Breast Ultrasound Microvascular Imaging and Radiogenomics

Combined use of super-resolution ultrasound imaging and shear-wave elastography for differential diagnosis of breast masses

Objectives

Shear-wave elastography (SWE) provides valuable stiffness within breast masses, making it a useful supplement to conventional ultrasound imaging. Super-resolution ultrasound (SRUS) imaging enhances microvascular visualization, aiding in the differential diagnosis of breast masses. Current clinical ultrasound diagnosis of breast cancer primarily relies on gray-scale ultrasound. The combined diagnostic potential of tissue stiffness and microvascular characteristics, two critical tumor biomarkers, remains insufficiently explored. This study aims to evaluate the correlation between the elastic modulus, assessed using SWE, and microvascular characteristics captured through SRUS, in order to evaluate the effectiveness of combining these techniques in distinguishing between benign and malignant breast masses.

Materials and methods

In this single-center prospective study, 97 patients underwent SWE to obtain parameters including maximum elasticity (Emax), minimum elasticity (Emin), mean elasticity (Emean), standard deviation of elasticity (Esd), and elasticity ratio. SRUS was used to calculate the microvascular flow rate and microvessel density (MVD) within the breast masses. Spearman correlation analysis was used to explore correlations between Emax and MVD. Receiver operating characteristic curves and nomogram were employed to assess the diagnostic efficacy of combining SRUS with SWE, using pathological results as the gold standard.

Results

Emax, Emean, Esd, and MVD were significantly higher in malignant breast masses compared to benign ones (p < 0.001), while Emin was significantly lower in malignant masses (p < 0.05). In Spearman correlation analysis, Emax was significantly positively correlated with MVD (p < 0.01). The area under the curve for SRUS combined with SWE (0.924) was significantly higher than that for SWE (0.883) or SRUS (0.830) alone (p < 0.001), thus indicating improved diagnostic accuracy. The decision curve analysis of the nomogram indicated that SWE combined with SRUS model had a higher net benefit in predicting breast cancer.

Conclusions

The MVD of the breast mass shows a significant positive correlation with Emax. By integrating SRUS with SWE, this study proposes a novel diagnostic approach designed to improve specificity and accuracy in breast cancer detection, surpassing the limitations of current ultrasound-based methods. This approach shows promise for early breast cancer detection, with the potential to reduce the need for unnecessary biopsies and improve patient outcomes.

Evaluation of normal and abnormal fetal renal microvascular flow characteristics of three-dimensional MV-flow imaging

OBJECTIVE

To evaluate the applicability of three-dimensional MV-Flow imaging for prenatal renal diagnosis.

METHOD

This prospective study included normal and abnormal kidneys ranging from 20 to 40 weeks gestation between April and July 2023. All participants underwent conventional ultrasound and three-dimensional MV-Flow examinations. The renal volume and microvascular indexes were obtained by the three-dimensional MV-Flow.

RESULTS

A total of 207 normal kidneys from 154 fetuses and 67 abnormal kidneys from 53 fetuses, with conditions such as renal cystic diseases, hyperechoic kidney, large kidney, and small kidney were included. Normal renal volume, vascularization index, and vascularization-flow index increased slightly with gestational age (p < 0.001). No correlation was found between gestational age and flow index (p = 0.604). The microvascular indexes decreased in the fetal renal cystic disease group while renal volume increased. Higher vascularization index and vascularization-flow index were observed in the hyperechoic kidney group. The microvascular indexes of the large and small kidney groups were within the reference range for normal kidneys. Only the autosomal dominant polycystic kidney disease exhibited an absence of distinct subcapsular microvascular flow in the MV-Flow image, referred to as the "thick shell sign".

CONCLUSION

Fetal renal volume, vascularization index, and vascularization-flow index increase with gestational age. Quantitative evaluation using 3D MV-Flow imaging reveals varying renal volume and microvascular perfusion characteristics among different fetal renal abnormalities.

Bioactive Nanotherapeutic Ultrasound Contrast Agent for Concurrent Breast Cancer Ultrasound Imaging and Treatment

Contrast-enhanced ultrasound (CEUS) plays a crucial role in cancer diagnosis. The use of ultrasound contrast agents (UCAs) is inevitable in CEUS. However, current applications of UCAs primarily focus on enhancing imaging quality of ultrasound contrast rather than serving as integrated platforms for both diagnosis and treatment in clinical settings. In this study, a novel UCA, termed NPs-DPPA(C3F8), is innovatively prepared using a combination of nanoprecipitation and ultrasound vibration methods. The DPPA lipid possesses inherent antiangiogenic and antitumor activities, and when combined with C3F8, it functions as a theranostic agent. Notably, the preparation of NPs-DPPA(C3F8) is straightforward, requiring only one hour from raw materials to the final product due to the use of a single material, DPPA. NPs-DPPA(C3F8) exhibits inherent antiangiogenic and biotherapeutic activities, effectively inhibiting triple-negative breast cancer (TNBC) angiogenesis and reducing VEGFA expression both in vitro and in vivo. Clinically, NPs-DPPA(C3F8) enables simultaneous real-time imaging, tumor assessment, and antitumor activity. Additionally, through ultrasound cavitation, NPs-DPPA(C3F8) can overcome the dense vascular walls to increase accumulation at the tumor site and facilitate internalization by tumor cells. The successful preparation of NPs-DPPA(C3F8) offers a novel approach for integrating clinical diagnosis and treatment of TNBC.

Ultrasound-Based Noncontrast Microvascular Imaging for Evaluation of Breast Lesions: Imaging Techniques and Review of Diagnostic Criteria

Vascularity plays a pivotal role in the progression of breast lesions and may be associated with their aggressiveness and likelihood of being malignant. Contrast-enhanced imaging techniques are necessary to evaluate vascularity due to the limited sensitivity of conventional color Doppler techniques, in which motion artifacts are eliminated using wall filters. However, in this process, low-flow signals from small vessels also get removed unintentionally. Advancements in technology have revolutionized the way ultrasound images are generated, resulting in tremendous improvements in Doppler imaging techniques. The new, ultrasound-based noncontrast microvascular imaging techniques overcome the limitations of conventional Doppler, and are highly sensitive for detecting microvessels and low flow. The resultant high Doppler sensitivity leads to detection of vascularity in more breast lesions. It is important for radiologists to understand the imaging principles and the clinical implications of the new techniques, to optimally utilize them and aid correct diagnosis. Angio-PLUS is one such recent advancement, which uses unfocused or plane waves and three-dimensional wall filtering to analyze tissue motion in time, space, and amplitude domains that effectively distinguish between blood flow and tissue. The information is beneficial for assessing the lesion vascularity without using contrast. This article aims to explain the Doppler imaging techniques, their clinical applications, scanning methods, and review the common Doppler-based diagnostic criteria used in the evaluation of breast lesions.

Value of Contrast-Enhanced Ultrasound Combined with Immune-Inflammatory Markers in Predicting Axillary Lymph Node Metastasis of Breast Cancer

RATIONALE AND OBJECTIVES

To evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) combined with immune-inflammatory markers in predicting axillary lymph node metastasis (ALNM) in breast cancer patients.

METHODS

From January 2020 to June 2023, the clinicopathological data and ultrasound features of 401 breast cancer patients who underwent biopsy or surgery were recorded. Patients were randomly divided into a training set (321 patients) and a validation set (80 patients). The risk factors for ALNM were determined using univariate, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis, and prediction models were constructed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess their diagnostic performance.

RESULTS

Logistic regression analysis demonstrated that systemic immunoinflammatory index (SII), CA125, Ki67, pathological type, lesion size, enhancement pattern and Breast Imaging Reporting and Data System (BI-RADS) category were significant risk factors for ALNM. Three different models were constructed, and the combined model yielded an AUC of 0.903, which was superior to the clinical model (AUC=0.790) and ultrasound model (AUC=0.781). A nomogram was constructed based on the combined model, calibration curves and DCA demonstrated its satisfactory performance in predicting ALNM.

CONCLUSION

The nomogram combining ultrasound features and immune-inflammatory markers could serve as a valuable instrument for predicting ALNM in breast cancer patients.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

Prediction of disease-free survival using strain elastography and diffuse optical tomography in patients with T1 breast cancer: a 10-year follow-up study

BACKGROUND

Early-stage breast cancer (BC) presents a certain risk of recurrence, leading to variable prognoses and complicating individualized management. Yet, preoperative noninvasive tools for accurate prediction of disease-free survival (DFS) are lacking. This study assessed the potential of strain elastography (SE) and diffuse optical tomography (DOT) for non-invasive preoperative prediction of recurrence in T1 BC and developed a prediction model for estimating the probability of DFS.

METHODS

A total of 565 eligible patients with T1 invasive BC were enrolled prospectively and followed to investigate the recurrence. The associations between imaging features and DFS were evaluated and a best-prediction model for DFS was developed and validated.

RESULTS

During the median follow-up period of 10.8 years, 77 patients (13.6%) developed recurrences. The fully adjusted Cox proportional hazards model showed a significant trend between an increasing strain ratio (SR) (P < 0.001 for trend) and the total hemoglobin concentration (TTHC) (P = 0.001 for trend) and DFS. In the subgroup analysis, an intensified association between SR and DFS was observed among women who were progesterone receptor (PR)-positive, lower Ki-67 expression, HER2 negative, and without adjuvant chemotherapy and without Herceptin treatment (all P < 0.05 for interaction). Significant interactions between TTHC status and the lymphovascular invasion, estrogen receptor (ER) status, PR status, HER2 status, and Herceptin treatment were found for DFS(P < 0.05).The imaging-clinical combined model (TTHC + SR + clinicopathological variables) proved to be the best prediction model (AUC = 0.829, 95% CI = 0.786-0.872) and was identified as a potential risk stratification tool to discriminate the risk probability of recurrence.

CONCLUSION

The combined imaging-clinical model we developed outperformed traditional clinical prognostic indicators, providing a non-invasive, reliable tool for preoperative DFS risk stratification and personalized therapeutic strategies in T1 BC. These findings underscore the importance of integrating advanced imaging techniques into clinical practice and offer support for future research to validate and expand on these predictive methodologies.

Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis

PURPOSE

To develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microflow imaging (MFI) in breast cancer diagnosis.

MATERIALS AND METHODS

We retrospectively collected a dataset consisting of 516 breast lesions (364 benign and 152 malignant) in 471 women who underwent B-mode US and MFI. The internal dataset was split into training (n = 410) and test datasets (n = 106) for developing AI algorithms from deep convolutional neural networks from MFI. AI algorithms were trained to provide malignancy risk (0-100%). The developed AI algorithms were further validated with an independent external dataset of 264 lesions (229 benign and 35 malignant). The diagnostic performance of B-mode US, AI algorithms, or their combinations was evaluated by calculating the area under the receiver operating characteristic curve (AUROC).

RESULTS

The AUROC of the developed three AI algorithms (0.955-0.966) was higher than that of B-mode US (0.842, P < 0.0001). The AUROC of the AI algorithms on the external validation dataset (0.892-0.920) was similar to that of the test dataset. Among the AI algorithms, no significant difference was found in all performance metrics combined with or without B-mode US. Combined B-mode US and AI algorithms had a higher AUROC (0.963-0.972) than that of B-mode US (P < 0.0001). Combining B-mode US and AI algorithms significantly decreased the false-positive rate of BI-RADS category 4A lesions from 87% to 13% (P < 0.0001).

CONCLUSION

AI-based MFI diagnosed breast cancers with better performance than B-mode US, eliminating 74% of false-positive diagnoses in BI-RADS category 4A lesions.

Radiomics analysis combining gray-scale ultrasound and mammography for differentiating breast adenosis from invasive ductal carcinoma

Objectives

To explore the utility of gray-scale ultrasound (GSUS) and mammography (MG) for radiomic analysis in distinguishing between breast adenosis and invasive ductal carcinoma (IDC).

Methods

Data from 147 female patients with pathologically confirmed breast lesions (breast adenosis: 61 patients; IDC: 86 patients) between January 2018 and December 2022 were retrospectively collected. A training cohort of 113 patients (breast adenosis: 50 patients; IDC: 63 patients) diagnosed from January 2018 to December 2021 and a time-independent test cohort of 34 patients (breast adenosis: 11 patients; IDC: 23 patients) diagnosed from January 2022 to December 2022 were included. Radiomic features of lesions were extracted from MG and GSUS images. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the most discriminant features, followed by logistic regression (LR) to construct clinical and radiomic models, as well as a combined model merging radiomic and clinical features. Model performance was assessed using receiver operating characteristic (ROC) analysis.

Results

In the training cohort, the area under the curve (AUC) for radiomic models based on MG features, GSUS features, and their combination were 0.974, 0.936, and 0.991, respectively. In the test cohort, the AUCs were 0.885, 0.876, and 0.949, respectively. The combined model, incorporating clinical and all radiomic features, and the MG plus GSUS radiomics model were found to exhibit significantly higher AUCs than the clinical model in both the training cohort and test cohort (p<0.05). No significant differences were observed between the combined model and the MG plus GSUS radiomics model in the training cohort and test cohort (p>0.05).

Conclusion

The effectiveness of radiomic features derived from GSUS and MG in distinguishing between breast adenosis and IDC is demonstrated. Superior discriminatory efficacy is shown by the combined model, integrating both modalities.

Research on ultrasound-based radiomics: a bibliometric analysis

Background

A large number of studies related to ultrasound-based radiomics have been published in recent years; however, a systematic bibliometric analysis of this topic has not yet been conducted. In this study, we attempted to identify the hotspots and frontiers in ultrasound-based radiomics through bibliometrics and to systematically characterize the overall framework and characteristics of studies through mapping and visualization.

Methods

A literature search was carried out in Web of Science Core Collection (WoSCC) database from January 2016 to December 2023 according to a predetermined search formula. Bibliometric analysis and visualization of the results were performed using CiteSpace, VOSviewer, R, and other platforms.

Results

Ultimately, 466 eligible papers were included in the study. Publication trend analysis showed that the annual publication trend of journals in ultrasound-based radiomics could be divided into three phases: there were no more than five documents published in this field in any year before 2018, a small yearly increase in the number of annual publications occurred between 2018 and 2022, and a high, stable number of publications appeared after 2022. In the analysis of publication sources, China was found to be the main contributor, with a much higher number of publications than other countries, and was followed by the United States and Italy. Frontiers in Oncology was the journal with the highest number of papers in this field, publishing 60 articles. Among the academic institutions, Fudan University, Sun Yat-sen University, and the Chinese Academy of Sciences ranked as the top three in terms of the number of documents. In the analysis of authors and cocited authors, the author with the most publications was Yuanyuan Wang, who has published 19 articles in 8 years, while Philippe Lambin was the most cited author, with 233 citations. Visualization of the results from the cocitation analysis of the literature revealed a strong centrality of the subject terms papillary thyroid cancer, biological behavior, potential biomarkers, and comparative assessment, which may be the main focal points of research in this subject. Based on the findings of the keyword analysis and cluster analysis, the keywords can be categorized into two major groups: (I) technological innovations that enable the construction of radiomics models such as machine learning and deep learning and (II) applications of predictive models to support clinical decision-making in certain diseases, such as papillary thyroid cancer, hepatocellular carcinoma (HCC), and breast cancer.

Conclusions

Ultrasound-based radiomics has received widespread attention in the medical field and has been gradually been applied in clinical research. Radiomics, a relatively late development in medical technology, has made substantial contributions to the diagnosis, prediction, and prognostic evaluation of diseases. Additionally, the coupling of artificial intelligence techniques with ultrasound imaging has yielded a number of promising tools that facilitate clinical decision-making and enable the practice of precision medicine. Finally, the development of ultrasound-based radiomics requires multidisciplinary cooperation and joint efforts from the field biomedicine, information technology, statistics, and clinical medicine.

Value of micro-flow imaging and high-definition micro-flow imaging in differentiating malignant and benign breast lesions

AIM

To evaluate the value of non-contrast micro-flow imaging (MFI) and high-definition micro-flow imaging (HD-MFI) in differentiating malignant and benign breast lesions.

MATERIALS AND METHODS

One hundred and thirty-three patients with 138 breast lesions (80 benign and 58 malignant lesions) were examined using colour Doppler flow imaging (CDFI), MFI, and HD-MFI before biopsy, with blood flow signals graded into four types (grade 0, 1, 2, and 3) and penetrating vessels evaluated. The micro-vascular patterns of MFI and HD-MFI were evaluated and classified into five patterns: avascular, line-like, tree-like, root hair-like, and crab claw-like pattern. The diagnostic efficiency of micro-vascular patterns was analysed. Moreover, ultrasound Breast Imaging Reporting and Data System (BI-RADS) 4A lesions were also re-assessed according to the micro-vascular patterns of MFI or HD-MFI.

RESULTS

The capability of detecting blood flow and penetrating vessels from high to low was HD-MFI, MFI, and CDFI, respectively (p<0.05). Rich blood flow signals, penetrating vessels, and root hair-like or crab claw-like pattern were more likely in malignant breast lesions, while few blood flow signals, tree-like pattern were mostly in benign lesions (p<0.05). The diagnostic efficiency of HD-MFI and MFI were higher than CDFI (p>0.05). MFI could reduce unnecessary biopsy of 52 US BI-RADS 4A lesions but with two malignancies missed, while 56 ultrasound BI-RADS 4A lesions could be downgraded by HD-MFI with none malignancies missed.

CONCLUSIONS

MFI and HD-MFI can detect more blood flow in breast lesions than CDFI, and could help distinguish benign and malignant breast lesions. HD-MFI could reduce the unnecessary biopsy of US BI-RADS 4A lesions without missed malignancy.

Neoadjuvant chemotherapy for breast cancer: an evaluation of its efficacy and research progress

Neoadjuvant chemotherapy (NAC) for breast cancer is widely used in the clinical setting to improve the chance of surgery, breast conservation and quality of life for patients with advanced breast cancer. A more accurate efficacy evaluation system is important for the decision of surgery timing and chemotherapy regimen implementation. However, current methods, encompassing imaging techniques such as ultrasound and MRI, along with non-imaging approaches like pathological evaluations, often fall short in accurately depicting the therapeutic effects of NAC. Imaging techniques are subjective and only reflect macroscopic morphological changes, while pathological evaluation is the gold standard for efficacy assessment but has the disadvantage of delayed results. In an effort to identify assessment methods that align more closely with real-world clinical demands, this paper provides an in-depth exploration of the principles and clinical applications of various assessment approaches in the neoadjuvant chemotherapy process.

New Approaches and Recommendations for Risk-Adapted Breast Cancer Screening

Population-based breast cancer screening using mammography as the gold standard imaging modality has been in clinical practice for over 40 years. However, the limitations of mammography in terms of sensitivity and high false-positive rates, particularly in high-risk women, challenge the indiscriminate nature of population-based screening. Additionally, in light of expanding research on new breast cancer risk factors, there is a growing consensus that breast cancer screening should move toward a risk-adapted approach. Recent advancements in breast imaging technology, including contrast material-enhanced mammography (CEM), ultrasound (US) (automated-breast US, Doppler, elastography US), and especially magnetic resonance imaging (MRI) (abbreviated, ultrafast, and contrast-agent free), may provide new opportunities for risk-adapted personalized screening strategies. Moreover, the integration of artificial intelligence and radiomics techniques has the potential to enhance the performance of risk-adapted screening. This review article summarizes the current evidence and challenges in breast cancer screening and highlights potential future perspectives for various imaging techniques in a risk-adapted breast cancer screening approach. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 5.

Role of qualitative contrast-enhanced ultrasound in the diagnosis of malignant breast lesions

Background

Carcinoma breast is the commonest cancer among women. Various authors have studied breast cancer with Contrast-Enhanced Ultrasound (CEUS) with promising results. Despite promising results, the additional cost of post-processing software limits its availability. In this study, we evaluated the utility of CEUS in differentiating malignant from benign breast lesions on regular ultrasound equipment without the use of dedicated software.

Methods

We performed CEUS in 121 women with 121 breast lesions. CEUS was done by creating a custom preset on existing ultrasound equipment with the help of an application specialist authorized by the vendor. Lesions were evaluated qualitatively without the use of any commercial software. The pattern of enhancement i.e. homogenous, heterogeneous, peripheral, or no enhancement, and the number of penetrating vessels i.e., few or multiple were recorded. Results were compared with histopathological diagnosis.

Results

There were a total of 121 breast lesions. The study showed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 86.67 %, 54.10 %, 65 %, and 80.49% respectively for differentiating benign vs malignant lesions on the basis of the pattern of contrast enhancement. Using penetrating vessels for differentiating malignant lesions from benign lesions, the sensitivity, specificity, PPV, and NPV were found to be 64%, 67.86%, 78.05%, and 51.35% respectively.

Conclusion

CEUS is useful in differentiating malignant from benign breast lesions. It can be easily performed by creating a custom preset on standard ultrasound equipment without the use of expensive software.

Deep learning and ultrasound feature fusion model predicts the malignancy of complex cystic and solid breast nodules with color Doppler images

This study aimed to evaluate the performance of traditional-deep learning combination model based on Doppler ultrasound for diagnosing malignant complex cystic and solid breast nodules. A conventional statistical prediction model based on the ultrasound features and basic clinical information was established. A deep learning prediction model was used to train the training group images and derive the deep learning prediction model. The two models were validated, and their accuracy rates were compared using the data and images of the test group, respectively. A logistic regression method was used to combine the two models to derive a combination diagnostic model and validate it in the test group. The diagnostic performance of each model was represented by the receiver operating characteristic curve and the area under the curve. In the test cohort, the diagnostic efficacy of the deep learning model was better than traditional statistical model, and the combined diagnostic model was better and outperformed the other two models (combination model vs traditional statistical model: AUC: 0.95 > 0.70, P = 0.001; combination model vs deep learning model: AUC: 0.95 > 0.87, P = 0.04). A combination model based on deep learning and ultrasound features has good diagnostic value.

Diagnostic Performance of Superb Microvascular Imaging in Differentiating Benign and Malignant Axillary Lymph Nodes

ABSTRACT

The aim was to evaluate the effectiveness of superb microvascular imaging (SMI) in axillary lymph nodes (LNs).Benign and malignant LNs diagnosed via histopathological examination constituted the study subgroups. In addition to grayscale findings for morphological evaluation, vascular patterns and appearance of internal vessels were analyzed by both power Doppler ultrasound (PDUS) and SMI. The number of vascular branches was counted, and a vascularity index (VI) was calculated by SMI.Fifty-two LNs with suspicious findings in terms of metastasis (33 malignant and 19 benign) were evaluated. Diagnostic accuracy according to vascular patterns was 82% for PDUS and 92% for SMI. In the presence of asymmetric cortical thickening, there was a significant difference between benign and malignant LNs in the number of vascular branches of both thin and thick cortical sides ( P < 0.01). Mean VI was significantly higher in the malignant group ( P < 0.05). In differentiating malignancy, when a cutoff VI value was set to 9%, sensitivity was 69.7%, and specificity was 63.2%.Evaluating the vascularity of axillary LNs by SMI is a useful tool in determining the potential of axillary metastasis, especially in the absence of typical sonographic findings. Superb microvascular imaging can beneficially be used to select the most suspicious LN and suspicious area of the LN to sample.

Utility of ultrasound Angio-PLUS imaging for detecting blood flow in breast masses and comparison with color Doppler for differentiating benign from malignant masses

BACKGROUND

Tumor neo-angiogenesis plays an important role in the development and growth of breast cancers, but its detection by imaging is challenging. A novel microvascular imaging (MVI) technique, Angio-PLUS, promises to overcome the limitations of color Doppler (CD) in detecting low-velocity flow and small diameter vessels.

PURPOSE

To determine the utility of the Angio-PLUS technique for detecting blood flow in breast masses and compare it with CD for differentiating benign from malignant masses.

MATERIAL AND METHODS

A total of 79 consecutive women with breast masses were prospectively evaluated using CD and Angio-PLUS techniques, and biopsied as per BI-RADS recommendations. Vascular imaging scores were assigned using three factors (number, morphology, and distribution) and vascular patterns were divided into five groups: internal-dot-spot, external-dot-spot, marginal, radial, and mesh patterns. The independent samples t-test, Mann-Whitney U test, Wilcoxon signed rank test, or Fisher's exact test were used to compare the two groups as appropriate. Area under the receiver operating characteristic (ROC) curve (AUC) methods were used to assess diagnostic accuracy.

RESULTS

Vascular scores were significantly higher on Angio-PLUS than CD (median=11, [IQR=9-13] vs. 5 [IQR=3-9], P < 0.001). Malignant masses had higher vascular scores than benign masses on Angio-PLUS (P < 0.001). AUC was 80% (95% CI=70.3-89.7; P < 0.001) for Angio-PLUS and 51.9% for CD. Using Angio-PLUS at a cutoff value of ≥9.5, sensitivity was 80% and specificity was 66.7%. Vascular pattern descriptors on AP showed good correlation with histopathological results (PPV mesh 95.5%, radial 96.9%, and NPV of marginal orientation 90.5%).

CONCLUSION

Angio-PLUS was more sensitive in detecting vascularity and superior in differentiating benign from malignant masses compared to CD. Vascular pattern descriptors on Angio-PLUS were useful.

A Novel Nomogram Based on Imaging Biomarkers of Shear Wave Elastography, Angio Planewave Ultrasensitive Imaging, and Conventional Ultrasound for Preoperative Prediction of Malignancy in Patients with Breast Lesions

Several studies have demonstrated the difficulties in distinguishing malignant lesions of the breast from benign lesions owing to overlapping morphological features on ultrasound. Consequently, we aimed to develop a nomogram based on shear wave elastography (SWE), Angio Planewave Ultrasensitive imaging (Angio PLUS (AP)), and conventional ultrasound imaging biomarkers to predict malignancy in patients with breast lesions. This prospective study included 117 female patients with suspicious lesions of the breast. Features of lesions were extracted from SWE, AP, and conventional ultrasound images. The least absolute shrinkage and selection operator (Lasso) algorithms were used to select breast cancer-related imaging biomarkers, and a nomogram was developed based on six of the 16 imaging biomarkers. This model exhibited good discrimination (area under the receiver operating characteristic curve (AUC): 0.969; 95% confidence interval (CI): 0.928, 0.989) between malignant and benign breast lesions. Moreover, the nomogram also showed demonstrated good calibration and clinical usefulness. In conclusion, our nomogram can be a potentially useful tool for individually-tailored diagnosis of breast tumors in clinical practice.

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.

Ultrasound high-definition microvasculature imaging with novel quantitative biomarkers improves breast cancer detection accuracy

OBJECTIVES

To overcome the limitations of power Doppler in imaging angiogenesis, we sought to develop and investigate new quantitative biomarkers of a contrast-free ultrasound microvasculature imaging technique for differentiation of benign from malignant pathologies of breast lesion.

METHODS

In this prospective study, a new high-definition microvasculature imaging (HDMI) was tested on 521 patients with 527 ultrasound-identified suspicious breast masses indicated for biopsy. Four new morphological features of tumor microvessels, microvessel fractal dimension (mvFD), Murray's deviation (MD), bifurcation angle (BA), and spatial vascularity pattern (SVP) as well as initial biomarkers were extracted and analyzed, and the results correlated with pathology. Multivariable logistic regression analysis was used to study the performance of different prediction models, initial biomarkers, new biomarkers, and combined new and initial biomarkers in differentiating benign from malignant lesions.

RESULTS

The new HDMI biomarkers, mvFD, BA, MD, and SVP, were statistically significantly different in malignant and benign lesions, regardless of tumor size. Sensitivity and specificity of the new biomarkers in lesions > 20 mm were 95.6% and 100%, respectively. Combining the new and initial biomarkers together showed an AUC, sensitivity, and specificity of 97% (95% CI: 95-98%), 93.8%, and 89.2%, respectively, for all lesions regardless of mass size. The classification was further improved by adding the Breast Imaging Reporting and Data System (BI-RADS) score to the prediction model, showing an AUC, sensitivity, and specificity of 97% (95% CI: 95-98%), 93.8%, and 89.2%, respectively.

CONCLUSION

The addition of new quantitative HDMI biomarkers significantly improved the accuracy in breast lesion characterization when used as a complementary imaging tool to the conventional ultrasound.

KEY POINTS

• Novel quantitative biomarkers extracted from tumor microvessel images increase the sensitivity and specificity in discriminating malignant from benign breast masses. • New HDMI biomarkers Murray's deviation, bifurcation angles, microvessel fractal dimension, and spatial vascularity pattern outperformed the initial biomarkers. • The addition of BI-RADS scores based on US descriptors to the multivariable analysis using all biomarkers remarkably increased the sensitivity, specificity, and AUC in all size groups.

3D Harmonic and Subharmonic Imaging for Characterizing Breast Lesions: A Multi-Center Clinical Trial

OBJECTIVE

Breast cancer is the most frequent type of cancer among women. This multi-center study assessed the ability of 3D contrast-enhanced ultrasound to characterize suspicious breast lesions using clinical assessments and quantitative parameters.

METHODS

Women with suspicious breast lesions scheduled for biopsy were enrolled in this prospective, study. Following 2D grayscale ultrasound and power Doppler imaging (PDI), a contrast agent (Definity; Lantheus) was administrated. Contrast-enhanced 3D harmonic imaging (HI; transmitting/receiving at 5.0/10.0 MHz), as well as 3D subharmonic imaging (SHI; transmitting/receiving at 5.8/2.9 MHz), were performed using a modified Logiq 9 scanner (GE Healthcare). Five radiologists independently scored the imaging modes (including standard-of-care imaging) using a 7-point BIRADS scale as well as lesion vascularity and diagnostic confidence. Parametric volumes were constructed from time-intensity curves for vascular heterogeneity, perfusion, and area under the curve. Diagnostic accuracy was determined relative to pathology using receiver operating characteristic (ROC) and reverse, step-wise logistical regression analyses. The κ-statistic was calculated for inter-reader agreement.

RESULTS

Data were successfully acquired in 219 cases and biopsies indicated 164 (75%) benign and 55 (25%) malignant lesions. SHI depicted more anastomoses and vascularity than HI (P < .021), but there were no differences by pathology (P > .27). Ultrasound achieved accuracies of 82 to 85%, which was significantly better than standard-of-care imaging (72%; P < .03). SHI increased diagnostic confidence by 3 to 6% (P < .05), but inter-reader agreements were medium to low (κ < 0.52). The best regression model achieved 97% accuracy by combining clinical reads and parametric SHI.

CONCLUSIONS

Combining quantitative 3D SHI parameters and clinical assessments improves the characterization of suspicious breast lesions.

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.

Shear wave elastography and pulsed doppler for breast lesions: Similar diagnostic performance and positively correlated stiffness and blood flow resistance

PURPOSE

To compare the diagnostic performance of shear wave elastography (SWE) and pulsed Doppler ultrasound in breast lesions, and to explore whether the quantitative SWE parameters correlated with pulsed Doppler ultrasound parameters.

MATERIALS AND METHODS

Seventy-nine patients with 79 breast lesions who had undergone conventional ultrasound, pulsed Doppler ultrasound and SWE examination were included. All of them underwent core needle biopsy or surgery within one week. Parameters including Emax (the maximum elastic modulus), Emean (mean elastic modulus), Emin (minimum elastic modulus), Esd (elastic modulus standard deviation), and RI (resistive index), PI (pulsatility index), PSV (peak systolic velocity) and EDV (end diastolic velocity) were obtained for statistical analysis.

RESULTS

Almost all SWE parameters were significantly different between benign and malignant breast lesions (P＜0.05). There was no significant difference between Esd and PI (P＞0.05), which had the best AUC among SWE and vascular parameters respectively (0.877 vs. 0.871). Emax showed a moderate correlation with PI (P = 0.000, r = 0.552) and RI (P = 0.000, r = 0.544), and Esd moderately correlated with PI (P = 0.000, r = 0.567) and RI (P = 0.000, r = 0.546). For the benign group, no parameters showed any significant correlation (P＞0.05), while for the malignant group, Emax and Esd also significantly correlated with PI or RI.

CONCLUSIONS

SWE and pulsed Doppler ultrasound had similar diagnostic efficacy for breast lesions. SWE and pulsed Doppler parameters were significantly correlated in breast lesions, especially in malignant ones, indicating the potential association between elastographic and vascular characteristics of breast tumors.