Multimodal Ultrasound Imaging Research Articles

Multiple techniques to evaluate the relationship between carotid artery plaque and acute stroke.

To evaluate the important characteristics of the plaque vulnerability using multimodal ultrasound imaging methods (2D, contrast-enhanced ultrasound, and elastography), and to explore the relationship between plaque and acute stroke. A total of 244 patients with carotid plaque were enrolled, including 104 patients with acute stroke ipsilateral to the plaque as the case group and 140 patients as the control group. All patients underwent conventional carotid ultrasound, contrast-enhanced ultrasound (CEUS) and elastography (SWE). The results of each examination were compared and analyzed, and the relationship between the results and the occurrence of stroke was discussed. In the acute stroke group, the men, with a history of alcohol consumption the direction of contrast media diffusion was higher than that in the control group, but the plaque gray value (GSM), maximum, average and minimum Young's elastic modulus imaging values (YM) were slightly lower than those in the control group (P < 0.05). Logistic regression analysis showed that waist to body ratio (WHtR), GSM, YM, neovascularization density and contrast diffusion direction were independent risk factors for predicting acute ischemic stroke. The influence degree of each factor from strong to weak was waist to body ratio, neovascularity density, GSM and YM, respectively. The area under the curve (AUC) for the diagnosis of acute ischemic stroke by regression model was 0.746. The combination of multiple ultrasound techniques to evaluate the vulnerability of carotid plaque and predict the occurrence of acute stroke provides valuable information for clinical decision making.

Multimodal ultrasound imaging: a method to improve the accuracy of sentinel lymph node diagnosis in breast cancer.

This study assessed the utility of multimodal ultrasound in enhancing the accuracy of breast cancer sentinel lymph node (SLN) assessment and compared it with single-modality ultrasound. Preoperative examinations, including two-dimensional ultrasound (2D US), intradermal contrast-enhanced ultrasound (CEUS), intravenous CEUS, shear-wave elastography (SWE), and surface localization, were conducted on 86 SLNs from breast cancer patients. The diagnostic performance of single and multimodal approaches for detecting metastatic SLNs was compared to postoperative pathological results. Among the 86 SLNs, 29 were pathologically diagnosed as metastatic, and 57 as non-metastatic. Single-modality ultrasounds had AUC values of 0.826 (intradermal CEUS), 0.705 (intravenous CEUS), 0.678 (2D US), and 0.677 (SWE), respectively. Intradermal CEUS significantly outperformed the other methods (p<0.05), while the remaining three methods had no statistically significant differences (p>0.05). Multimodal ultrasound, combining intradermal CEUS, intravenous CEUS, 2D US, and SWE, achieved an AUC of 0.893, with 86.21% sensitivity and 84.21% specificity. The DeLong test confirmed that multimodal ultrasound was significantly better than the four single-modal ultrasound methods (p<0.05). Decision curve analysis and clinical impact curves demonstrated the superior performance of multimodal ultrasound in identifying high-risk SLN patients. Multimodal ultrasound improves breast cancer SLN identification and diagnostic accuracy.

Enhancing malignancy prediction in thyroid nodules: A multimodal ultrasound radiomics approach in TI-RADS category 4 lesions.

To explore the diagnostic value of intralesional and perilesional radiomics based on multimodal ultrasound (US) images in predicting the malignant ACR TIRADS 4 thyroid nodules (TNs). A total of 297 cases of TNs in patients who underwent preoperative thyroid grayscale US and shear wave elastography (STE) were enrolled (training cohort: n = 150, internal validation cohort: n = 77, external validation cohort: n = 70). Regions of interests (ROIs) were delineated on grayscale US images and STE images, and then an isotropic expansion of 1.0, 1.5, 2.0, 2.5, and 3.0 mm was applied. Predictive models were established using recursive feature elimination-support vector machines (RFE-SVM) based on radiomics features calculated by random forest. The perilesional ROI1.5mm expansion achieved the highest area under curve (AUC) (AUC: 0.753 for grayscale US, 0.728 for STE; 95% confidence interval (CI): 0.664-0.743, 0.684-0.739, respectively). The joint model had the highest AUC values of 0.936 in the training dataset, 0.926 in internal dataset, and 0.893 in external dataset. The calibration curve showed good consistency and the decision curve indicated a greater clinical net benefit of the joint model. Joint model containing perilesional radiomics (1.5 mm) had significant value in predicting the malignant ACR TIRADS 4 TNs.

Multi-modal ultrasound multistage classification of PTC cervical lymph node metastasis via DualSwinThyroid.

This study aims to predict cervical lymph node metastasis in papillary thyroid carcinoma (PTC) patients with high accuracy. To achieve this, we introduce a novel deep learning model, DualSwinThyroid, leveraging multi-modal ultrasound imaging data for prediction. We assembled a substantial dataset consisting of 3652 multi-modal ultrasound images from 299 PTC patients in this retrospective study. The newly developed DualSwinThyroid model integrates various ultrasound modalities and clinical data. Following its creation, we rigorously assessed the model's performance against a separate testing set, comparing it with established machine learning models and previous deep learning approaches. Demonstrating remarkable precision, DualSwinThyroid achieved an AUC of 0.924 and an 96.3% accuracy on the test set. The model efficiently processed multi-modal data, pinpointing features indicative of lymph node metastasis in thyroid nodule ultrasound images. It offers a three-tier classification that aligns each level with a specific surgical strategy for PTC treatment. DualSwinThyroid, a deep learning model designed with multi-modal ultrasound radiomics, effectively estimates the degree of cervical lymph node metastasis in PTC patients. In addition, it also provides early, precise identification and facilitation of interventions for high-risk groups, thereby enhancing the strategic selection of surgical approaches in managing PTC patients.

Multimodal ultrasound imaging for diagnostic differentiation of sclerosing adenosis from invasive ductal carcinoma.

Sclerosing adenosis (SA) is a common proliferative benign lesion without atypia in the breast that may mimic invasive ductal carcinoma (IDC) on medical imaging, leading to it often being misdiagnosed and mistreated. Consequently, the purpose of this study was to assess the diagnostic value of multimodal ultrasound imaging in distinguishing SA from IDC. Multimodal ultrasound imaging, including automated breast volume scan (ABVS), elasticity imaging (EI), and color Doppler flow imaging (CDFI), were performed on 120 consecutive patients comprising 122 breast lesions (54 SA, 68 IDC). All lesions were pathologically confirmed. Multimodal ultrasound imaging features were compared between the two groups. Binary logistic regression analysis based on ABVS, EI, and CDFI was conducted to formulate a logistic regression equation for differentiating SA from IDC. The diagnostic performances of ABVS, EI, CDFI, and their combination were compared by the receiver operating characteristic (ROC) curve analysis. The sensitivity, specificity, and accuracy of ABVS, EI, CDFI, and their combination in differentiating SA from IDC were, respectively, 75.00%, 72.22%, and 73.77%; 86.76%, 72.22%, and 80.33%; 73.53%, 64.81%, and 69.67%; and 88.24%, 74.07%, and 81.97%. Combining multimodal ultrasound imaging yielded an area under the curve (AUC) of 0.895 (95% confidence interval: 0.827-0.943), which was higher than that of ABVS, EI, and CDFI, with AUC values of 0.736, 0.795, and 0.692, respectively, and the difference was statistically significant (ABVS vs. combined model, P<0.001; CDFI vs. combined model, P<0.001; EI vs. combined model, P<0.001). There was no significant difference in the diagnostic efficacy among the three imaging modalities (ABVS vs. EI, P=0.266; ABVS vs. CDFI, P=0.4671; EI vs. CDFI, P=0.051). Compared with those in IDC, the calcification (16.67% vs. 57.35%; P<0.001) and retraction phenomena in the coronal planes (18.52% vs. 57.35%; P<0.001) were less common in patients with SA, while circumscribed margin (38.89% vs. 5.88%; P<0.001), vascularity grade 0-I (64.81% vs. 26.47%; P<0.001), and elasticity scores 1-3 (72.22% vs. 13.24%; P<0.001) were more frequently found in patients with SA. Patients with SA were significantly younger than were patients with IDC (43±11 vs. 54±11 years; P<0.001), and the lesion size was smaller in patients with SA than in those with IDC (median size 1.0 cm; interquartile range (IQR), 0.9 cm vs. median size 1.3 cm; IQR, 1.3 cm; P<0.001). The preliminary results suggested that multimodal ultrasound imaging can improve the diagnostic accuracy of SA and provide additional information for differential diagnosis of SA and IDC.

Predictive value of multimodal ultrasound imaging and BCL-2 expression levels in the outcome of neoadjuvant chemotherapy for breast cancer

Predictive value of multimodal ultrasound imaging and BCL-2 expression levels in the outcome of neoadjuvant chemotherapy for breast cancer

Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis.

This retrospective study aimed to establish ultrasound radiomics models to predict central lymph node metastasis (CLNM) based on preoperative multimodal ultrasound imaging features fusion of primary papillary thyroid carcinoma (PTC). In total, 498 cases of unifocal PTC were randomly divided into two sets which comprised 348 cases (training set) and 150 cases (validition set). In addition, the testing set contained 120 cases of PTC at different times. Post-operative histopathology was the gold standard for CLNM. The following steps were used to build models: the regions of interest were segmented in PTC ultrasound images, multimodal ultrasound image features were then extracted by the deep learning residual neural network with 50-layer network, followed by feature selection and fusion; subsequently, classification was performed using three classical classifiers-adaptive boosting (AB), linear discriminant analysis (LDA), and support vector machine (SVM). The performances of the unimodal models (Unimodal-AB, Unimodal-LDA, and Unimodal-SVM) and the multimodal models (Multimodal-AB, Multimodal-LDA, and Multimodal-SVM) were evaluated and compared. The Multimodal-SVM model achieved the best predictive performance than the other models (P < 0.05). For the Multimodal-SVM model validation and testing sets, the areas under the receiver operating characteristic curves (AUCs) were 0.910 (95% CI, 0.894-0.926) and 0.851 (95% CI, 0.833-0.869), respectively. The AUCs of the Multimodal-SVM model were 0.920 (95% CI, 0.881-0.959) in the cN0 subgroup-1 cases and 0.828 (95% CI, 0.769-0.887) in the cN0 subgroup-2 cases. The ultrasound radiomics model only based on the PTC multimodal ultrasound image have high clinical value in predicting CLNM and can provide a reference for treatment decisions.

Review on ultrasonographic diagnosis of thyroid diseases based on deep learning

In recent years, the incidence of thyroid diseases has increased significantly and ultrasound examination is the first choice for the diagnosis of thyroid diseases. At the same time, the level of medical image analysis based on deep learning has been rapidly improved. Ultrasonic image analysis has made a series of milestone breakthroughs, and deep learning algorithms have shown strong performance in the field of medical image segmentation and classification. This article first elaborates on the application of deep learning algorithms in thyroid ultrasound image segmentation, feature extraction, and classification differentiation. Secondly, it summarizes the algorithms for deep learning processing multimodal ultrasound images. Finally, it points out the problems in thyroid ultrasound image diagnosis at the current stage and looks forward to future development directions. This study can promote the application of deep learning in clinical ultrasound image diagnosis of thyroid, and provide reference for doctors to diagnose thyroid disease.

Deep learning-based automatic scoring models for the disease activity of rheumatoid arthritis based on multimodal ultrasound images.

We aimed to investigate the value of deep learning (DL) models based on multimodal ultrasonographic (US) images to quantify RA activity. Static greyscale (SGS), dynamic greyscale (DGS), static power Doppler (SPD) and dynamic power Doppler (DPD) US images were collected and evaluated by two expert radiologists according to the EULAR-OMERACT Synovitis Scoring system. Four DL models were developed based on the ResNet-type structure, evaluated on two separate test cohorts, and finally compared with the performance of 12 radiologists with different levels of experience. In total, 1244 images were used for the model training, and 152 and 354 for testing (cohort 1 and 2, respectively). The best-performing models for the scores of 0/1/2/3 were the DPD, SGS, DGS and SPD models, respectively (Area Under the receiver operating characteristic Curve [AUC] = 0.87/0.95/0.74/0.95; no significant differences). All the DL models provided results comparable to the experienced radiologists on a per-image basis (intraclass correlation coefficient: 0.239-0.756, P < 0.05). The SPD model performed better than the SGS one on test cohort 1 (score of 0/2/3: AUC = 0.82/0.67/0.95 vs 0.66/0.66/0.75, respectively) and test cohort 2 (score of 0: AUC = 0.89 vs 0.81). The dynamic DL models performed better than the static ones in most of the scoring processes and were more accurate than the most of senior radiologists, especially the DPD model. DL models based on multimodal US images allow a quantitative and objective assessment of RA activity. Dynamic DL models in particular have potential value in assisting radiologists to improve the accuracy of RA US-based grading.

Advanced multimodal imaging of solid thyroid lesions with artificial intelligence-optimized B-mode, elastography, and contrast-enhanced ultrasonography parametric and with perfusion imaging: Initial results.

Goal of the study was the assessment of AI-assisted diagnosis of solid thyroid foci with multimodal modern ultrasound imaging. 50 patients (26-81 years, 54.7±13.1 years) were included in the study. Multimodal ultrasound imaging by means of B-mode with linear probe (4-12 MHz) with option of automated documentation of findings by means of AI, with supplementary Ultra MicroAngiography (UMA) was used. Macrovascularisation was assessed by dynamic contrast ultrasonography (CEUS) with parametric evaluation and perfusion analysis, and microvascularization was assessed by combined strain and shear wave elastography on a novel high-performance ultrasound system (Resona R9/Mindray) by an experienced examiner with independent reading. The evaluation was performed according to TI-RADS III-V.The volume of the thyroid lobes on both sides averaged 39 ml±5 ml (27 to 69 ml). The 13 cases of histologically confirmed thyroid carcinomas (8 papillary, 2 medullary, 2 microfollicular, 1 anaplastic CA) with a mean size of 15 mm±6 mm (9-21 mm) were correctly evaluated by TI-RADS V on the basis of irregular shape, induration > 2.5 m/s or > 30kPA and striking wash-out kinetics. Tumor lymph nodes could only be correctly detected preoperatively in one case of medullary carcinoma according to the surgical findings, based on irregular vascularization with UMA in roundish shape with cortex > 4 mm, transverse diameter up to 11 mm. In 25 cases of inhomogeneous nodular goiter an evaluation with TI-RADS III was performed in 31 cases, in 4 cases with incomplete marginal contour, partial marginal vascularization with UMA and partial wash out with indurations up to 2.5 m/s 30 kPA an evaluation with TI-RADS IV and surgical excision for nodular goiter. In 12 cases regressive nodular changes without relevant malignancy criteria resulted in nodular goiter, with focal changes up to 1.5 cm in diameter, classified as requiring control with TI-RADS III. There were no relevant changes in findings in the controls after 6 months. From the AI tool, the 20/25 goiter nodes were assessed as TI-RADS III, 7/12 adenomas, 5 goiter nodes, and 5 adenomas as TI-RADS IV, 5/13 carcinomas as TI-RADS IV, and 8/13 carcinomas as TI-RADS V.Multimodal ultrasound diagnostics supported by AI has a high diagnostic potential for the evaluation of solid thyroid lesions and standardizes the reporting with digital representative image documentation. CEUS perfusion and modern elastography techniques allow targeted follow-up of TI-RADS III findings.

A case report of multimodal ultrasound imaging in the diagnosis of giant retroperitoneal ganglioneuroma

AbstractRetroperitoneal ganglioneuroma is a rare benign tumor that is challenging in terms of clinical diagnosis. Computed tomography and magnetic resonance imaging are usually performed for diagnosis rather than convenient and inexpensive ultrasonography. Here, we present the case of a 21‐year‐old female patient who was diagnosed by multimodal ultrasound imaging and whose diagnosis was confirmed by ultrasound‐guided core needle biopsy before surgery. We hope that this rare case will help clinicians and radiologists realize the advantages of multimodal ultrasound imaging in the diagnosis of retropeitoneal solid tumors, and reduce misdiagnosis.

Multimodal ultrasound features of breast cancers: correlation with molecular subtypes

ObjectivesTo investigate whether multimodal intratumour and peritumour ultrasound features correlate with specific breast cancer molecular subtypes.MethodsFrom March to November 2021, a total of 85 patients with histologically proven breast cancer underwent B-mode, real-time elastography (RTE), colour Doppler flow imaging (CDFI) and contrast-enhanced ultrasound (CEUS). The time intensity curve (TIC) of CEUS was obtained, and the peak and time to peak (TTP) were analysed. Chi-square and binary logistic regression were used to analyse the connection between multimodal ultrasound imaging features and breast cancer molecular subtype.ResultsAmong 85 breast cancers, the subtypes were as follows: luminal A (36 cases, 42.4%), luminal B (20 cases, 23.5%), human epidermal growth factor receptor-2 positive (HER2+) (16 cases, 18.8%), and triple negative breast cancer (TNBC) (13 cases, 15.3%). Binary logistic regression models showed that RTE (P < 0.001) and CDFI (P = 0.036) were associated with the luminal A cancer subtype (C-index: 0.741), RTE (P = 0.016) and the peak ratio between intratumour and corpus mamma (P = 0.036) were related to the luminal B cancer subtype (C-index: 0.788). The peak ratio between peritumour and intratumour (P = 0.039) was related to the HER2 + cancer subtype (C-index: 0.859), and CDFI (P = 0.002) was associated with the TNBC subtype (C-index: 0.847).ConclusionsMultimodal ultrasound features could be powerful predictors of specific breast cancer molecular subtypes. The intra- and peritumour CEUS features play assignable roles in separating luminal B and HER2 + breast cancer subtypes.

Value of clinical features combined with multimodal ultrasound in predicting lymph node metastasis in cervical central area of papillary thyroid carcinoma.

To explore the clinical features, multimodal ultrasound features and multimodal ultrasound imaging features in predicting lymph node metastasis in the central cervical region of papillary thyroid carcinoma. A total of 129 patients with papillary thyroid carcinoma (PTC) confirmed by pathology were selected from our hospital from September 2020 to December 2022. According to the pathological results of cervical central lymph nodes, these patients were divided into metastatic group and non-metastatic group. Patients were randomly sampled and divided into training group (n = 90) and verification group (n = 39) according to the ratio of 7:3. The independent risk factors for central lymph node metastasis (CLNM) were determined by least absolute shrinkage and selection operator and multivariate logistic regression. Based on independent risk factors to build a prediction model, select the best diagnostic effectiveness of the prediction model sketch line chart, and finally, the line chart calibration and clinical benefits were evaluated. A total of 8, 11 and 17 features were selected from conventional ultrasound images, shear wave elastography (SWE) images and contrast-enhanced ultrasound (CEUS) images to construct the Radscore of conventional ultrasound, SWE and CEUS, respectively. After univariate and multivariate logistic regression analysis, male, multifocal, encapsulation, iso-high enhancement and multimodal ultrasound imaging score were independent risk factors for cervical CLNM in PTC patients (p < 0.05). Based on independent risk factors, a clinical combined with multimodal ultrasound feature model was constructed, and multimodal ultrasound Radscore were added to the clinical combined with multimodal ultrasound feature model to form a joint prediction model. In the training group, the diagnostic efficacy of combined model (AUC = 0.934) was better than that of clinical combined with multimodal ultrasound feature model (AUC = 0.841) and multimodal ultrasound radiomics model (AUC = 0.829). In training group and validation group, calibration curves show that the joint model has good predictive ability for cervical CLNM of PTC patients; The decision curve shows that most of the net benefits of the nematic chart are higher than those of clinical + multimodal ultrasound feature model and multimodal ultrasound radiomics model within a reasonable risk threshold range. Male, multifocal, capsular invasion and iso-high enhancement are independent risk factors of CLNM in PTC patients, and the clinical plus multimodal ultrasound model based on these four factors has good diagnostic efficiency. The joint prediction model after adding multimodal ultrasound Radscore to clinical and multimodal ultrasound features has the best diagnostic efficiency, high sensitivity and specificity, which is expected to provide objective basis for accurately formulating individualized treatment plans and evaluating prognosis.

Multimodal ultrasound imaging with conventional B-mode, elastography, and parametric analysis of contrast-enhanced ultrasound (CEUS): A novel approach to assess small bowel manifestation in severe COVID-19 disease.

The aim was to describe the small bowel morphology with conventional B-mode and elastography and additionally to evaluate dynamic effects of COVID-19 associated small bowel microvascularization using CEUS with color coded perfusion parameters.Thirteen patients with severe COVID-19 acute respiratory distress syndrome (ARDS) were investigated. 13 patients required intensive care treatment with mechanical ventilation. Five patients required extracorporeal membrane oxygenation (ECMO). Contrast-enhanced ultrasound (CEUS) was performed by an experienced investigator as a bolus injection of up to 2.4 ml sulfur hexafluoride microbubbles via a central venous catheter. In the parametric analysis of CEUS, the flare of microbubbles over time is visualized with colors. This is the first work using parametric analysis of CEUS to detect perfusion differences in the small bowel.Parametric analysis of CEUS in the intestinal phase was carried out, using DICOM loops for 20 seconds. In 5 patients, parametric analysis revealed intraindividual differences in contrast agent behavior in the small bowel region. Analogous to the computed tomography (CT) images parametric analysis showed regions of simultaneous hyper- and hypoperfusion of the small intestine in a subgroup of patients. In 5 patients, the parametric image of transmural global contrast enhancement was visualized.Our results using CEUS to investigate small bowel affection in COVID-19 suggest that in severe COVID-19 ARDS systemic inflammation and concomitant micro embolisms may lead to disruption of the epithelial barrier of the small intestine.This is the first study using parametric analysis of CEUS to evaluate the extent of small bowel involvement in severe COVID-19 disease and to detect microemboli. In summary, we show that in COVID-19 the small bowel may also be an important interaction site. This is in line with the fact that enterocytes have been shown to a plenitude of angiotensin converting enzyme (ACE)-2 receptors as entry sites of the virus.

Non-mass Breast Lesions: Could Multimodal Ultrasound Imaging Be Helpful for Their Diagnosis?

To develop a prediction model for discriminating malignant from benign breast non-mass-like lesions (NMLs) using conventional ultrasound (US), strain elastography (SE) of US elastography and contrast-enhanced ultrasound (CEUS). A total of 101 NMLs from 100 patients detected by conventional US were enrolled in this retrospective study. The characteristics of NMLs in conventional US, SE and CEUS were compared between malignant and benign NMLs. Histopathological results were used as the reference standard. Binary logistic regression analysis was performed to identify the independent risk factors. A multimodal method to evaluate NMLs based on logistic regression was developed. The diagnostic performance of conventional US, US + SE, US + CEUS and the combination of these modalities was evaluated and compared. Among the 101 lesions, 50 (49.5%) were benign and 51 (50.5%) were malignant. Age ≥45 y, microcalcifications in the lesion, elasticity score &gt;3, earlier enhancement time and hyper-enhancement were independent diagnostic indicators included to establish the multimodal prediction method. The area under the receiver operating characteristic curve (AUC) of US + SE + CEUS was significantly higher than that of US (p &lt; 0.0001) and US + SE (p &lt; 0.0001), but there was no significant difference between the AUC of US + SE + CEUS and the AUC of US + CEUS (p = 0.216). US + SE + CEUS and US + CEUS could significantly improve the diagnostic efficiency and accuracy of conventional US in the diagnosis of NMLs.

Deep learning for the diagnosis of suspicious thyroid nodules based on multimodal ultrasound images.

This study aimed to differentially diagnose thyroid nodules (TNs) of Thyroid Imaging Reporting and Data System (TI-RADS) 3-5 categories using a deep learning (DL) model based on multimodal ultrasound (US) images and explore its auxiliary role for radiologists with varying degrees of experience. Preoperative multimodal US images of 1,138 TNs of TI-RADS 3-5 categories were randomly divided into a training set (n = 728), a validation set (n= 182), and a test set (n = 228) in a 4:1:1.25 ratio. Grayscale US (GSU), color Doppler flow imaging (CDFI), strain elastography (SE), and region of interest mask (Mask) images were acquired in both transverse and longitudinal sections, all of which were confirmed by pathology. In this study, fivefold cross-validation was used to evaluate the performance of the proposed DL model. The diagnostic performance of the mature DL model and radiologists in the test set was compared, and whether DL could assist radiologists in improving diagnostic performance was verified. Specificity, sensitivity, accuracy, positive predictive value, negative predictive value, and area under the receiver operating characteristics curves (AUC) were obtained. The AUCs of DL in the differentiation of TNs were 0.858 based on (GSU+ SE), 0.909 based on (GSU + CDFI), 0.906 based on (GSU + CDFI + SE), and 0.881 based (GSU + Mask), which were superior to that of 0.825-based single GSU (p = 0.014, p< 0.001, p< 0.001, and p = 0.002, respectively). The highest AUC of 0.928 was achieved by DL based on (G + C + E + M)US, the highest specificity of 89.5% was achieved by (G + C + E)US, and the highest accuracy of 86.2% and sensitivity of 86.9% were achieved by DL based on (G + C + M)US. With DL assistance, the AUC of junior radiologists increased from 0.720 to 0.796 (p< 0.001), which was slightly higher than that of senior radiologists without DL assistance (0.796 vs. 0.794, p > 0.05). Senior radiologists with DL assistance exhibited higher accuracy and comparable AUC than that of DL based on GSU (83.4% vs. 78.9%, p = 0.041; 0.822 vs. 0.825, p = 0.512). However, the AUC of DL based on multimodal US images was significantly higher than that based on visual diagnosis by radiologists (p< 0.05). The DL models based on multimodal US images showed exceptional performance in the differential diagnosis of suspicious TNs, effectively increased the diagnostic efficacy of TN evaluations by junior radiologists, and provided an objective assessment for the clinical and surgical management phases that follow.

RATING: Medical knowledge-guided rheumatoid arthritis assessment from multimodal ultrasound images via deep learning

SummaryMultimodal ultrasound has demonstrated its power in the clinical assessment of rheumatoid arthritis (RA). However, for radiologists, it requires strong experience. In this paper, we propose a rheumatoid arthritis knowledge guided (RATING) system that automatically scores the RA activity and generates interpretable features to assist radiologists' decision-making based on deep learning. RATING leverages the complementary advantages of multimodal ultrasound images and solves the limited training data problem with self-supervised pretraining. RATING outperforms all of the existing methods, achieving an accuracy of 86.1% on a prospective test dataset and 85.0% on an external test dataset. A reader study demonstrates that the RATING system improves the average accuracy of 10 radiologists from 41.4% to 64.0%. As an assistive tool, not only can RATING indicate the possible lesions and enhance the diagnostic performance with multimodal ultrasound but it can also enlighten the road to human-machine collaboration in healthcare.

Multimodal ultrasound imaging: A method to improve the accuracy of diagnosing thyroid TI-RADS 4 nodules.

Thyroid nodule is a common and frequently occurring disease in the neck in recent years, and ultrasound has become the preferred imaging diagnosis method for thyroid nodule due to its advantages of noninvasive, nonradiation, real-time, and repeatable. The thyroid imaging, reporting and data system (TI-RADS) classification standard scores suspicious nodules that are difficult to determine benign and malignant as grade 4, and further pathological puncture is recommended clinically, which may lead to a large number of unnecessary biopsies and operations. Including conventional ultrasound, ACR TI-RADS, shear wave elastography, super microvascular imaging, contrast enhanced ultrasound, "firefly," artificial intelligence, and multimodal ultrasound imaging used in combination. In order to identify the most clinically significant malignant tumors when reducing invasive operations. This article reviews the application and research progress of multimodal ultrasound imaging in the diagnosis of TI-RADS 4 thyroid nodules.

Multi-region radiomics for artificially intelligent diagnosis of breast cancer using multimodal ultrasound

PurposeThe ultrasound (US) diagnosis of breast cancer is usually based on a single-region of a whole breast tumor from a single ultrasonic modality, which limits the diagnostic performance. Multiple regions on multimodal US images of breast tumors may all have useful information for diagnosis. This study aimed to propose a multi-region radiomics approach with multimodal US for artificially intelligent diagnosis of malignant and benign breast tumors. Materials and methodsFirstly, radiomics features were extracted from five regions of interest (ROIs) on B-mode US and contrast-enhanced ultrasound (CEUS) images, including intensity statistics, gray-level co-occurrence matrix texture features and binary texture features. The multiple ROIs included the whole tumor region, strongest perfusion region, marginal region and surrounding region. Secondly, a deep neural network, composed of the point-wise gated Boltzmann machine and the restricted Boltzmann machine, was adopted to comprehensively learn and select features. Thirdly, the support vector machine was used for classification between benign and malignant breast tumors. Finally, five single-region classification models were generated from five ROIs, and they were fused to form an integrated classification model. ResultsExperimental evaluation was conducted on multimodal US images of breast from 187 patients with breast tumors (68 malignant and 119 benign). Under five-fold cross-validation, the classification accuracy, sensitivity, specificity, Youden's index and area under the receiver operating characteristic curve (AUC) with our model were 87.1% ± 3.3%, 77.4% ± 11.8%, 92.4% ± 7.2%, 69.8% ± 8.6% and 0.849 ± 0.043, respectively. Our model was significantly better than single-region single-modal methods in terms of the AUC and accuracy (p < 0.05). ConclusionIn addition to the whole tumor region, the other regions including the strongest perfusion region, marginal region and surrounding region on US images can assist breast cancer diagnosis. The multi-region multimodal radiomics model achieved the best classification results. Our artificially intelligent model would be potentially useful for clinical diagnosis of breast cancer.

Gastrocnemius Muscle Contraction and Its Role in Orthostatic Anti-Gravity Adjustment – The Effects of Body Mass Index

BACKGROUND: Obesity is a global concern with severe detrimental health and economic effect. Body mass index (BMI) is an inexpensive, non-invasive indicator for different diseases, and associated with abnormal weight. AIM: The aim of the study was to investigate the relationship between BMI and the contraction of gastrocnemius muscle (GM) and its possible role in peripheral muscle pump activity and pathogenesis of orthostatic intolerance. METHODS: One hundred and four volunteers (63 women and 41 men, mean age 39 ± 14 years) were divided into three subgroups according to their BMI values (with normal weight, overweight, and obesity). Changes in the transverse diameter and pennation angle of GM at rest and during maximal active plantar flexion (MAPF) were measured with multimodal ultrasound imaging. An active orthostatic test was performed and changes in systemic blood pressure and heart rate in supine position and active standing on the 1st, 5th, and 10th min were monitored. The results were statistically processed with alternative, variational, and correlational analysis. RESULTS: Patients with abnormal BMI were significantly older and had higher anthropometrical parameters compared to the subgroup with normal weight. They showed a significantly larger diameter and pennation angles at rest and during maximal active plantar flexion of GM bilaterally, which was more pronounced for the dominant right leg. However, the amount of changes in the GM diameter and pennation angles was similar in the different subgroups. CONCLUSION: The study showed that BMI affects predominantly the initial values of GM parameters at rest and MAPF without influence on its antigravity contractility associated with active straightening.