Future of breast elastography

A case of primary breast angiosarcoma diagnosed by multimodal ultrasound imaging with literature review

Primary Breast Angiosarcoma (PBA) is an exceptionally rare form of breast cancer, accounting for less than 0.05% of all breast cancers. It is characterized by a high level of malignancy, invasiveness, and has a prognosis that is typically poor. The lack of distinctive clinical features makes it prone to underdiagnosis and misdiagnosis. This study retrospectively examines a case utilizing multimodal ultrasound imaging techniques (including 2D ultrasound, contrast-enhanced ultrasound, and ultrasound elastography) for diagnosing PBA. Furthermore, the study reviews relevant literature to summarize the ultrasound characteristics of PBA, with the aim of improving understanding of this elusive condition.

New Frontiers in Breast Cancer Imaging: The Rise of AI

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.

Compound Acoustic Radiation Force Impulse Imaging of Bovine Eye by Using Phase-Inverted Ultrasound Transducer

In general, it is difficult to visualize internal ocular structure and detect a lesion such as a cataract or glaucoma using the current ultrasound brightness-mode (B-mode) imaging. This is because the internal structure of the eye is rich in moisture, resulting in a lack of contrast between tissues in the B-mode image, and the penetration depth is low due to the attenuation of the ultrasound wave. In this study, the entire internal ocular structure of a bovine eye was visualized in an ex vivo environment using the compound acoustic radiation force impulse (CARFI) imaging scheme based on the phase-inverted ultrasound transducer (PIUT). In the proposed method, the aperture of the PIUT is divided into four sections, and the PIUT is driven by the out-of-phase input signal capable of generating split-focusing at the same time. Subsequently, the compound imaging technique was employed to increase signal-to-noise ratio (SNR) and to reduce displacement error. The experimental results demonstrated that the proposed technique could provide an acoustic radiation force impulse (ARFI) image of the bovine eye with a broader depth-of-field (DOF) and about 80% increased SNR compared to the conventional ARFI image obtained using the in-phase input signal. Therefore, the proposed technique can be one of the useful techniques capable of providing the image of the entire ocular structure to diagnose various eye diseases.

Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024

The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.

Strain Elastography Fat-to-Lesion Index Is Associated with Mammography BI-RADS Grading, Biopsy, and Molecular Phenotype in Breast Cancer

Breast cancer (BC) affects millions of women worldwide, causing over 500,000 deaths annually. It is the leading cause of cancer mortality in women, with 70% of deaths occurring in developing countries. Elastography, which evaluates tissue stiffness, is a promising real-time minimally invasive technique for BC diagnosis. This study assessed strain elastography (SE) and the fat-to-lesion (F/L) index for BC diagnosis. This prospective study included 216 women who underwent SE, ultrasound, mammography, and breast biopsy (108 malignant, 108 benign). Three expert radiologists performed imaging and biopsies. Mean F/L index was 3.70 ± 2.57 for benign biopsies and 18.10 ± 17.01 for malignant. We developed two predictive models: a logistic regression model with AUC 0.893, 79.63% sensitivity, 87.62% specificity, 86.9% positive predictive value (+PV), and 80.7% negative predictive value (-PV); and a neural network with AUC 0.902, 80.56% sensitivity, 88.57% specificity, 87.9% +PV, and 81.6% -PV. The optimal Youden F/L index cutoff was >5.76, with 84.26% sensitivity and specificity. The F/L index positively correlated with BI-RADS (Spearman's r = 0.073, p < 0.001) and differed among molecular subtypes (Kruskal-Wallis, p = 0.002). SE complements mammography for BC diagnosis. With adequate predictive capacity, SE is fast, minimally invasive, and useful when mammography is contraindicated.

A human pilot study on positive electrostatic charge effects in solid tumors of the late-stage metastatic patients

Background

Correlative interactions between electrical charges and cancer cells involve important unknown factors in cancer diagnosis and treatment. We previously reported the intrinsic suppressive effects of pure positive electrostatic charges (PEC) on the proliferation and metabolism of invasive cancer cells without any effect on normal cells in cell lines and animal models. The proposed mechanism was the suppression of pro-caspases 3 and 9 with an increase in Bax/Bcl2 ratio in exposed malignant cells and perturbation induced in the KRAS pathway of malignant cells by electrostatic charges due to the phosphate molecule electrostatic charge as the trigger of the pathway. This study aimed to examine PECs as a complementary treatment for patients with different types of solid metastatic tumors, who showed resistance to chemotherapy and radiotherapy.

Methods

In this study, solid metastatic tumors of the end-stage patients (n = 41) with various types of cancers were locally exposed to PEC for at least one course of 12 days. The patient's signs and symptoms, the changes in their tumor size, and serum markers were followed up from 30 days before positive electrostatic charge treating (PECT) until 6 months after the study.

Results

Entirely, 36 patients completed the related follow-ups. Significant reduction in tumor sizes and cancer-associated enzymes as well as improvement in cancer-related signs and symptoms and patients' lifestyles, without any side effects on other tissues or metabolisms of the body, were observed in more than 80% of the candidates.

Conclusion

PECT induced significant cancer remission in combination with other therapies. Therefore, this non-ionizing radiation would be a beneficial complementary therapy, with no observable side effects of ionizing radiotherapy, such as post-radiation inflammation.

Linear Regression Modeling Based Scoring System to Reduce Benign Breast Biopsies Using Multi-parametric US with Color Doppler and SWE

RATIONALE AND OBJECTIVES

To develop a simple ultrasound (US) based scoring system to reduce benign breast biopsies.

MATERIALS AND METHODS

Women with BI-RADS 4 or 5 breast lesions underwent shear-wave elastography (SWE) imaging before biopsy. Standard US and color Doppler US (CDUS) parameters were recorded, and the size ratio (SzR=longest/shortest diameter) was calculated. Measured/calculated SWE parameters were minimum (SWVMin) and maximum (SWVMax) shear velocity, velocity heterogeneity (SWVH=SWVMax-SWVMin), velocity ratio (SWVR=SWVMin/SWVMax), and normalized SWVR (SWVRn=(SWVMax-SWVMin)/SWVMin). Linear regression analysis was performed by converting continuous parameters into categorical corresponding equivalents using decision tree analyses. Linear regression models were fitted using stepwise regression analysis and optimal coefficients for the predictors in the models were determined. A scoring model was devised from the results and validated using a different data set from another center consisting of 187 cases with BI-RADS 3, 4, and 5 lesions.

RESULTS

A total of 418 lesions (238 benign, 180 malignant) were analyzed. US and CDUS parameters exhibited poor (AUC=0.592-0.696), SWE parameters exhibited poor-good (AUC=0.607-0.816) diagnostic performance in benign/malignant discrimination. Linear regression models of US+CDUS and US+SWE parameters revealed an AUC of 0.819 and 0.882, respectively. The developed scoring system could have avoided biopsy in 37.8% of benign lesions while missing 1.1% of malignant lesions. The scoring system was validated with a 100% NPV rate with a specificity of 74.6%.

CONCLUSION

The linear regression model using US+SWE parameters performed better than any single parameter alone. The developed scoring method could lead to a significant decrease in benign biopsies.

Translating new breast ultrasound techniques into clinical practice: evaluating their intended uses and describing other unexpected uses for them

Several new ultrasound tools have been developed to further evaluate breast lesions detected on B-mode ultrasound. Strain elastography (SRE) was developed to assess the likelihood of malignancy of lesions based on their stiffness. This has been incorporated into the latest edition of the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) lexicon and atlas. However, no agreed cut-off stiffness values have been established to distinguish benign from malignant lesions making the translation into routine clinical practice difficult. Superb microvascular imaging (SMI) was developed to better evaluate the vascularity within sonographic lesions and assess their likelihood of malignancy. However, there is also no agreed cut-off value for vascular index (VI) to distinguish between benign and malignant lesions. MicroPure was developed to better visualize and evaluate calcifications seen on ultrasound. Its effective use in breast screening and evaluating the calcifications detected for likelihood of malignancy have not been established. This article describes the original intended uses of these applications and reviews the studies evaluating them, showing the varying success of the translation of these tools into routine clinical practice. Also described are some other uses of these tools for which they were not originally intended. This illustrates the importance of being perceptive to alternative uses of imaging tools in their translation from bench to bedside.

Correlation of shear-wave elastography parameters with the molecular subtype and axillary lymph node status in breast cancer

PURPOSE

To examine correlations between shear-wave elastography (SWE) parameters with molecular subtype and axillary lymph node (LN) status of breast cancer.

METHODS

We retrospectively analyzed 545 consecutive women (mean age, 52.7 ± 10.7 years; range, 26-83) with breast cancer who underwent preoperative breast ultrasound with SWE between December 2019 and January 2021. SWE parameters (E_max, E_mean, and E_ratio) and the histopathologic information from surgical specimens including histologic type, histologic grade, size of invasive cancer, hormone receptor and HER2 status, Ki-67 proliferation index, and axillary LN status were analyzed. The relationships between SWE parameters and histopathologic findings were analyzed using an independent sample t-test, one-way ANOVA test with Tukey's post hoc test, and logistic regression analyses.

RESULTS

Higher stiffness values of SWE were associated with larger lesion size (>20 mm) on ultrasound, high histologic grade, larger invasive cancer size (>20 mm), high Ki-67, and axillary LN metastasis. E_max and E_mean were the lowest in the luminal A-like subtype, and all three parameters were the highest in the triple-negative subtype. Lower value of E_max was independently associated with the luminal A-like subtype (P = 0.04). Higher value of E_mean was independently associated with axillary LN metastasis for tumors ≤ 20 mm (P = 0.03).

CONCLUSION

Increases in the tumor stiffness values on SWE were significantly associated with aggressive histopathologic features of breast cancer. Lower stiffness values were associated with the luminal A-like subtype, and tumors with higher stiffness values were associated with axillary LN metastasis in small breast cancers.

Recent Advances in Ultrasound Breast Imaging: From Industry to Clinical Practice

Breast ultrasound (US) has undergone dramatic technological improvement through recent decades, moving from a low spatial resolution, grayscale-limited technique to a highly performing, multiparametric modality. In this review, we first focus on the spectrum of technical tools that have become commercially available, including new microvasculature imaging modalities, high-frequency transducers, extended field-of-view scanning, elastography, contrast-enhanced US, MicroPure, 3D US, automated US, S-Detect, nomograms, images fusion, and virtual navigation. In the subsequent section, we discuss the broadened current application of US in breast clinical scenarios, distinguishing among primary US, complementary US, and second-look US. Finally, we mention the still ongoing limitations and the challenging aspects of breast US.

Comparison of diagnostic potential of shear wave elastography between breast mass lesions and non-mass-like lesions

BACKGROUND

Shear wave elastography (SWE) can improve the specificity of B-mode ultrasound (US) without reducing the sensitivity for breast cancer diagnosis. Existing research on SWE includes both mass lesions and non‑mass‑like (NML) lesions or only NML lesions; however, there are no studies comparing the diagnostic potential of SWE in the detection of mass and NML lesions in the same trial.

OBJECTIVE

This study aimed to compare the diagnostic performance of SWE in detecting mass lesions and NML lesions and determine the different individualised thresholds of the SWE parameters according to the lesion type.

METHODS

This Study included 623 breast lesions of 562 consecutive women, who were scheduled for conventional US and SWE between January 2021 and December 2021. The diagnostic performances of conventional US and each quantitative SWE parameter (maximum elastic modulus [Emax], mean elastic modulus [Emean], and elastic modulus standard deviation [Esd]) were assessed. Histological diagnosis for all Breast Imaging Reporting and Database System (BI-RADS) category 4/5 patients and some BI-RADS category 3 patients and the follow-up results of other BI-RADS category 3 patients were used as the reference standard.

RESULTS

In this study, 281 benign lesions and 342 malignant lesions were identified. The diagnostic performance of conventional US and SWE was better in the mass lesion group than in the NML lesion group. Every SWE parameter had a different threshold in each group, and the thresholds of the SWE parameters were higher in the mass lesion group than in the NML lesion group. In the mass lesion group, Esd had the highest Az value, whereas in the NML lesion group, Emax had the highest Az value. In both the mass and NML lesion groups, the diagnostic specificity of the combination of conventional US and SWE was significantly higher than that of conventional US alone (P < 0.05), without a significantly decrease in the diagnosticsensitivity.

CONCLUSIONS

SWE could increase the confidence of breast ultrasound diagnosis, especially for NML lesions. NML lesions had lower thresholds of SWE parameters than did the mass lesions.

Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature-based machine learning method for breast cancer detection to improve the performance beyond a benchmark deep learning algorithm and to furthermore provide a color overlay visual map of the probability of malignancy within a lesion. This overall framework is termed disease-specific imaging. Previously, 150 breast lesions were segmented and classified utilizing a modified fully convolutional network and a modified GoogLeNet, respectively. In this study multiparametric analysis was performed within the contoured lesions. Features were extracted from ultrasound radiofrequency, envelope, and log-compressed data based on biophysical and morphological models. The support vector machine with a Gaussian kernel constructed a nonlinear hyperplane, and we calculated the distance between the hyperplane and each feature's data point in multiparametric space. The distance can quantitatively assess a lesion and suggest the probability of malignancy that is color-coded and overlaid onto B-mode images. Training and evaluation were performed on in vivo patient data. The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic curve, which is more precise than the performance of radiologists and a deep learning system. Further, the correlation between the probability and Breast Imaging Reporting and Data System enables a quantitative guideline to predict breast cancer. Therefore, we anticipate that the proposed framework can help radiologists achieve more accurate and convenient breast cancer classification and detection.

Evaluation of diagnostic efficacy of multimode ultrasound in BI-RADS 4 breast neoplasms and establishment of a predictive model

Objectives

To explore the diagnostic efficacy of ultrasound (US), two-dimensional and three-dimensional shear-wave elastography (2D-SWE and 3D-SWE), and contrast-enhanced ultrasound (CEUS) in breast neoplasms in category 4 based on the Breast Imaging Reporting and Data System (BI-RADS) from the American College of Radiology (ACR) and to develop a risk-prediction nomogram based on the optimal combination to provide a reference for the clinical management of BI-RADS 4 breast neoplasms.

Methods

From September 2021 to April 2022, a total of 104 breast neoplasms categorized as BI-RADS 4 by US were included in this prospective study. There were 78 breast neoplasms randomly assigned to the training cohort; the area under the receiver-operating characteristic curve (AUC), 95% confidence interval (95% CI), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 2D-SWE, 3D-SWE, CEUS, and their combination were analyzed and compared. The optimal combination was selected to develop a risk-prediction nomogram. The performance of the nomogram was assessed by a validation cohort of 26 neoplasms.

Results

Of the 78 neoplasms in the training cohort, 16 were malignant and 62 were benign. Among the 26 neoplasms in the validation cohort, 6 were malignant and 20 were benign. The AUC values of 2D-SWE, 3D-SWE, and CEUS were not significantly different. After a comparison of the different combinations, 2D-SWE+CEUS showed the optimal performance. Least absolute shrinkage and selection operator (LASSO) regression was used to filter the variables in this combination, and the variables included Emax, Eratio, enhancement mode, perfusion defect, and area ratio. Then, a risk-prediction nomogram with BI-RADS was built. The performance of the nomogram was better than that of the radiologists in the training cohort (AUC: 0.974 vs. 0.863). In the validation cohort, there was no significant difference in diagnostic accuracy between the nomogram and the experienced radiologists (AUC: 0.946 vs. 0.842).

Conclusions

US, 2D-SWE, 3D-SWE, CEUS, and their combination could improve the diagnostic efficiency of BI-RADS 4 breast neoplasms. The diagnostic efficacy of US+3D-SWE was not better than US+2D-SWE. US+2D-SWE+CEUS showed the optimal diagnostic performance. The nomogram based on US+2D-SWE+CEUS performs well.

Accuracy and reproducibility of shear wave elastography according to the size and elasticity of lesions: A phantom study

While the extrinsic factors affecting reproducibility of shear wave elastography (SWE) have been well documented, there are few resources assessing intrinsic characteristics of the lesion affecting the reproducibility and accuracy of SWE. In this regard, this study aimed to evaluate the accuracy of measured elasticity and the reproducibility of SWE according to the lesion size and stiffness. Two breast radiologists examined 20 targets of 4 different levels of stiffness and 5 different sizes (2.5, 4, 7, 11, and 18 mm) in a customized elasticity phantom. The B-mode image, color elastography image, and kPa measurement were obtained twice by each examiner with a 1-week interval. Inter- and intra-observer reproducibility and the accuracy of measured kPa were analyzed using intraclass correlation coefficient (ICC) and Bland-Altman analysis. Subgroup analysis was run to evaluate the effect of lesion size and stiffness on the reproducibility and accuracy of measured kPa. Inter- and intraobserver reproducibility for measuring kPa showed excellent agreement (ICC: 0.9742 and 0.9582; ICC: 0.9932 and 0.9294). The size and stiffness of the targets did not affect reproducibility. The overall accuracy of measured kPa was very high (ICC: 0.8049). In the subgroup analysis, targets that were ≤4 mm in size showed lower accuracy (ICC: 0.542), whereas targets that were 7 and 11 mm in size showed higher accuracy (ICC: 0.9832 and 0.9656, respectively). SWE shows excellent reproducibility regardless of lesion size or stiffness in phantom targets. The accuracy of measured kPa is high in lesions that are 7 and 11 mm in size but is low in lesions that are ≤4 mm in size.

Prospective analysis of breast masses using the combined score for quantitative ultrasonography parameters

To investigate the diagnostic value of combined SWE, SMI, and B-mode US scores for distinguishing between benign and malignant masses. A total of 450 breast masses that underwent US-guided core needle biopsies were prospectively enrolled. The breast masses were assessed based on the BI-RADS and quantitative SWE and SMI parameters. The SWEmax, SWEratio, and SMI_VI cutoff value were determined using Youden’s index by comparison to the pathological results. The BI-RADS categories were scored on a scale from 1 to 5, and SWEmax, SWEratio, and SMI_VI were dichotomized based on each cutoff values (0 or 1). The combined scores (1 to 8) were calculated as the sum of the BI-RADS score and the quantitative scores and compared to the pathologic results using AUROC analysis. The cutoff values were 52.25 kPa for SWEmax, 5.03 for SWEratio, and 2.15% for SMI_VI. In AUROC, the combined scores showed significantly better diagnostic performance compared to BI-RADS alone (p < 0.001). The combined score showed significantly increased than BI-RADS alone in specificity (p < 0.001) and accuracy (p < 0.001), but a sensitivity decreased without significance (p = 0.082). When a combined score cutoff value of 4 was used, the false negative rate was 2.7%. Using the combined score, 76.4% of the C4a lesions were considered benign also pathologically diagnosed as benign. The combined scores showed improved diagnostic performance in differentiating between benign and malignant breast masses, which could be helpful for determining a breast biopsy eligibility.

Evaluation of Tissue Stiffness Around Lesions by Sound Touch Shear Wave Elastography in Breast Malignancy Diagnosis

The aim of the study described here was to assess the evaluation of tissue stiffness around lesions by sound touch shear wave elastography (STE) in breast malignancy diagnosis. This was an institutional ethics committee-approved, single-center study. A total of 90 women with breast masses examined with conventional ultrasound and STE were eligible for enrollment from December 2020 to July 2021. The maximum and mean elastic values of masses, E_max and E_mean, were determined. Shell function was used to measure the maximum and mean elastic values of tissues around masses in annular shells 0.5, 1.0, 1.5 and 2.0 mm wide, recorded as corresponding E_max-shell and E_mean-shell. All parameters were analyzed and compared with histopathologic results. Receiver operating characteristic curves were constructed to assess diagnostic performance. Logistic regression analysis was conducted to determine the best diagnostic model. Collagen fiber content of tissues around breast lesions was evaluated using Masson staining and ImageJ software. Ninety women with breast masses were included in this study; 50 had benign (mean diameter 15.84 ± 4.39 mm) and 40 had malignant (mean diameter 17.40 ± 5.42 mm) masses. The diagnostic value of E_{max-shell-2.0} was the highest (area under the curve = 0.930) with a sensitivity of 87.5% and specificity of 88%. According to stepwise logistic regression analysis, E_{max-shell-2.0} and age were independent predictors of malignancy. E_{max-shell-2.0} was also found to be highly correlated with the collagen fiber content of tissue in the malignant group (r = 0.877). Tissue stiffness around lesions measured by STE is a useful metric in identifying malignant breast masses by reflecting collagen fiber content, and E_{max-shell-2.0} performs best.

Deep learning in ultrasound elastography imaging: A review

It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi-static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayer perceptron, convolutional neural network, and recurrent neural network are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected. This article is protected by copyright. All rights reserved.

Breast Cancer—How Can Imaging Help?

Breast cancer is the most common malignant disease among women, causing death and suffering worldwide. It is known that, for the improvement of the survival rate and the psychological impact it has on patients, early detection is crucial. For this to happen, the imaging techniques should be used at their full potential. We selected and examined 44 articles that had as subject the use of a specific imaging method in breast cancer management (mammography, ultrasound, MRI, ultrasound-guided biopsy, PET-CT). After analyzing their data, we summarized and concluded which are the best ways to use each one of the mentioned techniques for a good outcome. We created a simplified algorithm with easy steps that can be followed by radiologists when facing this type of neoplasia.

Stiffness in breast masses with posterior acoustic shadowing: significance of ultrasound real time shear wave elastography

Background

To assess the stiffness of benign breast masses in ultrasound images with posterior acoustic shadowing (PAS) and malignant lesions, and explore the significance of differential diagnosis using ultrasound real time shear wave elastography.

Material and methods

All 117 mammary masses (98 patients) with PAS were assessed by using routine ultrasound examination, and elastic modulus values were obtained with the real time shear wave elastography mode. All breast lesions were confirmed by surgery or biopsy. The significance of differences in ultrasound elastography values between breast benign and malignant masses with posterior acoustic shadowing was assessed, and the ROC curves of elasticity modulus values were analyzed.

Results

Among the 117 masses, 72 were benign and 45 were malignant. The two types of breast masses showed significant differences in size, margin, internal echo, calcification, and blood flow characteristics (P < 0.05), although the difference in orientation was not significant (P > 0.05). Emean, Emax and Esd obtained with real time shear wave elastography showed statistically significant differences between benign masses with posterior acoustic shadowing and breast cancer (P < 0.05), while Emin showed no significant difference between them (P = 0.633). Ultrasound real time shear wave elastography showed higher sensitivity and specificity than conventional ultrasound.

Conclusions

Benign and malignant breast masses with PAS show different ultrasound manifestations. Real time shear wave elastography can facilitate the differential diagnosis and treatment planning for these breast masses.

Simplified Green's function for surface waves in quasi-incompressible elastic plates with application to elastography

Surface wave elastography is a growing method to estimate the elasticity in soft solids. It is particularly useful in the case of agrifoods like meat, cheese, or fruits because it does not require major infrastructure or large equipment and could be developed in portable devices. However, estimating the shear elastic properties from surface wave measurements is not straightforward. The shear wavelength in those materials is cm sized for the excitation frequencies usually employed in elastography (∼10² Hz), and the size of samples is comparable to it. Thus, the surface wave speed is frequency dependent with no direct relation to the shear wave speed. In this work we propose a simplified Green's function for soft solid elastic plates which allows to retrieve the shear elasticity from near field measurements. The model is compared with experimental results obtained in agar-gelatin phantoms and food samples (cheese and bovine liver). The results show a good overall agreement although improvements can be achieved by incorporating diffraction and viscosity to the model.

A Surgeon's Empirical Perspectives on Use of High-resolution Ultrasound in Preoperatively Detecting a Rupture in the Context of Breast Implant Crisis in Korea

BACKGROUND

We previously proposed a novel method for detecting a rupture of a breast implant using high-resolution ultrasound (HRUS). We therefore conducted this retrospective, observational study to describe its feasibility in making a preoperative diagnosis of rupture of the device in patients receiving an implant-based augmentation mammaplasty.

METHODS

We initially evaluated the medical records of the patients who had received primary or secondary augmentation mammaplasty using a breast implant at other hospitals for aesthetic or reconstructive purposes between August 31, 2017, and August 31, 2020. The patients underwent breast US using the Aplio i600 (Canon Medical System, Otawara, Tochigi, Japan) system with a 7-18 MHz linear transducer. Through a retrospective review of the patients' medical records, we analyzed their baseline and clinical characteristics. Then, we compared an agreement between preoperative diagnosis of rupture on HRUS and findings at reoperation.

RESULTS

A total of 29 patients with rupture (55 breasts) were evaluated for the performance of ultrasound in making a diagnosis of rupture. This showed that they were unaware of rupture but they were diagnosed with it on ultrasound. Preoperatively, there were no cases of rupture in 110 left breasts (80.9%) and 107 right breasts (78.7%), which exactly matched to the number of breasts without rupture on HRUS. Moreover, preoperatively, there were 26 (19.1%) and 29 cases (21.3%) of rupture in the left and right breast, respectively, which exactly matched to the number of breasts with rupture on HRUS.

CONCLUSIONS

In conclusion, patients who are suspected of having rupture of a breast implant should be stringently evaluated for presence of rupture and, if any, its scope using HRUS. Moreover, we propose that surgeons consider using HRUS in making a preoperative diagnosis of rupture of a breast implant.

LEVEL OF EVIDENCE III

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Probing elastic anisotropy of human skin in vivo with light using non-contact acoustic micro-tapping OCE and polarization sensitive OCT

Skin broadly protects the human body from undesired factors such as ultraviolet radiation and abrasion and helps conserve body temperature and hydration. Skin's elasticity and its level of anisotropy are key to its aesthetics and function. Currently, however, treatment success is often speculative and subjective, and is rarely based on skin's elastic properties because there is no fast and accurate non-contact method for imaging of skin's elasticity. Here we report on a non-contact and non-invasive method to image and characterize skin's elastic anisotropy. It combines acoustic micro-tapping optical coherence elastography (AμT-OCE) with a nearly incompressible transversely isotropic (NITI) model to quantify skin's elastic moduli. In addition, skin sites were imaged with polarization sensitive optical coherence tomography (PS-OCT) to help define fiber orientation. Forearm skin areas were investigated in five volunteers. Results clearly demonstrate elastic anisotropy of skin in all subjects. AμT-OCE has distinct advantages over competitive techniques because it provides objective, quantitative characterization of skin's elasticity without contact, which opens the door for broad translation into clinical use. Finally, we demonstrate that a combination of multiple OCT modalities (structural OCT, OCT angiography, PS-OCT and AμT-OCE) may provide rich information about skin and can be used to characterize scar.

Determining the elastography strain ratio cut off value for differentiating benign from malignant breast lesions: systematic review and meta-analysis

Background

Elastography is an addition to grey-scale ultrasonic examination that has gained substantial traction within the last decade. Strain ratio (SR) has been incorporated as a semiquantitative measure within strain elastography, thus a potential imaging biomarker. The World Federation for Ultrasound in Medicine and Biology (WFUMB) published guidelines in 2015 for breast elastography. These guidelines acknowledge the marked variance in SR cut-off values used in differentiating benign from malignant lesions. The objective of this review was to include more recent evidence and seek to determine the optimal strain ratio cut off value for differentiating between benign and malignant breast lesions.

Methods

Comprehensive search of MEDLINE and Web of Science electronic databases with additional searches via Google Scholar and handsearching set from January 2000 to May 2020 was carried out. For retrieved studies, screening for eligibility, data extraction and analysis was done as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy (PRISMA-DTA) Statement guidelines of 2018. Quality and risk of bias assessment of the studies were performed using the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.

Results

A total of 424 articles, 412 from electronic database and 12 additional searches were retrieved and 65 studies were included in the narrative synthesis and subgroup analysis. The overall threshold effect indicated significant heterogeneity among the studies with Spearman correlation coefficient of Logit (TPR) vs Logit (FPR) at − 0.301, p-value = 0.015. A subgroup under machine model consisting seven studies with 783 patients and 844 lesions showed a favourable threshold, Spearman’s correlation coefficient,0.786 (p = 0.036).

Conclusion

From our review, currently the optimal breast SR cut-off point or value remains unresolved despite the WFUMB guidelines of 2015. Machine model as a possible contributor to cut-off value determination was suggested from this review which can be subjected to more industry and multi-center research determination.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00447-5.

Diagnostic performance improvement with combined use of proteomics biomarker assay and breast ultrasound

PURPOSE

To investigate the combined use of blood-based 3-protein signature and breast ultrasound (US) for validating US-detected lesions.

METHODS

From July 2011 to April 2020, women who underwent whole-breast US within at least 6 months from sampling period were retrospectively included. Blood-based 3-protein signature (Mastocheck®) value and US findings were evaluated. Following outcome measures were compared between US alone and the combination of Mastocheck® value with US: sensitivity, specificity, positive predictive value (PPV), negative predictive value, area under the receiver operating characteristic curve (AUC), and biopsy rate.

RESULTS

Among the 237 women included, 59 (24.9%) were healthy individuals and 178 (75.1%) cancer patients. Mean size of cancers was 1.2 ± 0.8 cm. Median value of Mastocheck® was significantly different between nonmalignant (- 0.24, interquartile range [IQR] - 0.48, - 0.03) and malignant lesions (0.55, IQR - 0.03, 1.42) (P < .001). Utilizing Mastocheck® value with US increased the AUC from 0.67 (95% confidence interval [CI] 0.61, 0.73) to 0.81 (95% CI 0.75, 0.88; P < .001), and specificity from 35.6 (95% CI 23.4, 47.8) to 64.4% (95% CI 52.2, 76.6; P < .001) without loss in sensitivity. PPV was increased from 82.2 (95% CI 77.1, 87.3) to 89.3% (95% CI 85.0, 93.6; P < .001), and biopsy rate was significantly decreased from 79.3 (188/237) to 72.1% (171/237) (P < .001). Consistent improvements in specificity, PPV, and AUC were observed in asymptomatic women, in women with dense breast, and in those with normal/benign mammographic findings.

CONCLUSION

Mastocheck® is an effective tool that can be used with US to improve diagnostic specificity and reduce false-positive findings and unnecessary biopsies.

Bi-Modal Transfer Learning for Classifying Breast Cancers via Combined B-Mode and Ultrasound Strain Imaging

Although accurate detection of breast cancer still poses significant challenges, deep learning (DL) can support more accurate image interpretation. In this study, we develop a highly robust DL model based on combined B-mode ultrasound (B-mode) and strain elastography ultrasound (SE) images for classifying benign and malignant breast tumors. This study retrospectively included 85 patients, including 42 with benign lesions and 43 with malignancies, all confirmed by biopsy. Two deep neural network models, AlexNet and ResNet, were separately trained on combined 205 B-mode and 205 SE images (80% for training and 20% for validation) from 67 patients with benign and malignant lesions. These two models were then configured to work as an ensemble using both image-wise and layer-wise and tested on a dataset of 56 images from the remaining 18 patients. The ensemble model captures the diverse features present in the B-mode and SE images and also combines semantic features from AlexNet and ResNet models to classify the benign from the malignant tumors. The experimental results demonstrate that the accuracy of the proposed ensemble model is 90%, which is better than the individual models and the model trained using B-mode or SE images alone. Moreover, some patients that were misclassified by the traditional methods were correctly classified by the proposed ensemble method. The proposed ensemble DL model will enable radiologists to achieve superior detection efficiency owing to enhance classification accuracy for breast cancers in ultrasound (US) images.

AI-enhanced breast imaging: Where are we and where are we heading?

Significant advances in imaging analysis and the development of high-throughput methods that can extract and correlate multiple imaging parameters with different clinical outcomes have led to a new direction in medical research. Radiomics and artificial intelligence (AI) studies are rapidly evolving and have many potential applications in breast imaging, such as breast cancer risk prediction, lesion detection and classification, radiogenomics, and prediction of treatment response and clinical outcomes. AI has been applied to different breast imaging modalities, including mammography, ultrasound, and magnetic resonance imaging, in different clinical scenarios. The application of AI tools in breast imaging has an unprecedented opportunity to better derive clinical value from imaging data and reshape the way we care for our patients. The aim of this study is to review the current knowledge and future applications of AI-enhanced breast imaging in clinical practice.

Outcomes of Return to Routine Screening for BI-RADS 3 Lesions Detected at Supplemental Automated Whole-Breast Ultrasound in Women with Dense Breasts: A Prospective Study

Background: Supplemental screening breast ultrasound (US) detects additional cancers in women with dense breasts but identifies many BI-RADS 3 lesions that result in short-term follow-ups and biopsies. Objective: To evaluate outcomes in patients recommended for return to routine screening for lesions assessed as BI-RADS 3 on supplemental automated whole-breast US (ABUS). Methods: This prospective study invited patients with BI-RADS 1 or 2 on screening mammography and breast density C or D to undergo supplemental ABUS. ABUS was interpreted as BI-RADS 1, 2, 3, or 0. Return to routine screening was recommended for ABUS BI-RADS 1, 2, or 3. ABUS BI-RADS 0 lesions underwent targeted hand-held US. Remaining patients were followed for 2 years. Malignancy rates were compared using Fisher's exact tests. Results: A total of 2257 women (mean age, 58.0±11.2 years) were included. Supplemental ABUS was scored as BI-RADS 1 in 1186 (52.5%), BI-RADS 2 in 591 (26.2%), BI-RADS 3 in 395 (17.5%), and BI-RADS 0 in 85 (3.8%). A total of 394 patients with ABUS BI-RADS 3 had 2-year follow-up, during which no cancer (0%, 95% CI 0.0%-0.9%) was diagnosed in the quadrant of the lesion. Among patients with 2-year follow-up, breast cancer was diagnosed in 4/1117 (0.4%) with ABUS BI-RADS 1, 2/556 (0.4%) with ABUS BI-RADS 2, and 2/394 (0.5%) with ABUS BI-RADS 3 (cancer in other quadrant than the lesion). Malignancy rates were not different between ABUS BI-RADS 1, 2, and 3 (p=.28). ABUS recall rate was 3.8% (85/2257; 95% CI 3.6%-4.0%). If short-term follow-up had been recommended for ABUS BI-RADS 3, ABUS recall rate would have been 21.3% (480/2257, 95% CI 19.6%-23.0%). Biopsy rate was 0.4% (12/2257; 95% CI 0.3%-0.9%); positive biopsy rate was 58.3% (7/12). One of 7 patients diagnosed with cancer by initial supplemental ABUS, and none of 8 patients diagnosed with cancer during subsequent follow-up, had node-positive cancer. Conclusions: Return to routine screening for ABUS BI-RADS 3 lesions results in a substantial decrease in recall rate, while being unlikely to result in adverse outcome. Clinical Impact: This prospective study supports a recommendation for routine annual follow-up for BI-RADS 3 lesions at supplemental ABUS.

Experimental Evidence of Generation and Reception by a Transluminal Axisymmetric Shear Wave Elastography Prototype

Experimental evidence on testing a non-ultrasonic-based probe for a new approach in transluminal elastography was presented. The proposed modality generated shear waves by inducing oscillatory rotation on the lumen wall. Detection of the propagated waves was achieved at a set of receivers in mechanical contact with the lumen wall. The excitation element of the probe was an electromagnetic rotational actuator whilst the sensing element was comprised by a uniform anglewise arrangement of four piezoelectric receivers. The prototype was tested in two soft-tissue-mimicking phantoms that contained lumenlike conduits and stiffer inclusions. The shear wave speed of the different components of the phantoms was characterized using shear wave elastography. These values were used to estimate the time-of-flight of the expected reflections. Ultrafast ultrasound imaging, based on Loupas’ algorithm, was used to estimate the displacement field in transversal planes to the lumenlike conduit and to compare against the readouts from the transluminal transmission–reception tests. Experimental observations between ultrafast imaging and the transluminal probe were in good agreement, and reflections due to the stiffer inclusions were detected by the transluminal probe. The obtained experimental evidence provided proof-of-concept for the transluminal elastography probe and encouraged further exploration of clinical applications.

Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses

A major limitation of screening breast ultrasound (US) is a substantial number of false-positive biopsy. This study aimed to develop a deep learning-based computer-aided diagnosis (DL-CAD)-based diagnostic model to improve the differential diagnosis of screening US-detected breast masses and reduce false-positive diagnoses. In this multicenter retrospective study, a diagnostic model was developed based on US images combined with information obtained from the DL-CAD software for patients with breast masses detected using screening US; the data were obtained from two hospitals (development set: 299 imaging studies in 2015). Quantitative morphologic features were obtained from the DL-CAD software, and the clinical findings were collected. Multivariable logistic regression analysis was performed to establish a DL-CAD-based nomogram, and the model was externally validated using data collected from 164 imaging studies conducted between 2018 and 2019 at another hospital. Among the quantitative morphologic features extracted from DL-CAD, a higher irregular shape score (P = .018) and lower parallel orientation score (P = .007) were associated with malignancy. The nomogram incorporating the DL-CAD-based quantitative features, radiologists' Breast Imaging Reporting and Data Systems (BI-RADS) final assessment (P = .014), and patient age (P < .001) exhibited good discrimination in both the development and validation cohorts (area under the receiver operating characteristic curve, 0.89 and 0.87). Compared with the radiologists' BI-RADS final assessment, the DL-CAD-based nomogram lowered the false-positive rate (68% vs. 31%, P < .001 in the development cohort; 97% vs. 45% P < .001 in the validation cohort) without affecting the sensitivity (98% vs. 93%, P = .317 in the development cohort; each 100% in the validation cohort). In conclusion, the proposed model showed good performance for differentiating screening US-detected breast masses, thus demonstrating a potential to reduce unnecessary biopsies.

Breast Elasticity Imaging Techniques: Comparison of Strain Elastography and Shear-Wave Elastography in the Same Population

Our purpose was to compare the diagnostic performances of strain elastography (SE) and shear-wave elastography (SWE) in differentiating breast lesions by combining with conventional ultrasound (US). A total of 198 patients with 203 breast lesions underwent conventional US, SE and SWE examination using MyLab 90 and Aixplorer US systems. The SE parameters were SEscore, fat-to-lesion ratio, gland-to-lesion ratio, muscle-to-lesion ratio and SEmean, and the SWE parameters were Emax, Emean, Emin and Esd. Conventional US had the best diagnostic performance, with an area under the curve (AUC) of 0.896. Among all SE parameters, the AUCs of SEscore, fat-to-lesion ratio and SEmean were 0.802, 0.810 and 0.833. For SWE parameters, they were 0.845, 0.746 and 0.845, respectively, for Emax, Emean and Esd. When combined with US, the sensitivity and AUC of SWE seemed to be better than those of SE (96.55% vs. 93.10%, 0.958 vs. 0.947), but no statistically significant difference existed between them.

Combination of shear-wave elastography with ultrasonography for detection of breast cancer and reduction of unnecessary biopsies: a systematic review and meta-analysis

Purpose

This study was undertaken to compare the diagnostic performance and biopsy reduction rate of combined shear-wave elastography (SWE) and B-mode ultrasonography (US) versus B-mode US alone for breast lesions and to determine the most discriminatory parameter in SWE.

Methods

A systematic review and meta-analysis were conducted. The resources for the study were obtained from MEDLINE, Embase, Cochrane Library, and KoreaMed on August 17, 2018. The quality of the articles was evaluated using the Scottish Intercollegiate Guidelines Network (SIGN) tool.

Results

Twenty-five articles with 5,147 breast lesions were selected. The meta-analysis showed pooled sensitivities of 0.94 and 0.97 (P=0.087), pooled specificities of 0.85 and 0.61 (P=0.009), and area under the receiver operating characteristic curve (AUC) of 0.96 and 0.96 (P=0.095) for combined SWE and B-mode US versus B-mode US alone. When SWE was combined with B-mode US, the Breast Imaging Reporting and Data System category changed from 4 to 3 in 71.3% of the tests, decreasing the frequency of unnecessary biopsies by 41.1%. All four parameters of SWE (the color grade of lesion stiffness, maximum elasticity, mean elasticity, and color grade of lesion stiffness/homogeneity of the lesion) improved the specificity when they were added to B-mode US. The AUC for each SWE parameter was 0.99, 0.96, 0.96, and 0.93, respectively.

Conclusion

Adding SWE to B-mode US not only provides additional diagnostic information for differentiating between benign and malignant breast lesions, but also decreases the likelihood of unnecessary biopsies.

The role of rare breast cancers in the false negative strain elastography results

PURPOSE

Elastography was primarily used as an adjunctive method along with ultrasonography in differentiation between benign from malignant lesions. Occasionally, overlaps can occur which are caused by some rare invasive breast cancers. Our aim is to analyze the role of rare breast cancers in false negative strain elastography results and to assess the relation among false negative results and tumor size, lesion distance to skin, and tumor grade.

METHODS

Patients with BI-RADS 5 category underwent strain elastography and core biopsy. All those with confirmed invasive breast cancer were included. For each rare breast cancer, four usual invasive breast cancer cases were taken as a control group. The cut-off value of strain ratio was considered as 2.3. The true positive and the false negative groups were compared in terms of histological type (rare carcinomas and the others) and the other parameters. Pearson Chi-square and Fisher's exact test were used for statistical analyses. P values < 0.05 were considered statistically significant.

RESULTS

One hundred-thirteen patients were defined as true positive (70.6%), and 47 patients were defined as false negative (29.4%). Strain ratio values of the rare breast cancers were significantly lower than those of the other breast cancers (p = 0.012). There was no statistically significant difference between the groups with respect to tumor size, distance to skin, and tumor grade (p > 0.05).

CONCLUSION

The rare breast cancers are an important cause of false negativity in elastographic evaluation of invasive breast cancers. The results should be interpreted in combination with grayscale US findings.

What shear wave elastography parameter best differentiates breast cancer and predicts its histologic aggressiveness?

Purpose

This study aimed to identify useful shear wave elastography (SWE) parameters for differentiating breast cancer and predicting associated immunohistochemical factors and subtypes.

Methods

From November 2018 to February 2019, a total of 211 breast lesions from 190 patients who underwent conventional breast ultrasonography and SWE were included. The Breast Imaging Reporting and Data System categories and qualitative and quantitative SWE parameters for each lesion were obtained. Pathologic results including immunohistochemical factors were evaluated. The diagnostic performance of each parameter and its correlation with histological characteristics, immunohistochemical factors, and subtypes of breast cancer were analyzed using analysis of variance, the independent t test, the Fisher exact test, logistic regression analysis, and the DeLong method.

Results

Among 211 breast lesions, 82 were malignant, and 129 were benign. Of the SWE parameters, Emax showed the highest area under the curve (AUC) for differentiating malignant from benign lesions (AUC, 0.891; cut-off>50.85). Poor tumor differentiation and progesterone receptor-negativity were correlated with higher SDmean and Emax (P<0.05). Ki-67-positive breast cancer showed higher SDmean and a heterogeneous color distribution (P<0.05). Ki-67 and cytokeratin 5/6-positive breast cancers showed higher Emax/Efat ratios (P<0.05). Luminal B, human epidermal growth factor receptor 2-enriched, and triple-negative (non-basal) subtypes showed somewhat higher SDmean values than the luminal A and triple-negative (basal) subtypes (P=0.028).

Conclusion

Emax is a reliable parameter for differentiating malignancies from benign breast lesions. In addition, high stiffness and SDmean values in tumors measured on SWE could be used to predict poorly differentiated, progesterone receptor-negative, or Ki-67-positive breast cancer.

Why Are Viscosity and Nonlinearity Bound to Make an Impact in Clinical Elastographic Diagnosis?

The adoption of multiscale approaches by the biomechanical community has caused a major improvement in quality in the mechanical characterization of soft tissues. The recent developments in elastography techniques are enabling in vivo and non-invasive quantification of tissues' mechanical properties. Elastic changes in a tissue are associated with a broad spectrum of pathologies, which stems from the tissue microstructure, histology and biochemistry. This knowledge is combined with research evidence to provide a powerful diagnostic range of highly prevalent pathologies, from birth and labor disorders (prematurity, induction failures, etc.), to solid tumors (e.g., prostate, cervix, breast, melanoma) and liver fibrosis, just to name a few. This review aims to elucidate the potential of viscous and nonlinear elastic parameters as conceivable diagnostic mechanical biomarkers. First, by providing an insight into the classic role of soft tissue microstructure in linear elasticity; secondly, by understanding how viscosity and nonlinearity could enhance the current diagnosis in elastography; and finally, by compounding preliminary investigations of those elastography parameters within different technologies. In conclusion, evidence of the diagnostic capability of elastic parameters beyond linear stiffness is gaining momentum as a result of the technological and imaging developments in the field of biomechanics.