, contrast-enhanced ultrasound, and optical flow motion information to enhance accuracy of immunohistochemical HER2 prediction. The proposed prediction model was evaluated using hybrid ultrasound dataset from 335 breast cancer patients. The experimental results indicated that the Tri-Branch model had a high accuracy of 86.23% for HER2 status prediction, and the HER2 status prediction for patients with different pathology grades exhibited some meaningful clinical observations.

Development and Deployment of a Novel Diagnostic Tool Based on Conventional Ultrasound for Fibrosis Assessment in Chronic Kidney Disease

Accurate identification of risk information about fibrosis severity is crucial for clinical decision-making and clinical management of patients with chronic kidney disease (CKD). This study aimed to develop an ultrasound (US)-derived computer-aided diagnosis tool for identifying CKD patients at high risk of developing moderate-severe renal fibrosis, in order to optimize treatment regimens and follow-up strategies. A total of 162 CKD patients undergoing renal biopsies and US examinations were prospectively enrolled and randomly divided into training (n=114) and validation (n=48) cohorts. A multivariate logistic regression approach was employed to develop the diagnostic tool named S-CKD for differentiating moderate-severe renal fibrosis from mild one in the training cohort by integrating the significant variables, which were screened out from demographic characteristics and conventional US features via the least absolute shrinkage and selection operator regression algorithm. The S-CKD was then deployed as both an online web-based and an offline document-based, easy-to-use auxiliary device. In both the training and validation cohorts, the S-CKD's diagnostic performance was evaluated through discrimination and calibration.The clinical benefit of using S-CKD was revealed by decision curve analysis (DCA) and clinical impact curves. The proposed S-CKD achieved an area under the receiver operating characteristic curve of 0.84 (95% confidence interval (CI): 0.77-0.91) and 0.81 (95% CI: 0.68-0.94) in the training and validation cohorts, respectively, indicating satisfactory diagnosis performance. Results of the calibration curves showed that S-CKD has excellent predictive accuracy (Hosmer-Lemeshow test: training cohort, p=0.497; validation cohort, p=0.205). The DCA and clinical impact curves exhibited a high clinical application value of the S-CKD at a wide range of risk probabilities. The S-CKD tool developed in this study is capable of discriminating between mild and moderate-severe renal fibrosis in patients with CKD and achieving promising clinical benefits, which may aid clinicians in personalizing medical decision-making and follow-up arrangement.

Preoperative prediction of lymphovascular invasion in patients with T1 breast invasive ductal carcinoma based on radiomics nomogram using grayscale ultrasound.

The aim of this study was to develop a radiomics nomogram based on grayscale ultrasound (US) for preoperatively predicting Lymphovascular invasion (LVI) in patients with pathologically confirmed T1 (pT1) breast invasive ductal carcinoma (IDC). One hundred and ninety-two patients with pT1 IDC between September 2020 and August 2022 were analyzed retrospectively. Study population was randomly divided in a 7: 3 ratio into a training dataset of 134 patients (37 patients with LVI-positive) and a validation dataset of 58 patients (19 patients with LVI-positive). Clinical information and conventional US (CUS) features (called clinic_CUS features) were recorded and evaluated to predict LVI. In the training dataset, independent predictors of clinic_CUS features were obtained by univariate and multivariate logistic regression analyses and incorporated into a clinic_CUS prediction model. In addition, radiomics features were extracted from the grayscale US images, and the radiomics score (Radscore) was constructed after radiomics feature selection. Subsequent multivariate logistic regression analysis was also performed for Radscore and the independent predictors of clinic_CUS features, and a radiomics nomogram was developed. The performance of the nomogram model was evaluated via its discrimination, calibration, and clinical usefulness. The US reported axillary lymph node metastasis (LNM) (US_LNM) status and tumor margin were determined as independent risk factors, which were combined for the construction of clinic_CUS prediction model for LVI in pT1 IDC. Moreover, tumor margin, US_LNM status and Radscore were independent predictors, incorporated as the radiomics nomogram model, which achieved a superior discrimination to the clinic_CUS model in the training dataset (AUC: 0.849 vs. 0.747; P < 0.001) and validation dataset (AUC: 0.854 vs. 0.713; P = 0.001). Calibration curve for the radiomic nomogram showed good concordance between predicted and actual probability. Furthermore, decision curve analysis (DCA) confirmed that the radiomics nomogram had higher clinical net benefit than the clinic_CUS model. The US-based radiomics nomogram, incorporating tumor margin, US_LNM status and Radscore, showed a satisfactory preoperative prediction of LVI in pT1 IDC patients.

Ultrasound-based radiomics nomogram combined with clinical features for the prediction of central lymph node metastasis in papillary thyroid carcinoma patients with Hashimoto's thyroiditis.

BackgroundHashimoto thyroiditis (HT) is the most common autoimmune thyroid disease and is considered an independent risk factor for papillary thyroid carcinoma (PTC), with a higher incidence of PTC in patients with HT.ObjectiveTo build an integrated nomogram using clinical information and ultrasound-based radiomics features in patients with papillary thyroid carcinoma (PTC) with Hashimoto thyroiditis (HT) to predict central lymph node metastasis (CLNM).MethodsIn total, 235 patients with PTC with HT were enrolled in this study, including 101 with CLNM and 134 without CLNM. They were divided randomly into training and validation datasets with a 7:3 ratio for developing and evaluating clinical features plus conventional ultrasound features (Clin-CUS) model and clinical features plus radiomics scores (Clin-RS) model, respectively. In the Clin-RS model, the Pyradiomics package (V1.3.0) was used to extract radiomics variables, and LASSO regression was used to select features and construct radiomics scores (RS). The Clin-CUS and Clin-RS nomogram models were built using logistic regression analysis.ResultsTwenty-seven CLNM-associated radiomics features were selected using univariate analysis and LASSO regression from 1488 radiomics features and were calculated to construct the RS. The integrated model (Clin-RS) had better diagnostic performance than the Clin-CUS model for differentiating CLNM in the training dataset (AUC: 0.845 vs. 0.778) and the validation dataset (AUC: 0.808 vs. 0.751), respectively.ConclusionOur findings suggest that applying an ultrasound-based radiomics approach can effectively predict CLNM in patients with PTC with HT. By incorporating clinical information and RS, the Clin-RS model can achieve a high diagnostic performance in diagnosing CLNM in patients with PTC with HT.

Nomogram based on radiomics analysis of ultrasound images can improve preoperative BRAF mutation diagnosis for papillary thyroid microcarcinoma.

BackgroundThe preoperative identification of BRAF mutation could assist to make appropriate treatment strategies for patients with papillary thyroid microcarcinoma (PTMC). This study aimed to establish an ultrasound (US) radiomics nomogram for the assessment of BRAF status.MethodsA total of 328 PTMC patients at the China-Japan Friendship Hospital between February 2019 and November 2021 were enrolled in this study. They were randomly divided into training (n = 232) and validation (n = 96) cohorts. Radiomics features were extracted from the US images. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the BRAF status-related features and calculate the radiomics score (Rad-score). Univariate and multivariate logistic regression analyses were subsequently performed to identify the independent factors among Rad-score and conventional US features. The US radiomics nomogram was established and its predictive performance was evaluated via discrimination, calibration, and clinical usefulness in the training and validation sets.ResultsMultivariate analysis indicated that the Rad-score, composition, and aspect ratio were independent predictive factors of BRAF status. The US radiomics nomogram which incorporated the three variables showed good calibration. The discrimination of the US radiomics nomogram showed better discriminative ability than the conventional US model both in the training set (AUC 0.685 vs. 0.592) and validation set (AUC 0.651 vs. 0.622). Decision curve analysis indicated the superior clinical applicability of the nomogram compared to the conventional US model.ConclusionsThe US radiomics nomogram displayed better performance than the conventional US model in predicting BRAF mutation in patients with PTMC.

Contrast-enhanced ultrasound–based nomogram for predicting malignant involvements among sonographically indeterminate/suspicious cervical lymph nodes in patients with differentiated thyroid carcinoma

This prospective study was aimed at assessing the value of nomograms based on conventional and contrast-enhanced ultrasound (CEUS) features in the pre-operative diagnosis of sonographically indeterminate/suspicious lymph node metastasis (LNM) in patients with differentiated thyroid carcinoma (DTC). A total of 72 cervical LNs from 47 patients with DTC from January to June 2018 were included in the primary data set, and 30 LNs from 15 patients with DTC from July to August 2018 were included in the external validation data set. The LNs of the included patients were preoperatively evaluated by conventional ultrasound (US) and CEUS. Each included LN was labeled by puncture localization with carbon nanoparticle suspension injection (Canalin) under US guidance and dissected separately to ensure the one-to-one correspondence between ultrasonic features and pathology status. Univariate logistic regression analysis was used to identify risk factors for LNM. A nomogram was used to construct a prediction model for cervical metastatic LNs. Round shape, absence of hilar structure, peripheral or mixed blood flow and centripetal or mass enhancement were risk factors for lymph node metastases. The area under the receiver operating characteristic curve of the nomogram model based on conventional US and CEUS features was 0.93 (95% confidence interval: 0.872-0.985), which was superior to that of the nomogram based on conventional US features(0.85, 95% confidence interval: 0.707-0.989). CEUS features can provide incremental benefit in the diagnosis of LNM among DTC cohorts. Nomograms based on conventional US and CEUS features can predict LN status with high accuracy.

Analysis of the Influence of Thyroid Nodule Characteristics on the Results of Shear Wave Elastography.

ObjectiveTo analyze the ultrasonic characteristics of false-negative and false-positive results of shear wave elastography (SWE) in the diagnosis of thyroid nodules to clarify the influence of nodular characteristics on SWE and to guide the clinical application of SWE.MethodsA total of 435 thyroid nodules from 343 patients with the diagnosis confirmed by surgical pathology were analyzed. Preoperative ultrasonography and SWE were conducted. The conventional ultrasound characteristics of thyroid nodules and the maximum Young’s modulus were recorded. The false negativity and false positivity of SWE for the diagnosis of thyroid nodules were calculated. The ultrasonic characteristics of thyroid nodules with SWE false results were analyzed, and logistic regression analysis was adopted to determine the ultrasonic characteristics associated with SWE false results of thyroid nodules.ResultsAmong 323 malignant nodules, the SWE false negativity was 27.2% (88/323). The false positivity of SWE in 112 benign nodules was 19.6% (22/112). Regression analysis showed that an increase in the nodule volume increased the risk of SWE false-positive results (odds ratio [OR] 3.286; 95% confidence interval [CI]: 1.572–6.871; P = 0.002) and decreased the risk of false-negative results (OR 0.238; 95% CI: 0.115–0.493; P < 0.001). Nodules with coarse calcification had an increased risk of SWE false-positive results compared with those without calcification (OR 5.303; 95% CI: 1.098–25.619; P = 0.038). However, nodules with scattered hyperechoic foci had a reduced risk of SWE false-negative results (OR 0.515; 95% CI: 0.280–0.951; P = 0.034).ConclusionNodular size and calcification were correlated with SWE false results, and the clinical application of SWE should be combined with conventional ultrasound features. Fine needle aspiration or a puncture biopsy should be conducted if necessary.