Prospective evaluation of contrast-enhanced ultrasound of breast BI-RADS 3-5 lesions

Meta-analysis of contrast-enhanced ultrasound in differential diagnosis of breast adenosis and breast cancer

This systematic review and meta-analysis study aimed to determine the total capacity of contrast-enhanced ultrasound (CEUS) in the differential diagnosis of breast lesions and breast cancer. For collecting papers, four groups of keywords were searched in five databases. The required information was extracted from the selected papers. In addition to the descriptive findings, a meta-analysis was also conducted. Thirty-three of thirty-six studies (91.67%) on the differential diagnosis of various degrees and types of breast lesions showed that CEUS has proper performance. The pooled values related to the sensitivity and specificity of CEUS were computed by 88.00 and 76.17.

Current status and future perspectives of contrast-enhanced ultrasound diagnosis of breast lesions

Advances in various imaging modalities for breast lesions have improved diagnostic capabilities not only for tumors but also for non-tumorous lesions. Contrast-enhanced ultrasound (CEUS) plays a crucial role not only in the differential diagnosis of breast lesions, identification of sentinel lymph nodes, and diagnosis of lymph node metastasis but also in assessing the therapeutic effects of neoadjuvant chemotherapy (NAC). In CEUS, two image interpretation approaches, i.e., qualitative analysis and quantitative analysis, are employed and applied in various clinical settings. In this paper, we review CEUS for breast lesions, including its various applications.

Adding contrast-enhanced ultrasound can improve the predictive ability of breast conventional ultrasound and mammography for pathological upgrade of biopsy-confirmed ductal carcinoma in situ

OBJECTIVES

To evaluate the added value of contrast-enhanced ultrasound (CEUS) on top of breast conventional imaging for predicting the upgrading of ductal carcinoma in situ (DCIS) to invasive cancer after surgery.

METHODS

This retrospective study enrolled 140 biopsy-proven DCIS lesions in 138 patients and divided them into two groups based on postoperative histopathology: non-upgrade and upgrade groups. Conventional ultrasound (US), mammography (MMG), CEUS and clinicopathological (CL) features were reviewed and compared between the two groups. The predictive performance of different models (with and without CEUS features) for histologic upgrade were compared to calculate the added value of CEUS.

RESULTS

Fifty-nine (42.1 %) lesions were histologically upgraded to invasive cancer after surgery. By logistic regression analyses, we found that high-grade DCIS at biopsy (P=0.004), ultrasonographic lesion size > 20 mm (P=0.007), mass-like lesion on US (P=0.030), the presence of suspicious calcification on MMG (P=0.014), the presence of perfusion defect (P=0.005) and the area under TIC>1021.34 ml (P<0.001) on CEUS were six independent factors predicting concomitant invasive components after surgery. The CL+US+MMG model made with the four predictors in the clinicopathologic, US and MMG categories yielded an area under the receiver operating curve (AUROC) value of 0.759 (95 % CI: 0.680-0.828) in predicting histological upgrade. The combination model built by adding the two CEUS predictors to the CL+US+MMG model showed higher predictive efficacy than the CL+US+MMG model (P=0.018), as the AUROC value was improved to 0.861 (95 % CI: 0.793-0.914).

CONCLUSIONS

The addition of contrast-enhanced ultrasound to breast conventional imaging could improve the preoperative prediction of an upgrade to invasive cancer from CNB -proven DCIS lesions.

Evaluating diagnostic significance: The utilization of elastography and contrast-enhanced ultrasound for differential diagnosis in breast lesions

OBJECTIVE

The primary aim of this study is to assess the diagnostic efficacy of elastography and contrast-enhanced ultrasound (CEUS) in the identification of breast lesions subsequent to the optimization and correction of the BI-RADS category 4 classification obtained through conventional ultrasound. The objective is to augment both the specificity and accuracy of breast lesion diagnosis, thereby establishing a reliable framework for reducing unnecessary biopsies in clinical settings.

METHODS

A cohort comprising 50 cases of breast lesions classified under BI-RADS category 4 was collected during the period from November 2022 and November 2023. These cases were examined utilizing strain elastography (SE), shear wave elastography (SWE), and CEUS. Novel scoring methodologies for ultrasonic elastography (UE) and CEUS were formulated for this investigation. Subsequently, the developed UE and CEUS scoring systems were used to refine and optimize the conventional BI-RADS classification, either in isolation or in conjunction. Based on the revised classification, the benign group was classified as category 3 and the suspected malignant group was classified as category 4a and above, with pathological results serving as the definitive reference standard. The diagnostic efficacy of the optimized UE and CEUS, both independently and in combination, was meticulously scrutinized and compared using receiver operating characteristic (ROC) curve analysis, with pathological findings as the reference standard.

RESULTS

Within the study group, malignancy manifested in 11 cases. Prior to the implementation of the optimization criteria, 78% (39 out of 50) of patients underwent biopsies deemed unnecessary. Following the application of optimization criteria, specifically a threshold of≥8.5 points for the UE scoring method and≥6.5 points for the CEUS scoring method, the incidence of unnecessary biopsies diminished significantly. Reduction rates were observed at 53.8% (21 out of 39) with the UE protocol, 56.4% (22 out of 39) with the CEUS protocol, and 89.7% (35 out of 39) with the combined UE and CEUS optimization protocols.

CONCLUSION

The diagnostic efficacy of conventional ultrasound BI-RADS category 4 classification for breast lesions is enhanced following optimized correction using UE and CEUS, either independently or in conjunction. The application of the combined protocol demonstrates a notable reduction in the incidence of unnecessary biopsies.

A Nomogram for Enhancing the Diagnostic Effectiveness of Solid Breast BI-RADS 3-5 Masses to Determine Malignancy Based on Imaging Aspects of Conventional Ultrasonography and Contrast-Enhanced Ultrasound

BACKGROUND

To establish and validate a nomogram model, which can incorporate clinical data, and imaging features of ultrasound (US) and contrast-enhanced ultrasound (CEUS), for improving the diagnostic efficiency of solid breast lesions.

PATIENTS AND METHODS

A total of 493 patients with solid breast lesions were randomly divided into training (n = 345) and validation (n = 148) cohorts with a ratio of 7:3 and, clinical data and image features of US and CEUS were reviewed and retrospectively analyzed. The breast lesions in both the training and validation cohorts were analyzed using the BI-RADS and nomogram models.

RESULTS

Five variables, including the shape and calcification features of conventional US, enhancement type and size after enhancement features of CEUS, and BI-RADS, were selected to construct the nomogram model. As compared to the BI-RADS model, the nomogram model demonstrated satisfactory discriminative function (area under the receiver operating characteristic [ROC] curves [AUC], 0.940; 95% confidence interval [CI], 0.909 to 0.971; sensitivity, 0.905; and specificity, 0.902 in the training cohort and AUC, 0.968; 95% CI, 0.941 to 0.995; sensitivity, 0.971; and specificity, 0.867 in the validation cohort). In addition, the nomogram model showed good consistency and clinical potential according to the calibration curve and DCA.

CONCLUSION

The nomogram model could identify benign from malignant breast lesions with good performance.

Ceusia-Breast: computer-aided diagnosis with contrast enhanced ultrasound image analysis for breast lesions

BACKGROUND

In recent years, contrast-enhanced ultrasonography (CEUS) has been used for various applications in breast diagnosis. The superiority of CEUS over conventional B-mode imaging in the ultrasound diagnosis of the breast lesions in clinical practice has been widely confirmed. On the other hand, there have been many proposals for computer-aided diagnosis of breast lesions on B-mode ultrasound images, but few for CEUS. We propose a semi-automatic classification method based on machine learning in CEUS of breast lesions.

METHODS

The proposed method extracts spatial and temporal features from CEUS videos and breast tumors are classified as benign or malignant using linear support vector machines (SVM) with combination of selected optimal features. In the proposed method, tumor regions are extracted using the guidance information specified by the examiners, then morphological and texture features of tumor regions obtained from B-mode and CEUS images and TIC features obtained from CEUS video are extracted. Then, our method uses SVM classifiers to classify breast tumors as benign or malignant. During SVM training, many features are prepared, and useful features are selected. We name our proposed method "Ceucia-Breast" (Contrast Enhanced UltraSound Image Analysis for BREAST lesions).

RESULTS

The experimental results on 119 subjects show that the area under the receiver operating curve, accuracy, precision, and recall are 0.893, 0.816, 0.841 and 0.920, respectively. The classification performance is improved by our method over conventional methods using only B-mode images. In addition, we confirm that the selected features are consistent with the CEUS guidelines for breast tumor diagnosis. Furthermore, we conduct an experiment on the operator dependency of specifying guidance information and find that the intra-operator and inter-operator kappa coefficients are 1.0 and 0.798, respectively.

CONCLUSION

The experimental results show a significant improvement in classification performance compared to conventional classification methods using only B-mode images. We also confirm that the selected features are related to the findings that are considered important in clinical practice. Furthermore, we verify the intra- and inter-examiner correlation in the guidance input for region extraction and confirm that both correlations are in strong agreement.

Value of ultrasound BI‑RADS classification in preoperative evaluation of the ultrasound‑guided Mammotome‑assisted minimally invasive resection of breast masses: A retrospective analysis

The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) classification has been used for the diagnosis of breast masses for several decades and constantly updated, but the terminology used to describe breast ultrasound findings is still evolving and a great amount of large sample data is necessary to verify and improve ultrasound BI-RADS. The objective of the present study was to explore the value of ultrasound Breast Imaging Reporting and Data System (US BI-RADS) classification in the preoperative evaluation of the US-guided Mammotome-assisted minimally invasive resection of breast masses. A total of 1,028 patients with 1,341 breast masses from a single hospital were selected for retrospective analysis. All patients underwent minimally invasive resection using a US-guided Mammotome device, and postoperative pathological examinations were performed for all samples. The preoperative US BI-RADS classification and postoperative pathological examination results were compared and analyzed. A receiver operating characteristic (ROC) curve was used to analyze the preoperative evaluation efficacy of the US BI-RADS classification in US-guided Mammotome-assisted minimally invasive breast mass resection. Among the 1,341 breast masses that underwent resection, 1,307 were benign and 34 were malignant. The specificity, sensitivity, accuracy, positive predictive value and negative predictive value of the US BI-RADS classification in the preoperative diagnosis of malignant breast masses were 83.47, 100.00, 83.89, 13.60 and 100.00%, respectively, and the area under the ROC curve was 0.917. It may be concluded that the US BI-RADS classification has a good preoperative diagnostic performance and can provide an accurate assessment prior to Mammotome-assisted minimally invasive resection. It may help surgeons to make reasonable decisions for subsequent therapy and therefore is worthy of further clinical use.

Ultrasound super-resolution imaging for differential diagnosis of breast masses

OBJECTIVE

Ultrasound imaging has been widely used in breast cancer screening. Recently, ultrasound super-resolution imaging (SRI) has shown the capability to break the diffraction limit to display microvasculature. However, the application of SRI on differential diagnosis of breast masses remains unknown. Therefore, this study aims to evaluate the feasibility and clinical value of SRI for visualizing microvasculature and differential diagnosis of breast masses.

METHODS

B mode, color-Doppler flow imaging (CDFI) and contrast-enhanced ultrasound (CEUS) images of 46 patients were collected respectively. SRI were generated by localizations of each possible contrast signals. Micro-vessel density (MVD) and microvascular flow rate (MFR) were calculated from SRI and time to peak (TTP), peak intensity (PI) and area under the curve (AUC) were obtained by quantitative analysis of CEUS images respectively. Pathological results were considered as the gold standard. Independent chi-square test and multivariate logistic regression analysis were performed using these parameters to examine the correlation.

RESULTS

The results showed that SRI technique could be successfully applied on breast masses and display microvasculature at a significantly higher resolution than the conventional CDFI and CEUS images. The results showed that the PI, AUC, MVD and MFR of malignant breast masses were significantly higher than those of benign breast masses, while TTP was significantly lower than that of benign breast masses. Among all five parameters, MVD showed the highest positive correlation with the malignancy of breast masses.

CONCLUSIONS

SRI is able to successfully display the microvasculature of breast masses. Compared with CDFI and CEUS, SRI can provide additional morphological and functional information for breast masses. MVD has a great potential in assisting the differential diagnosis of breast masses as an important imaging marker.

Diagnostic value of radiomics model based on gray-scale and contrast-enhanced ultrasound for inflammatory mass stage periductal mastitis/duct ectasia

Objective

The present study aimed to investigate the clinical application value of the radiomics model based on gray-scale ultrasound (GSUS) and contrast-enhanced ultrasound (CEUS) images in the differentiation of inflammatory mass stage periductal mastitis/duct ectasia (IMSPDM/DE) and invasive ductal carcinoma (IDC).

Methods

In this retrospective study, 254 patients (IMSPDM/DE: 129; IDC:125) were enrolled between January 2018 and December 2020 as a training cohort to develop the classification models. The radiomics features were extracted from the GSUS and CEUS images. The least absolute shrinkage and selection operator (LASSO) regression model was employed to select the corresponding features. Based on these selected features, logistic regression analysis was used to aid the construction of these three radiomics signatures (GSUS, CEUS and GSCEUS radiomics signature). In addition, 80 patients (IMSPDM/DE:40; IDC:40) were recruited between January 2021 and November 2021 and were used as the validation cohort. The best radiomics signature was selected. Based on the clinical parameters and the radiomics signature, a classification model was built. Finally, the classification model was assessed using nomogram and decision curve analyses.

Results

Three radiomics signatures were able to differentiate IMSPDM/DE from IDC. The GSCEUS radiomics signature outperformed the other two radiomics signatures and the AUC, sensitivity, specificity, and accuracy were estimated to be 0.876, 0.756, 0.804, and 0.798 in the training cohort and 0.796, 0.675, 0.838 and 0.763 in the validation cohort, respectively. The lower patient age (p&lt;0.001), higher neutrophil count (p&lt;0.001), lack of pausimenia (p=0.023) and GSCEUS radiomics features (p&lt;0.001) were independent risk factors of IMSPDM/DE. The classification model that included the clinical factors and the GSCEUS radiomics signature outperformed the GSCEUS radiomics signature alone (the AUC values of the training and validation cohorts were 0.962 and 0.891, respectively). The nomogram was applied to the validation cohort, reaching optimal discrimination, with an AUC value of 0.891, a sensitivity of 0.888, and a specificity of 0.750.

Conclusions

The present study combined the clinical parameters with the GSCEUS radiomics signature and developed a nomogram. This GSCEUS radiomics-based classification model could be used to differentiate IMSPDM/DE from IDC in a non-invasive manner.

Characterizing breast masses using an integrative framework of machine learning and CEUS-based radiomics

AIMS

We evaluated the performance of contrast-enhanced ultrasound (CEUS) based on radiomics analysis to distinguish benign from malignant breast masses.

METHODS

131 women with suspicious breast masses (BI-RADS 4a, 4b, or 4c) who underwent CEUS examinations (using intravenous injection of perflutren lipid microsphere or sulfur hexafluoride lipid-type A microspheres) prior to ultrasound-guided biopsies were retrospectively identified. Post biopsy pathology showed 115 benign and 16 malignant masses. From the cine clip of the CEUS exams obtained using the built-in GE scanner software, breast masses and adjacent normal tissue were then manually segmented using the ImageJ software. One frame representing each of the four phases: precontrast, early, peak, and delay enhancement were selected post segmentation from each CEUS clip. 112 radiomic metrics were extracted from each segmented tissue normalized breast mass using custom Matlab® code. Linear and nonlinear machine learning (ML) methods were used to build the prediction model to distinguish benign from malignant masses. tenfold cross-validation evaluated model performance. Area under the curve (AUC) was used to quantify prediction accuracy.

RESULTS

Univariate analysis found 35 (38.5%) radiomic variables with p < 0.05 in differentiating between benign from malignant masses. No feature selection was performed. Predictive models based on AdaBoost reported an AUC = 0.72 95% CI (0.56, 0.89), followed by Random Forest with an AUC = 0.71 95% CI (0.56, 0.87).

CONCLUSIONS

CEUS based texture metrics can distinguish between benign and malignant breast masses, which can, in turn, lead to reduced unnecessary breast biopsies.

Contrast-enhanced ultrasound of granular cell tumor in breast: A case report with review of the literature

Granular cell tumor is an infrequent, predominantly benign tumor originating from Schwann cells. Granular cell tumor of the breast (GCTB) can simulate breast malignant carcinoma on the clinical assessment. We herein present a rare case of GCTB which recurred in the contralateral breast. We believe the contrast-enhanced ultrasound (CEUS) findings of GCTB have never been described. The high similarity of breast malignant carcinoma makes its differential diagnosis difficult on the clinical and radiological features. In this report, we present the CEUS findings from a rare case of GCTB, explore the possible value of CEUS in differential diagnosis between benign breast lesions and malignant ones, and briefly review the literature.

Diagnostic value of contrast-enhanced ultrasound in intravenous leiomyomatosis: a single-center experiences

Objective

Intravenous leiomyomatosis (IVL) is a rare disease, and few studies have focused on the diagnostic value of contrast-enhanced ultrasound (CEUS) in this condition. This study aimed to investigate the diagnostic value of CEUS in IVL and summarize the specific CEUS characteristics of IVL.

Materials and Method

From December 2016 to March 2021, 93 patients admitted to our hospital with inferior vena cava (IVC) occupying lesions were prospectively enrolled and underwent detailed ultrasound multi-modality examinations, including conventional and contrast-enhanced ultrasound scans. The diagnostic value of CEUS and conventional ultrasound (CU) in IVL was compared, and the specific IVL signs were summarized.

Results

Among the 93 patients with inferior vena cava mass, 67 were IVL while 26 were non-IVL. The inter-observer agreement of the two senior doctors was good, with Kappa coefficient = 0.71 (95% CI: 0.572–0.885). The area under the ROC curve of CU for IVL diagnosis was 0.652 (95% CI: 0.528–0.776), and its sensitivity, specificity, accuracy, positive predictive value, negative predictive value, missed diagnosis rate, and misdiagnosis rate were 61.1%, 69.2%, 63.4%, 83.7%, 40.9%, 38.8%, and 30.8%, respectively. The area under curve (AUC) for IVL diagnosis by CEUS was 0.807 (95% CI: 0.701–0.911), and the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, missed diagnosis rate, and misdiagnosis rate were 82.0%, 84.6%, 82.8%, 93.2%, 64.7%, 15.4%, and 17.9%, respectively. In CEUS mode, “sieve hole sign” and “multi-track sign” were detected in 57 lesions, and the detected rate was higher than that of CU (https://loop.frontiersin.org/people/1014187 < 0.01).

Conclusion

CEUS can better show the fine blood flow inside the IVL, which is important for IVL differential diagnosis. Moreover, CEUS can obtain more information about IVL diagnosis than CU, compensating for the shortcomings of CU in detecting more blood flow within the lesion. Thus, this technique has great significance for IVL diagnosis.

Diagnostic Significance of 3D Automated Breast Volume Scanner in a Combination with Contrast-Enhanced Ultrasound for Breast Cancer

The incidence of cancer is increasing today, particularly lung and chest cancer. Employing novel methods to detect cancer in its earliest stages and discover painless, noninvasive treatments are urgently needed. The goal of the proposed study is to investigate the value of automated breast volume scanning (ABUS) in conjunction with contrast-enhanced ultrasonography (CEUS) in properly diagnosing breast cancer in its early stages and the effectiveness of neoadjuvant chemotherapy (NAC) in treating the disease. For the research study, information on 98 patients who had NAC and surgery in the breast surgery department of the Shaanxi Provincial Cancer Hospital has been gathered. All patients have received four cycles of NAC and underwent conventional ultrasound (HUSS), CEUS, ABUS, and pathological examination. At the same time, receiver operating characteristic (ROC) curve analysis, single factor, multiple linear regression, and other methods have also been used to analyze the diagnostic efficacy of breast cancer and NAC efficacy evaluation results. The study of this paper is totally based on the data collected from Shaanxi Provincial Cancer Hospital. The statistical and computational analyses are performed on the data collected for drawing inferences. When the findings are compared to the results of the pathological examination, HUSS has demonstrated a significant distinction between benign and malignant diagnoses with a statistical value of P < 0.05.ABUS combined with CEUS has shown no considerable differences in correlation study. Except for negative likelihood ratio, the diagnostic performance indexes of CEUS+ ABUS are substantially higher than HHUS with P < 0.05. ROC curve analysis is also performed which shows that CEUS and ABUS combination has higher precision in the analysis of breast cancer. ABUS pooled with CEUS shows great application value in the judgment of breast cancer as per the results obtained from the statistical analysis on data of 98 patients.

The clinical value of conventional ultrasound combined with contrast-enhanced ultrasound in the evaluation of BI-RADS 4 lesions detected by magnetic resonance imaging

OBJECTIVE

To determine the value of conventional ultrasound combined with contrast-enhanced ultrasound (CEUS) in Breast Imaging Reporting and Data System (BI-RADS) Category 4 lesions as detected by MRI.

METHODS

A total of 176 breast lesions from 171 patients were detected by MRI and categorised as BI-RADS 4. All patients also underwent ultrasound and CEUS scans. The combination of ultrasound-BI-RADS and CEUS 5-point scoring system created the Rerated BI-RADS (referred to as CEUS-BI-RADS). The diagnostic performances of ultrasound and CEUS-BI-RADS were then compared. A χ2 test was used to compare the CEUS features of mass-like and non-mass-like enhancement types of MRI-BI-RADS 4 lesions.

RESULTS

There were 167 (167/176) breast lesions detected by ultrasound, with a detection rate of 94.89%, while all were subsequently detected by "second-look" ultrasound combined with CEUS, with a detection rate of 100%. The areas under the receiver operating characteristic curves for ultrasound and CEUS-BI-RADS were 0.810 and 0.940, respectively. The diagnostic efficiency of CEUS-BI-RADS was significantly higher than that of ultrasound alone (z = 3.264, p = 0.001). For both mass-like and non-mass-like enhancement types of MRI-BI-RADS 4 lesions, CEUS-BI-RADS demonstrated satisfactory sensitivity and accuracy. Moreover, 29 (29/176) category 4 lesions were downgraded to 3 by CEUS-BI-RADS.

CONCLUSION

Ultrasound combined with CEUS can allow reclassification, reduce biopsy rates, and facilitate pre-surgical localisation for biopsy or surgery for MRI-BI-RADS 4 lesions.

ADVANCES IN KNOWLEDGE

For MRI-BI-RADS Category 4 lesions with a wide range of malignancies, ultrasound combined with CEUS is a promising diagnostic approach with high clinical utility.

Establishment of a deep-learning system to diagnose BI-RADS4a or higher using breast ultrasound for clinical application

Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI‐RADS) has become widespread worldwide, the problem of inter‐observer variability remains. To maintain uniformity in diagnostic accuracy, we have developed a system in which artificial intelligence (AI) can distinguish whether a static image obtained using a breast ultrasound represents BI‐RADS3 or lower or BI‐RADS4a or higher to determine the medical management that should be performed on a patient whose breast ultrasound shows abnormalities. To establish and validate the AI system, a training dataset consisting of 4028 images containing 5014 lesions and a test dataset consisting of 3166 images containing 3656 lesions were collected and annotated. We selected a setting that maximized the area under the curve (AUC) and minimized the difference in sensitivity and specificity by adjusting the internal parameters of the AI system, achieving an AUC, sensitivity, and specificity of 0.95, 91.2%, and 90.7%, respectively. Furthermore, based on 30 images extracted from the test data, the diagnostic accuracy of 20 clinicians and the AI system was compared, and the AI system was found to be significantly superior to the clinicians (McNemar test, p < 0.001). Although deep‐learning methods to categorize benign and malignant tumors using breast ultrasound have been extensively reported, our work represents the first attempt to establish an AI system to classify BI‐RADS3 or lower and BI‐RADS4a or higher successfully, providing important implications for clinical actions. These results suggest that the AI diagnostic system is sufficient to proceed to the next stage of clinical application.

Differentiation Between Granulomatous Lobular Mastitis and Breast Cancer Using Quantitative Parameters on Contrast-Enhanced Ultrasound

Objective

To investigate the Contrast-enhanced ultrasound (CEUS) imaging characteristics of granulomatous lobular mastitis (GLM) and the value of differentiating GLM from breast cancer.

Materials and methods

The study included 30 women with GLM (mean age 36.7 ± 5 years [SD]) and 58 women with breast cancer (mean age 48. ± 8 years [SD]) who were scheduled for ultrasound-guided tissue biopsy. All patients were evaluated with conventional US and CEUS prior to the biopsy. In both groups, the parameters of the quantitative and qualitative analysis of the CEUS were recorded and compared. The receiver-operating-characteristics curves (ROC) were created. Sensitivity, specificity, cut-off, and area under the curve (AUC) values were calculated.

Results

TTP values in GLM were statistically higher than in breast cancer (mean, 27.63 ± 7.29 vs. 20.10 ± 6.11), but WIS values were lower (mean, 0.16 ± 0.05 vs. 0.28 ± 0.17). Rich vascularity was discovered in 54.45% of breast cancer patients, but only 30.00% of GLM patients had rich vascularity. The AUC for the ROC test was 0.791 and 0.807, respectively. The optimal cut-off value for TTP was 24.5s, and the WIS cut-off value was 0.185dB/s, yielding 73.33% sensitivity, 84.48% specificity, and 86.21% sensitivity, 70% specificity respectively in the diagnosis of GLM. The lesion scores reduced from 4 to 3 with the addition of CEUS for the patients with GLM. However, the scores did not change for the patients with breast cancer.

Conclusion

CEUS could help distinguish GLM from breast cancer by detecting higher TTP and WIS values, potentially influencing clinical decision-making for additional biopsies.

Contrast-Enhanced Ultrasonography for Differential Diagnosis of Benign and Malignant Thyroid Lesions: Single-Institutional Prospective Study of Qualitative and Quantitative CEUS Characteristics

Objectives

To extend and revise the diagnostic value of contrast-enhanced ultrasonography (CEUS) for differentiation between malignant and benign thyroid nodules.

Methods

This single-institution prospective study aims to compare CEUS qualitative and objective quantitative parameters in benign and malignant thyroid nodules. Consecutive cohort of 100 patients was examined by CEUS, 68 out of them were further analysed in detail. All included patients underwent cytological and/or histopathological verification of the diagnosis.

Results

Fifty-five (81%) thyroid nodules were benign, and 13 (19%) were malignant. Ring enhancement pattern was strongly associated with a benign aetiology (positive predictive value 100%) and heterogeneous enhancement pattern with malignant aetiology (positive predictive value 72.7%). The shape of the TIC (time-intensity curve) was more often identical in the benign lesion (98.2%) than in malignant lesions (69.2%), p=0.004.

Conclusions

This study indicates that CEUS enhancement patterns were significantly different in benign and malignant lesions. Ring enhancement was a very strong indicator of benign lesions, whereas heterogeneous enhancement was valuable to detect malignant lesions.

Contrast-enhanced ultrasound of breast tumors: an initial experience

Background

The increase of neoadjuvant treatment for breast cancer creates a capacity challenge as response evaluation by magnetic resonance imaging (MRI) is a limited resource. Contrast-enhanced ultrasound (CEUS) has been proposed as an alternative imaging strategy.

Purpose

To get experience with examination of malignant breast tumors with CEUS and evaluate the potential for future use in response evaluation of neoadjuvant treatment.

Material and methods

In this pilot study, the dynamic contrast-enhancement of ultrasound and MRI examinations were analyzed in 14 women with histologically verified breast cancer.

Results

Analysis of the time intensity curve of CEUS demonstrated the difference between tumor and normal tissue. The peak intensity was five times higher in tumor tissue (mean increase 397%, 95% CI 250–545). The curve was steeper for tumor tissue (mean 1.76, 95% CI 1.26–2.26) than for normal tissue (mean 0.43, 95% CI 0.24–0.62).

Conclusion

CEUS is a feasible method of examining blood flow in malignant breast tumors.

The diagnostic value of contrast-enhanced ultrasound and superb microvascular imaging in differentiating benign from malignant solid breast lesions: A systematic review and meta-analysis

OBJECTIVE

To investigate the added value of contrast-enhanced ultrasound (CEUS) and superb microvascular imaging (SMI) to the conventional ultrasound (US) in the diagnosis of breast lesions.

METHODS

PubMed, EMBASE, Web of Science, Chinese national knowledge infrastructure databases, Chinese biomedical literature databases, and Wanfang were searched for relevant studies from November 2015 to November 2021. The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Studies (QUADAS) tool. Meta-Disc version 1.4 was used to calculate sensitivity (SEN), specificity (SPE), positive likelihood ratio (LR +), negative likelihood ratio (LR-), area under curve (AUC), and diagnostic odds ratio (DOR). Meta-regression analysis was performed using STATA 16.0 software to compare the diagnostic accuracy of the two techniques.

RESULTS

In the five studies included, 530 patients were eligible for this meta-analysis. For SMI, the pooled SEN and SPE were 0.75 (95% confidence interval [CI]: 0.69-0.91) and 0.88 (95% CI: 0.83-0.91), respectively, LR + was 5.75 (95% CI: 4.26-7.78), LR- was 0.29 (95% CI: 0.23-0.36), DOR was 21.42 (95% CI, 13.61-33.73), and AUC was 0.8871. For CEUS, the pooled SEN and SPE were 0.87 (95% CI: 0.82-0.91) and 0.86 (95% CI: 0.82-0.89), respectively, LR + was 5.92 (95% CI: 4.21-8.33), LR- was 0.16 (95% CI: 0.11-0.25), DOR was 38.27 (95% CI: 18.73-78.17), and AUC was 0.9210.

CONCLUSIONS

Adding CEUS and (or) SMI to conventional US could improve its diagnostic performance in differentiating benign from malignant solid breast lesions.

Quantification and Classification of Contrast Enhanced Ultrasound Breast Cancer Data: A Preliminary Study

This study aimed to investigate which of the two frequently adopted perfusion models better describes the contrast enhanced ultrasound (CEUS) perfusion signal in order to produce meaningful imaging markers with the goal of developing a machine-learning model that can classify perfusion curves as benign or malignant in breast cancer data. Twenty-five patients with high suspicion of breast cancer were analyzed with exponentially modified Gaussian (EMG) and gamma variate functions (GVF). The adjusted R² metric was the criterion for assessing model performance. Various classifiers were trained on the quantified perfusion curves in order to classify the curves as benign or malignant on a voxel basis. Sensitivity, specificity, geometric mean, and AUROC were the validation metrics. The best quantification model was EMG with an adjusted R² of 0.60 ± 0.26 compared to 0.56 ± 0.25 for GVF. Logistic regression was the classifier with the highest performance (sensitivity, specificity, G_mean, and AUROC = 89.2 ± 10.7, 70.0 ± 18.5, 77.1 ± 8.6, and 91.0 ± 6.6, respectively). This classification method obtained similar results that are consistent with the current literature. Breast cancer patients can benefit from early detection and characterization prior to biopsy.

Breast microscopic cancer segmentation and classification using unique 4-qubit-quantum model

The visual inspection of histopathological samples is the benchmark for detecting breast cancer, but a strenuous and complicated process takes a long time of the pathologist practice. Deep learning models have shown excellent outcomes in clinical diagnosis and image processing and advances in various fields, including drug development, frequency simulation, and optimization techniques. However, the resemblance of histopathologic images of breast cancer and the inclusion of stable and infected tissues in different areas make detecting and classifying tumors on entire slide images more difficult. In breast cancer, a correct diagnosis is needed for complete care in a limited amount of time. An effective detection can relieve the pathologist's workload and mitigate diagnostic subjectivity. Therefore, this research work investigates improved the pre-trained xception and deeplabv3+ design semantic model. The model has been trained on input images with ground masks on the tuned parameters that significantly improve the segmentation of ultrasound breast images into respective classes, that is, benign/malignant. The segmentation model delivered an accuracy of greater than 99% to prove the model's effectiveness. The segmented images and histopathological breast images are transferred to the 4-qubit-quantum circuit with six-layered architecture to detect breast malignancy. The proposed framework achieved remarkable performance as contrasted to currently published methodologies. HIGHLIGHTS: This research proposed hybrid semantic model using pre-trained xception and deeplabv3 for breast microscopic cancer classification in to benign and malignant classes at accuracy of 95% accuracy, 99% accuracy for detection of breast malignancy.

Color-map virtual touch tissue imaging (CMV) combined with BI-RADS for the diagnosis of breast lesions

OBJECTIVE

To investigate the importance of color-map virtual touch tissue imaging (CMV) in assisting Breast Imaging Reporting and Data Systems (BI-RADS) in diagnosing malignant breast lesions.

METHODS

A dataset included 134 patients and 146 breast lesions was assembled. All patients underwent biopsy or surgical excision of breast lesions, and pathological results were obtained. All patients with breast lesions also underwent conventional ultrasound (US) and CMV. Each lesion was assigned a CMV score based on the color pattern of the lesion and surrounding breast tissue and a BI-RADS classification rating based on US characteristics. We compared the diagnostic performance of using BI-RADS and CMV separately and their combination.

RESULTS

BI-RADS (odds ratio [OR]: 3.665; 95% confidence interval [CI]: 2.147, 6.258) and CMV (OR: 6.616; 95% CI: 2.272, 19.270) were independent predictors of breast malignancy (all P < 0.05). The area under the receiver operating characteristic curves (AUC) for either CMV or BI-RADS alone was inferior to that of the combination (0.877 vs. 0.962; 0.938 vs. 0.962; all P < 0.05).

CONCLUSIONS

The performance of BI-RADS in diagnosing breast lesions is significantly improved by combining CMV. Therefore, we recommend CMV as an adjunct to BI-RADS.