Ultrasensitive Ultrasound Microvessel Imaging for Characterizing Benign and Malignant Breast Tumors

Maximum intensity projection based on high frame rate contrast-enhanced ultrasound for the differentiation of breast tumors

Objective

We explored the role of maximum intensity projection (MIP) based on high frame rate contrast-enhanced ultrasound (H-CEUS) for the differentiation of breast tumors.

Methods

MIP imaging was performed in patients with breast tumors who underwent H-CEUS examinations. The microvasculature morphology of breast tumors was assessed. The receiver operating characteristic curve was plotted to evaluate the diagnostic performance of MIP.

Results

Forty-three breast tumors were finally analyzed, consisting of 19 benign and 24 malignant tumors. For the ≤30-s and >30-s phases, dot-, line-, or branch-like patterns were significantly more common in benign tumors. A tree-like pattern was only present in the benign tumors. A crab claw-like pattern was significantly more common in the malignant tumors. Among the tumors with crab claw-like patterns, three cases of malignant tumors had multiple parallel small spiculated vessels. There were significant differences in the microvasculature morphology for the ≤30-s and >30-s phases between the benign and malignant tumors (all p < 0.001). The area under the curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the ≤30-s phase were all higher than those of the >30-s phase for the classification of breast tumors.

Conclusion

MIP based on H-CEUS can be used for the differentiation of breast tumors, and the ≤30-s phase had a better diagnostic value. Multiple parallel small spiculated vessels were a new finding, which could provide new insight for the subsequent study of breast tumors.

From bedside to portable and wearable: development of a conformable ultrasound patch for deep breast tissue imaging

A breakthrough study from Du et al. has developed a wearable, ultrasound imaging patch for standardized and reproducible breast tissue imaging. The technology utilizes a honeycomb patch design to facilitate guided movement of the ultrasound array, enabling comprehensive, multiangle breast imaging. The system was validated in vitro and in vivo with a single human subject and has the potential for early-stage breast cancer detection. This study addressed the current limitations of wearable ultrasound technologies, including imaging over large, curvilinear organs and integration of superior piezoelectric materials for high-performance ultrasound arrays. The transition of ultrasound from the bedside to portable and wearable devices will pave the way for integration with big data collection, such as artificial intelligence (AI)-based diagnosis and personalized ultrasonographic profile generation, for rapid and objective measurements. This advancement is especially important in the context of breast cancer, where early diagnosis and assessment of medical therapy responses are paramount to patient care.

Superb Microvascular Imaging for the Differentiation of Benign and Malignant Breast Lesions: A System Review and Meta-Analysis

OBJECTIVE

To evaluate the diagnostic performance of SMI in the diagnosis of benign and malignant breast lesions.

METHODS

A systematic search of PubMed, EMBASE, Cochrane, OVID, SCI, and SCOPUS was performed to find relevant studies which applied SMI to differentiate benign and malignant breast lesions. All the studies were published before October 10, 2022. Only studies published in English were collected. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the quality of the included studies. Summary receiver operating characteristic (SROC) modeling was also performed to the diagnostic performance of SMI in the diagnosis of benign and malignant breast lesions. Subgroup analyses and meta-regression were performed to find out the heterogeneity.

RESULTS

Twenty studies which include a total of 2873 lesions (1748 benign and 1125 malignant) in 2740 patients were evaluated in this meta-analysis. The summary sensitivity and specificity were 0.82 (95% confidence interval [CI]: 0.76-0.86), 0.70 (95% CI: 0.64-0.76) for SMI vascular degree, 0.77 (95% CI: 0.67-0.84), 0.79 (95% CI: 0.75-0.83) for SMI vascular distribution, 0.78 (95% CI: 0.70-0.84), 0.75 (95% CI: 0.69-0.80) for SMI vascular morphology, 0.81 (95% CI: 0.72-0.87), 0.80 (95% CI: 0.75-0.85) SMI penetration vessel. For SMI overall vascular features, the summary sensitivity and summary specificity were 0.74 (95% CI: 0.61-0.84) and 0.80 (95% CI: 0.76-0.84). The result of subgroup analysis and meta-analysis showed malignant rate and country might be the cause of heterogeneity of diagnostic accuracy of vascular grade and morphology.

CONCLUSION

SMI vascular features have high sensitivity and specificity in the differentiation of benign and malignant lesions. Future international multicenter studies in various regions with large sample size are required to confirm these findings.

Ultrasensitive US Microvessel Imaging of Hepatic Microcirculation in the Cirrhotic Rat Liver

Background Liver microcirculation dysfunction plays a vital role in the occurrence and development of liver diseases, and thus, there is a clinical need for in vivo, noninvasive, and quantitative evaluation of liver microcirculation. Purpose To evaluate the feasibility of ultrasensitive US microvessel imaging (UMI) in the visualization and quantification of hepatic microvessels in healthy and cirrhotic rats. Materials and Methods In vivo studies were performed to image hepatic microvasculature by means of laparotomy in Sprague-Dawley rats (five cirrhotic and five control rats). In vivo conventional power Doppler US and ex vivo micro-CT were performed for comparison. UMI-based quantifications of perfusion, tortuosity, and integrity of microvessels were compared between the control and cirrhotic groups by using the Wilcoxon test. Spearman correlations between quantification parameters and pathologic fibrosis, perfusion function, and hepatic hypoxia were evaluated. Results UMI helped detect minute vessels below the liver capsule, as compared with conventional power Doppler US and micro-CT. With use of UMI, lower perfusion indicated by vessel density (median, 22% [IQR, 20%-28%] vs 41% [IQR, 37%-46%]; P = .008) and fractional moving blood volume (FMBV) (median, 6.4% [IQR, 4.8%-8.6%] vs 13% [IQR, 12%-14%]; P = .008) and higher tortuosity indicated by the sum of angles metric (SOAM) (median, 3.0 [IQR, 2.9-3.0] vs 2.7 [IQR, 2.6-2.9]; P = .008) were demonstrated in the cirrhotic rat group compared with the control group. Vessel density (r = 0.85, P = .003), FMBV (r = 0.86, P = .002), and median SOAM (r = -0.83, P = .003) showed strong correlations with pathologically derived vessel density labeled with dextran. Vessel density (r = -0.81, P = .005) and median SOAM (r = 0.87, P = .001) also showed strong correlations with hepatic tissue hypoxia. Conclusion Contrast-free ultrasensitive US microvessel imaging provided noninvasive in vivo imaging and quantification of hepatic microvessels in cirrhotic rat liver. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Fetzer in this issue.

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.

Adaptive and Robust Vessel Quantification in Contrast-Free Ultrafast Ultrasound Microvessel Imaging

The morphological features of vasculature in diseased tissue differ significantly from those in normal tissue. Therefore, vasculature quantification is crucial for disease diagnosis and staging. Ultrasound microvessel imaging (UMI) with ultrafast ultrasound acquisitions has been determined to have potential in clinical applications given its superior sensitivity in blood flow detection. However, the presence of spatial-dependent noise caused by a low imaging signal-to-noise ratio and incoherent clutter artifacts caused by moving hyperechoic scatterers degrades the performance of UMI and the reliability of vascular quantification. To tackle these issues, we proposed an improved UMI technique along with an adaptive vessel segmentation workflow for robust vessel identification and vascular feature quantification. A previously proposed sub-aperture cross-correlation technique and a normalized cross-correlation technique were applied to equalize the spatially dependent noise level and suppress the incoherent clutter artifact. A square operator and non-local means filter were then used to better separate the blood flow signal from residual background noise. On the de-noised ultrasound microvessel image, an automatic and adaptive vessel segmentation method was developed based on the different spatial patterns of blood flow signal and background noise. The proposed workflow was applied to a CIRS phantom, to a Doppler flow phantom and to an inflammatory bowel, kidney and liver, to validate its feasibility. Results revealed that automatic adaptive, and robust vessel identification performance can be achieved using the proposed method without the subjectivity caused by radiologists/operators.

Diagnostic accuracy of S-Detect to breast cancer on ultrasonography: A meta-analysis (PRISMA)

BACKGROUND

Computer-aided diagnosis (CAD) systems have shown great potential as an effective auxiliary diagnostic tool in breast imaging. Previous studies have shown that S-Detect technology has a high accuracy in the differential diagnosis of breast masses. However, the application of S-Detect in clinical practice remains controversial, and the results vary among different clinical trials. This meta-analysis aimed to determine the diagnostic accuracy of S-Detect for distinguishing between benign and malignant breast masses.

METHODS

We searched PubMed, Cochrane Library, and CBM databases from inception to April 1, 2021. Meta-analysis was conducted using STATA version 14.0 and Meta-Disc version 1.4 softwares. We calculated the summary statistics for sensitivity (Sen), specificity (Spe), positive, and negative likelihood ratio (LR+/LR-), diagnostic odds ratio(DOR), and summary receiver operating characteristic (SROC) curves. Cochran Q-statistic and I2 test were used to evaluate the potential heterogeneity between studies. Sensitivity analysis was performed to evaluate the influence of single studies on the overall estimate. We also performed meta-regression analyses to investigate potential sources of heterogeneity.

RESULTS

Eleven studies that met all the inclusion criteria were included in the meta-analysis. A total of 951 malignant and 1866 benign breast masses were assessed. All breast masses were histologically confirmed using S-Detect. The pooled Sen was 0.82 (95% confidence interval(CI) = 0.74-0.88); the pooled Spe was 0.83 (95%CI = 0.78-0.88). The pooled LR + was 4.91 (95%CI = 3.75-6.41); the pooled negative LR - was 0.21 (95%CI = 0.15-0.31). The pooled DOR of S-Detect in the diagnosis of breast nodules was 23.12 (95% CI = 14.53-36.77). The area under the SROC curve was 0.90 (SE = 0.0166). No evidence of publication bias was found (t = 0.54, P = .61).

CONCLUSIONS

Our meta-analysis indicates that S-Detect may have high diagnostic accuracy in distinguishing benign and malignant breast masses.

Improved Ultrafast Power Doppler Imaging by Using Spatiotemporal Non-Local Means Filtering

The change of microvasculature is associated with the occurrence and development of many diseases. Ultrafast power Doppler imaging (uPDI) is an emerging technology for the visualization of microvessels due to the development of ultrafast plane wave (PW) imaging and advanced clutter filters. However, the low signal-to-noise ratio (SNR) caused by unfocused transmit of PW imaging deteriorates the subsequent imaging of microvasculature. Nonlocal means (NLM) filtering has been demonstrated to be effective in the denoising of both natural and medical images, including ultrasound power Doppler images. However, the feasibility and performance of applying an NLM filter on the ultrasound radio frequency (RF) data have not been investigated so far. In this study, we propose to apply an NLM filter on the spatiotemporal domain of clutter filtered blood flow RF data (St-NLM) to improve the quality of uPDI. Experiments were conducted to compare the proposed method with three different methods (under various similarity window sizes), including conventional uPDI without NLM filtering (Non-NLM), NLM filtering on the obtained power Doppler images (PD-NLM), and NLM filtering on the spatial domain of clutter filtered blood flow RF data (S-NLM). Phantom experiments, in vivo contrast-enhanced human spinal cord tumor experiments, and in vivo contrast-free human liver experiments were performed to demonstrate the superiority of the proposed St-NLM method over the other three methods. Qualitative and quantitative results show that the proposed St-NLM method can effectively suppress the background noise, improve the contrast between vessels and background, and preserve the details of small vessels at the same time. In the human liver study, the proposed St-NLM method achieves 31.05-, 24.49-, and 11.15-dB higher contrast-to-noise ratios (CNRs) and 36.86-, 36.86-, and 15.22-dB lower noise powers than Non-NLM, PD-NLM, and S-NLM, respectively. In the human spinal cord tumor, the full-width at half-maximums (FWHMs) of vessel cross Section are 76, 201, and [Formula: see text] for St-NLM, Non-NLM, and S-NLM, respectively. The proposed St-NLM method can enhance the microvascular visualization in uPDI and has the potential for the diagnosis of many microvessel-change-related diseases.

Compressed Sensing-Based Super-Resolution Ultrasound Imaging for Faster Acquisition and High Quality Images

GOAL

Typical SRUS images are reconstructed by localizing ultrasound microbubbles (MBs) injected in a vessel using normalized 2-dimensional cross-correlation (2DCC) between MBs signals and the point spread function of the system. However, current techniques require isolated MBs in a confined area due to inaccurate localization of densely populated MBs. To overcome this limitation, we developed the ℓ1-homotopy based compressed sensing (L1H-CS) based SRUS imaging technique which localizes densely populated MBs to visualize microvasculature in vivo.

METHODS

To evaluate the performance of L1H-CS, we compared the performance of 2DCC, interior-point method based compressed sensing (CVX-CS), and L1H-CS algorithms. Localization efficiency was compared using axially and laterally aligned point targets (PTs) with known distances and randomly distributed PTs generated by simulation. We developed post-processing techniques including clutter reduction, noise equalization, motion compensation, and spatiotemporal noise filtering for in vivo imaging. We then validated the capabilities of L1H-CS based SRUS imaging technique with high-density MBs in a mouse tumor model, kidney, and zebrafish dorsal trunk, and brain.

RESULTS

Compared to 2DCC and CVX-CS algorithms, L1H-CS achieved faster data acquisition time and considerable improvement in SRUS image quality.

CONCLUSIONS AND SIGNIFICANCE

These results demonstrate that the L1H-CS based SRUS imaging technique has the potential to examine microvasculature with reduced acquisition and reconstruction time to acquire enhanced SRUS image quality, which may be necessary to translate it into clinics.

Suspicious breast lesions incidentally detected on chest computer tomography with histopathological correlation

OBJECTIVE

To evaluate incidental breast lesions on chest computed tomography with histopathological correlation. It is important for general radiologist to characterize a breast lesion as benign, indetermined, or sufficiently suspicious to warrant further work-up.

METHODS

A total of 35.000 chest CT examinations were performed between January 2016 and December 2020. 27 patients (mean age 70 years, age range 48-87 years) with incidental breast lesions were identified in this retrospective study. Two radiologists scored incidental breast lesions independently regarding their morphology, and the results were compared to histopathology which was obtained by an ultrasound-guided core needle biopsy or a surgical excision.

RESULTS

Out of 35.000 chest CT examinations, a total of 31 incidental breast lesions in 27 patients were detected. Among the 31 lesions, 23 were malignant and 8 benign. The malignant lesions included 17 carcinomas and 6 metastases (4 lymphomas and 2 melanomas). The benign lesions contained 2 hematomas, 4 fat necrosis, and 2 fibrosis lumps.

CONCLUSION

Chest computed tomography as a standard imaging technique is helpful for evaluation of suspicious breast lesions. This may ultimately influence patient management and lead to further imaging.

Qualitative Diagnosis of Solid Breast Mass by Blood Flow in Solid Breast Mass Based on Color Doppler Ultrasound

The incidence of breast cancer ranks first among female malignant tumor. With the increase of the sensitivity of color Doppler ultrasound blood flow, the blood flow distribution in and around the tumor can be clearly displayed, and the analysis of hemodynamic parameters is provided, which provides convenience for the study of tumor blood flow characteristics. Studies have shown that tumor cells can secrete a substance called angiogenesis factor, which makes the tumor site form a rich vascular network to promote tumor growth and metastasis. The tumor has many new blood vessels, abnormal structure, thin wall, lack of muscle layer, and is prone to form arteriovenous rash. These characteristics provide a pathological basis for color Doppler flow imaging (CDFI) for the diagnosis of breast cancer. This article discusses the role of two-dimensional sonographic features in the differential diagnosis of benign and malignant breast masses, CDFI was used to study the blood flow distribution and hemodynamic characteristics in benign and malignant breast masses; explore the value of blood flow characteristics and blood flow parameters in the differential diagnosis of breast masses. The experimental results show that the detection rate of blood flow signals and the classification of blood flow signals in the malignant group are higher than those in the benign group, mainly level II and III blood flow, and the irregular branched blood flow is more common, especially when the tumor appears penetrating blood flow supports the diagnosis of malignancy. PSV, RI and PI have a certain differential meaning in the diagnosis of benign and malignant breast masses. PSV, RI and PI of malignant masses are higher than benign masses. For tumors without obvious necrosis, the larger the tumor diameter, the richer the blood flow and the higher the blood flow grade is. The malignant tumors have more blood flow than the benign ones.

Ultrafast Power Doppler Imaging Using Frame-Multiply-and-Sum-Based Nonlinear Compounding

Ultrafast power Doppler imaging based on coherent compounding (UPDI-CC) has become a promising technique for microvascular imaging due to its high sensitivity to slow blood flows. However, since this method utilizes a limited number of plane-wave or diverging-wave transmissions for high-frame-rate imaging, it suffers from degraded image quality because of the low contrast resolution. In this article, an ultrafast power Doppler imaging method based on a nonlinear compounding framework, called frame-multiply-and-sum (UPDI-FMAS), is proposed to improve contrast resolution. In UPDI-FMAS, unlike conventional channel-domain delay-multiply-and-sum (DMAS) beamforming, the signal coherence is estimated based on autocorrelation function over plane-wave angle frames. To avoid phase distortion of blood flow signals during the autocorrelation process, clutter filtering is preferentially applied to individual beamformed plane-wave data set. Therefore, only coherent blood flow signals are emphasized, while incoherent background noise is suppressed. The performance of the UPDI-FMAS was evaluated with simulation, phantom, and in vivo studies. For the simulation and phantom studies with a constant laminar flow, the UPDI-FMAS showed improvements in the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) to those of UPDI-CC, i.e., over 10 and 7 dB for 13 plane waves, respectively, and the performances were improved as the number of plane waves increased. Moreover, the enhancement of the image quality due to the increased SNR and CNR in UPDI-FMAS was more clearly depicted with the in vivo study, in which a human kidney and a tumor-bearing mouse were evaluated. These results indicate that the FMAS compounding can improve the image quality of UPDI for microvascular imaging without loss of temporal resolution.

Diagnostic Value of Contrast-Enhanced Ultrasound for Differential Diagnosis of Malignant and Benign Soft Tissue Masses: A Meta-Analysis

This meta-analysis was aimed at investigating the value of using contrast-enhanced ultrasound (CEUS) in the differential diagnosis of benign and malignant soft tissue masses (STMs). Relevant studies published before March 24, 2020 were identified through a comprehensive search of PubMed, Ovid, Cochrane and Web of Science. According to the inclusion criteria, five studies were selected comprising 746 patients. In the differential diagnosis of benign and malignant STMs, the pooled sensitivity and specificity of CEUS were 76% (95% confidence interval [CI]: 71%-81%; heterogeneity [I²] = 74.5%) and 67% (95% CI: 62%-71%; I² = 36.5%), respectively. The diagnostic odds ratio was 7.37 (95% CI: 3.78%-14.35; I² = 66.6%). The overall area under the curve was 0.77 (standard error: 0.0392). Subgroup analysis revealed that different index tests of CEUS resulted in different diagnostic performance. Importantly, CEUS is an effective method for the differential diagnosis between benign and malignant STMs.