WFUMB guidelines and recommendations for clinical use of ultrasound elastography: Part 2: breast

An Optimization Approach for Creating Application-specific Ultrasound Speckle Tracking Algorithms

OBJECTIVE

Ultrasound speckle tracking enables in vivo measurement of soft tissue deformation or strain, providing a non-invasive diagnostic tool to quantify tissue health. However, adoption into new fields is challenging since algorithms need to be tuned with gold-standard reference data that are expensive or impractical to acquire. Here, we present a novel optimization approach that only requires repeated measurements, which can be acquired for new applications where reference data might not be readily available or difficult to get hold of.

METHODS

Soft tissue motion was captured using ultrasound for the medial collateral ligament (MCL) of three quasi-statically loaded porcine stifle joints, and medial ligamentous structures of a dynamically loaded human cadaveric knee joint. Using a training subset, custom speckle tracking algorithms were created for the porcine and human ligaments using surrogate optimization, which aimed to maximize repeatability by minimizing the normalized standard deviation of calculated strain maps for repeat measurements. An unseen test subset was then used to validate the tuned algorithms by comparing the ultrasound strains to digital image correlation (DIC) surface strains (porcine specimens) and length change values of the optically tracked ligament attachments (human specimens).

RESULTS

After 1500 iterations, the optimization routine based on the porcine and human training data converged to similar values of normalized standard deviations of repeat strain maps (porcine: 0.19, human: 0.26). Ultrasound strains calculated for the independent test sets using the tuned algorithms closely matched the DIC measurements for the porcine quasi-static measurements (R > 0.99, RMSE < 0.59%) and the length change between the tracked ligament attachments for the dynamic human dataset (RMSE < 6.28%). Furthermore, strains in the medial ligamentous structures of the human specimen during flexion showed a strong correlation with anterior/posterior position on the ligaments (R > 0.91).

CONCLUSION

Adjusting ultrasound speckle tracking algorithms using an optimization routine based on repeatability led to robust and reliable results with low RMSE for the medial ligamentous structures of the knee. This tool may be equally beneficial in other soft-tissue displacement or strain measurement applications and can assist in the development of novel ultrasonic diagnostic tools to assess soft tissue biomechanics.

Associated factors leading to misdiagnosis of a combined diagnostic model of different types of strain imaging and conventional ultrasound in evaluation of breast lesions: Selection strategy for using different types of strain imaging in evaluation of breast lesions

OBJECTIVE

To evaluate the effectiveness of a decision tree that integrates conventional ultrasound (CUS) with two different strain imaging (SI) techniques for diagnosing breast lesions, and to analyze the factors contributing to false negative (FN) and false positive (FP) in the decision tree's outcomes.

MATERIALS AND METHODS

Imaging and clinical data of 796 cases in the training set and 351 cases in the validation set were prospectively collected. A decision tree model that combines two types of SI and CUS was constructed, and its diagnostic performance was analyzed. Univariate analysis and multivariate analysis were applied to identify independent risk factors associated with FP and FN results of the decision tree model.

RESULTS

Size, shape, margin, vascularity, the types of internal calcifications, EI score and VTI pattern were found to be significantly independently associated with the diagnosis of benign and malignant breast lesions. Therefore, size, shape, margin, vascularity, EI score and VTI pattern were used to construct decision tree models. The Tree (EI+VTI) model had the highest AUC. Both in the training and validation groups, the AUC of Tree (EI+VTI) was significantly higher compared with that of EI, VTI, and BI-RADS (all, P < 0.05). Orientation, posterior acoustic features and the types of internal calcifications were significantly positively associated with misdiagnosis results of Tree (EI+VTI) in evaluation of breast lesions (all P < 0.05).

CONCLUSION

The diagnostic model based on a decision tree that integrates two distinct types of SI with CUS enhances the diagnostic accuracy of each method when used individually. This integration lowers the misdiagnosis rate, potentially assisting radiologists in more effective lesion assessments. When applying the decision tree model, attention should be paid to the orientation, posterior acoustic features, and the types of internal calcifications of the lesions.

Shear wave elastography to assess stiffness of the human ovary and other reproductive tissues across the reproductive lifespan in health and disease†

The ovary is one of the first organs to show overt signs of aging in the human body, and ovarian aging is associated with a loss of gamete quality and quantity. The age-dependent decline in ovarian function contributes to infertility and an altered endocrine milieu, which has ramifications for overall health. The aging ovarian microenvironment becomes fibro-inflammatory and stiff with age, and this has implications for ovarian physiology and pathology, including follicle growth, gamete quality, ovulation dynamics, and ovarian cancer. Thus, developing a non-invasive tool to measure and monitor the stiffness of the human ovary would represent a major advance for female reproductive health and longevity. Shear wave elastography is a quantitative ultrasound imaging method for evaluation of soft tissue stiffness. Shear wave elastography has been used clinically in assessment of liver fibrosis and characterization of tendinopathies and various neoplasms in thyroid, breast, prostate, and lymph nodes as a non-invasive diagnostic and prognostic tool. In this study, we review the underlying principles of shear wave elastography and its current clinical uses outside the reproductive tract as well as its successful application of shear wave elastography to reproductive tissues, including the uterus and cervix. We also describe an emerging use of this technology in evaluation of human ovarian stiffness via transvaginal ultrasound. Establishing ovarian stiffness as a clinical biomarker of ovarian aging may have implications for predicting the ovarian reserve and outcomes of Assisted Reproductive Technologies as well as for the assessment of the efficacy of emerging therapeutics to extend reproductive longevity. This parameter may also have broad relevance in other conditions where ovarian stiffness and fibrosis may be implicated, such as polycystic ovarian syndrome, late off target effects of chemotherapy and radiation, premature ovarian insufficiency, conditions of differences of sexual development, and ovarian cancer. Summary sentence:  Shear Wave Elastography is a non-invasive technique to study human tissue stiffness, and here we review its clinical applications and implications for reproductive health and disease.

Soft tissue elasticity at teeth and implant sites. A novel outcome measure of the soft tissue phenotype

AIM

To assess ultrasonographic tissue elasticity at teeth and implant sites and its variation after peri-implant soft tissue augmentation with a connective tissue graft (CTG).

METHODS

Twenty-eight patients, each contributing with one clinically healthy dental implant exhibiting a soft tissue dehiscence (PSTD), were included. Implant sites were augmented with CTG and monitored over 12 months. Ultrasonographic strain elastography, expressed as strain ratios (SR1, SR2, and SR3, respectively) was assessed at baseline, 6-, and 12-month, and compared with the corresponding contralateral homologous natural tooth. SR1 assessed the strain/elasticity of the midfacial coronal portion of the soft tissue in comparison to the natural tooth crown/implant-supported crown, SR2 evaluated the strain of the midfacial coronal soft tissue in relation to the one of the alveolar mucosa, while SR3 depicted the strain of the midfacial soft tissue in relation to the interproximal soft tissue on the transverse ultrasound scan.

RESULTS

SR1 in natural dentition and at implant sites was 0.20 ± 0.08 and 0.30 ± 0.14, respectively (p = .002), indicating that the coronal portion of the soft tissue around teeth is generally more elastic than its counterpart around dental implants. Soft tissue augmentation with CTG promoted an increased stiffness of the midfacial coronal portion of the soft tissue over 12 months (p < .001 for SR1, SR2, and SR3). Strain ratios at the 12-month time points were significantly higher than the values observed at 6 months (p < .001). Regression analysis demonstrated that strain elastography ratios in natural dentition were significantly associated with keratinized gingiva width, and gingival thickness. At implant sites, SR1 was significantly associated with keratinized mucosa width and mucosal thickness (p < .001 for both correlations), SR2 was significantly associated with keratinized mucosa width (p = .013), and SR3 was significantly associated with the surgical technique performed in combination with CTG (p = .022).

CONCLUSION

Ultrasound strain elastography captures and quantifies tissue elasticity and its changes after soft tissue augmentation. A different baseline tissue elasticity was observed between teeth and dental implants in the most coronal aspect of the soft tissue. The main factors affecting tissue elasticity-related outcomes were the keratinized tissue width, and mucosal thickness.

Shear wave elastography for solid breast masses evaluation: Quantitative measurement of mean elasticity value and elasticity ratio

Purpose

Shear wave elastography (SWE), an ultrasonographic technique to measure the elasticity of mass lesions to evaluate breast mass. This study aimed to find out the cutoff values identifying breast malignancy using the mean elasticity (E-mean) and elasticity ratio (E-ratio) of breast masses.

Methods

This retrospective study included women underwent SWE and US-guided biopsy of breast masses. During conventional US, the SWE mode was also performed, determining elasticity measurements, E-mean and E-ratio. Histopathological reports were obtained to identify mass status. The optimal and alternative cutoff values for E-mean and E-ratio to determine malignancy were assessed by receiver operating characteristic (ROC) curve analysis and Youden's index score.

Results

Among 147 benign and 93 malignant masses, the median of E-means were 26.20 (IQR 15.70-56.60) and 141.60 (IQR 119.80-154.60) kPa and the median E-ratios were 3.11 (IQR 1.83-5.23) and 9.24 (IQR 6.76-12.44), respectively. Using Youden's index, the optimal cutoff values for E-mean and E-ratio were 90.35 and 5.89, with sensitivity of 87.1 % and 82.8 %, specificity of 89.1 % and 83.7 %, positive predictive value (PPV) of 83.5 % and 76.2 %, negative predictive value (NPV) of 91.6 % and 88.5 %, positive likelihood ratio (LR+) of 8.00 and 5.07, and negative likelihood ratio (LR-) of 0.14 and 0.21, respectively.

Conclusion

This study revealed that SWE is useful in predicting malignancy. With the optimal cutoff values of E-mean and E-ratio at 90.35 kPa and 5.89, the sensitivity was nearly 90 % with E-mean and slightly over 80 % with E-ratio, respectively. These findings could be used in conjunction with conventional US.

Preoperative Role of Superb Microvascular Imaging and Shear-Wave Elastography for Prediction of Axillary Lymph Node Metastasis in Patients With Breast Cancer

ABSTRACT

This study aims to evaluate the role of shearwave elastography (SWE) and superb microvascular imaging (SMI) for preoperative prediction of axillary lymph node metastasis (ALNM) in patients with breast cancer. In a cohort of 214 women with breast cancer, B-Mode ultrasonography (US), SMIvascular-index (SMIvi), and SWE (E-mean, E-ratio) values were recorded before tru-cut biopsy. Axillary fine-needle aspiration biopsy (FNAB) and sentinel lymph node sampling results were collected. Imaging findings and histopathological data were statistically compared. Receiver operating characteristic curve analysis was used to evaluate diagnostic performance. Reverse stepwise logistical regression analysis was conducted. Although ALNM was negative in 111 cases, it was positive in 103 patients. Axillary lymph node metastasis (+) group had larger size ( P < 0.001), higher vascularization (SMIvi: 8.0 ± 6.0 versus 5.0 ± 4.3, P < 0.001), and higher elasticity value (E-mean: 129 ± 31 kPa versus 117.3 ± 40 kPa, P = 0.014). Axillary lymph node metastasis was observed statistically more frequently in Her-2 positive cases ( P = 0.005). There was no significant difference between other B-mode US findings ( P > 0.05), SMI Adler ( P = 0.878), and E-ratio ( P = 0.212). The most appropriate cutoff value for the prediction of ALNM was 23.5 mm for size, 3.8 for SMIvi, and 138.5 kPa for E-mean. The most sensitive (77%) method was the SMIvi measurement, while the most specific (86%) finding was Her-2 positivity. The combined model (being Her-2 positive, >23.5 cm, and >3.8 SMIvi) increased the specificity (78%), PPV (71%), and accuracy (68%). Although the increased size is a previously studied parameter in predicting the risk of ALNM, Her-2 and data obtained by SWE, and SMI can be used to assist conventional US.

A domain knowledge-based interpretable deep learning system for improving clinical breast ultrasound diagnosis

BACKGROUND

Though deep learning has consistently demonstrated advantages in the automatic interpretation of breast ultrasound images, its black-box nature hinders potential interactions with radiologists, posing obstacles for clinical deployment.

METHODS

We proposed a domain knowledge-based interpretable deep learning system for improving breast cancer risk prediction via paired multimodal ultrasound images. The deep learning system was developed on 4320 multimodal breast ultrasound images of 1440 biopsy-confirmed lesions from 1348 prospectively enrolled patients across two hospitals between August 2019 and December 2022. The lesions were allocated to 70% training cohort, 10% validation cohort, and 20% test cohort based on case recruitment date.

RESULTS

Here, we show that the interpretable deep learning system can predict breast cancer risk as accurately as experienced radiologists, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval = 0.882 - 0.921), sensitivity of 75.2%, and specificity of 91.8% on the test cohort. With the aid of the deep learning system, particularly its inherent explainable features, junior radiologists tend to achieve better clinical outcomes, while senior radiologists experience increased confidence levels. Multimodal ultrasound images augmented with domain knowledge-based reasoning cues enable an effective human-machine collaboration at a high level of prediction performance.

CONCLUSIONS

Such a clinically applicable deep learning system may be incorporated into future breast cancer screening and support assisted or second-read workflows.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Is musculoskeletal pain associated with increased muscle stiffness? Evidence map and critical appraisal of muscle measurements using shear wave elastography

INTRODUCTION AND AIMS

Approximately 21% of the world's population suffers from musculoskeletal conditions, often associated with sensations of stiff muscles. Targeted therapy requires knowing whether typically involved muscles are objectively stiffer compared to asymptomatic individuals. Muscle stiffness is quantified using ultrasound shear wave elastography (SWE). Publications on SWE-based comparisons of muscle stiffness between individuals with and without musculoskeletal pain are increasing rapidly. This work reviewed and mapped the existing evidence regarding objectively measured muscle stiffness in musculoskeletal pain conditions and surveyed current methods of applying SWE to measure muscle stiffness.

METHODS

A systematic search was conducted in PubMed and CINAHL using the keywords "muscle stiffness", "shear wave elastography", "pain", "asymptomatic controls" and synonyms. The search was supplemented by a hand search using Google Scholar. Included articles were critically appraised with the AXIS tool, supplemented by items related to SWE methods. Results were visually mapped and narratively described.

RESULTS

Thirty of 137 identified articles were included. High-quality evidence was missing. The results comprise studies reporting lower stiffness in symptomatic participants, no differences between groups and higher stiffness in symptomatic individuals. Results differed between pain conditions and muscles, and also between studies that examined the same muscle(s) and pathology. The methods of the application of SWE were inconsistent and the reporting was often incomplete.

CONCLUSIONS

Existing evidence regarding the objective stiffness of muscles in musculoskeletal pain conditions is conflicting. Methodological differences may explain most of the inconsistencies between findings. Methodological standards for SWE measurements of muscles are urgently required.

Ultrasound Viscoelastography by Acoustic Radiation Force: A State-of-the-Art Review

Ultrasound elastography (USE) is a promising tool for tissue characterization as several diseases result in alterations of tissue structure and composition, which manifest as changes in tissue mechanical properties. By imaging the tissue response to an applied mechanical excitation, USE mimics the manual palpation performed by clinicians to sense the tissue elasticity for diagnostic purposes. Next to elasticity, viscosity has recently been investigated as an additional, relevant, diagnostic biomarker. Moreover, since biological tissues are inherently viscoelastic, accounting for viscosity in the tissue characterization process enhances the accuracy of the elasticity estimation. Recently, methods exploiting different acquisition and processing techniques have been proposed to perform ultrasound viscoelastography. After introducing the physics describing viscoelasticity, a comprehensive overview of the currently available USE acquisition techniques is provided, followed by a structured review of the existing viscoelasticity estimators classified according to the employed processing technique. These estimators are further reviewed from a clinical usage perspective, and current outstanding challenges are discussed.

In Vivo Ultrasound Shear Wave Elastography Assessment of Acute Compartment Syndrome in a Turkey Model

OBJECTIVE

The aim of the work described here was to evaluate the objectivity and reproducibility of non-invasive intra-compartment pressure (ICP) measurement using ultrasound shear wave elastography (SWE) in a turkey model in vivo and to determine the biological and histologic changes in acute compartment syndrome (ACS).

METHODS

Twenty-four turkeys were randomly divided into four groups based on the duration and fasciotomy of ACS created by infusion of up to 50 mm Hg in the tibialis muscle: group 1, ACS 2 h; group 2, ACS 4 h; group 3, ACS 2 h + fasciotomy 2 h; group 4, ACS 4 h + fasciotomy 2 h. For each turkey, the contralateral limb was considered the control. Time-synchronized measures of SWE and ICP from each leg were collected. Then turkeys were euthanized for histology and quantitative reverse transcription polymerase chain reaction (qRT-PCR) examination.

RESULTS

All models created reproducible increases in ICP and SWE, which had a strong linear relationship (r = 0.802, p < 0.0001) during phase 1. SWE remained stable (50.86 ± 9.64 kPa) when ICP remained at 50.28 ± 2.17 mm Hg in phase 2. After fasciotomy, SWE declined stepwise and then normalized (r = 0.737, p < 0.0001). Histologically, the myofiber diameter of group 2 (82.31 ± 22.92 μm) and group 4 (90.90 ± 20.48 μm) decreased significantly (p < 0.01) compared with that of the control group (103.1 ± 20.39 μm); the interstitial space of all groups increased significantly (p < 0.01). Multifocal muscle damage revealed neutrophilic infiltration, degeneration, hemorrhage and necrosis, especially in group 4. Quantitative RT-PCR verified that interleukin-6 and heparin-binding EGF-like growth factor were significantly increased in group 4.

CONCLUSION

SWE provided sensitive measurements correlating to ICP in a clinically relevant ACS animal model. Once ACS time was exceeded, progression to irreversible necrosis continued spontaneously, even after fasciotomy. SWE may help surgeons in the early detection, monitoring, prognosis and decision making on fasciotomy for ACS.

Correlation of shear-wave elastography stiffness and apparent diffusion coefficient values with tumor characteristics in breast cancer

We aimed to investigate the correlation between shear-wave elastography (SWE) and apparent diffusion coefficient (ADC) values in breast cancer and to identify the associated characteristics. We included 91 breast cancer patients who underwent SWE and breast MRI prior to surgery between January 2016 and November 2017. We measured the lesion's mean (Emean) and maximum (Emax) elasticities of SWE and ADC values. We evaluated the correlation between SWE, ADC values and tumor size. The mean SWE and ADC values were compared for categorical variable of the pathological/imaging characteristics. ADC values showed negative correlation with Emean (r =  - 0.315, p = 0.002) and Emax (r =  - 0.326, p = 0.002). SWE was positively correlated with tumor size (r = 0.343-0.366, p < 0.001). A higher SWE value indicated a tendency towards a higher T stage (p < 0.001). Triple-negative breast cancer showed the highest SWE values (p = 0.02). SWE were significantly higher in breast cancers with posterior enhancement, vascularity, and washout kinetics (p < 0.02). SWE stiffness and ADC values were negatively correlated in breast cancer. SWE values correlated significantly with tumor size, and were higher in triple-negative subtype and aggressive imaging characteristics.

Prediction of complete response after neoadjuvant chemotherapy for invasive breast cancers: The utility of shear wave elastography and superb microvascular imaging in pretreatment breast ultrasound

PURPOSE

To investigate whether multiparametric parameters of pretreatment breast ultrasound (US) and clinicopathologic factors are associated with pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) for breast cancer.

METHODS

Between November 2018 and September 2022, 88 patients who underwent NAC and subsequent surgery were included in this study (median age, 55 years; interquartile range [IQR], 45, 59.3). Multiparametric breast US including grayscale, shear wave elastography (SWE) and superb microvascular imaging (SMI) of pathologically proven invasive breast cancers were retrospectively reviewed. Clinicopathological and multiparametric parameters of breast US, including size, SWEmax, SWEratio and vascular index on SMI (SMIVI) were compared between the groups. Univariate and multivariate logistic regression analyses were performed to determine factors predicting pCR after NAC. AUROC curve analysis was performed to determine the predictors' optimal cut-off values and diagnostic performance.

RESULTS

The pCR group (n = 24) showed a significantly smaller tumor size, lower SWEmax, higher Ki-67 index, higher hormone receptor negativity and negative axillary lymph node metastasis compared to the non-pCR group (n = 64). Multivariate regression analysis showed that SWEmax (adjusted odds ratio[aOR] = 0.956, 95 % confidence interval [CI] = 0.919-0.994, P = 0.025) and Ki-67 index (aOR = 1.083, 95 % CI = 1.012-1.159, P = 0.021) were independently associated with pathologically complete response. The optimal cut-off values for predicting pCR were 27.5 % for Ki-67 with an AUC of 0.743 and 134.8 kPa for SWEmax with an AUC of 0.779. A combination model including clinical factors and SWEmax showed the best diagnostic performance with an AUC of 0.876.

CONCLUSION

A higher Ki-67 index and lower SWEmax measured on pretreatment breast US were independently associated with pCR in invasive breast cancer after NAC.

Breast Cancer Risk Factors among Women with Solid Breast Lesions

BACKGROUND

Breast cancer is the most frequent malignancy in women worldwide and one of the most curable cancers if diagnosed at an early stage. Female patients presenting solid breast lesions are greatly predisposed to breast cancer development, and as such, effective screening of high-risk patients is valuable in early-stage breast cancer detection.

OBJECTIVES

The aim of our study was to identify the most relevant demographic, reproductive and lifestyle risk factors for breast cancer among women with solid breast lesions living in western Romania, namely the urban region consisting of Timisoara and the rural surrounding regions.

METHODS

From January 2017 to December 2021, 1161 patients with solid breast lesions, as detected by sonoelastography, were divided into two groups: patients with benign lesions (1019, 87.77%) and patients with malignant nodules (142, 12.23%). The malignancy group was confirmed by a histopathological result. Variables including age, BMI, menarche, menopause, years of exposure to estrogen, number of births, breastfeeding period, use of oral combined contraceptives, smoker status, family medical history and living area (rural-urban) were recorded.

RESULTS

It was evidenced by our study that the main risk factors for malignancy were elevated age (OR = 1.07, 95% CI 1.05-1.08), BMI (OR = 1.06, 95% CI 1.02-1.10), living area (rural) (OR = 1.86, 95% CI 1.13-2.85) and family medical history (negative) (OR 3.13, 95% CI 1.43-8.29). The other proposed risk factors were not found to be statistically significant.

CONCLUSIONS

Age and BMI were observed to be the most significant factors for breast cancer risk increase, followed by living in a rural area. A family history of breast cancer was shown to be inversely correlated with cancer risk increase.

Development of a Multi-Parametric ultrasonography nomogram for prediction of invasiveness in ductal carcinoma in situ

OBJECTIVE

To investigate the independent risk variables associated with the potential invasiveness of ductal carcinoma in situ (DCIS) on multi-parametric ultrasonography, and further construct a nomogram for risk assessment.

METHODS

Consecutive patients from January 2017 to December 2022 who were suspected of having ductal carcinoma in situ (DCIS) based on magnetic resonance imaging or mammography were prospectively enrolled. Histopathological findings after surgical resection served as the gold standard. Grayscale ultrasound, Doppler ultrasound, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) examinations were preoperative performed. Binary logistic regression was used for multifactorial analysis to identify independent risk factors from multi-parametric ultrasonography. The correlation between independent risk factors and pathological prognostic markers was analyzed. The predictive efficacy of DCIS associated with invasiveness was assessed by logistic analysis, and a nomogram was established.

RESULTS

A total of 250 DCIS lesions were enrolled from 249 patients, comprising 85 pure DCIS and 165 DCIS with invasion (DCIS-IDC), of which 41 exhibited micro-invasion. The multivariate analysis identified independent risk factors for DCIS with invasion on multi-parametric ultrasonography, including image size (>2cm), Doppler ultrasound RI (≥0.72), SWE's Emax (≥66.4 kPa), hyper-enhancement, centripetal enhancement, increased surrounding vessel, and no contrast agent retention on CEUS. These factors correlated with histological grade, Ki-67, and human epidermal growth factor receptor 2 (HER2) (P < 0.1). The multi-parametric ultrasound approach demonstrated good predictive performance (sensitivity 89.7 %, specificity 73.8 %, AUC 0.903), surpassing single US modality or combinations with SWE or CEUS modalities. Utilizing these factors, a predictive nomogram achieved a respectable performance (AUC of 0.889) for predicting DCIS with invasion. Additionally, a separate nomogram for predicting DCIS with micro-invasion, incorporating independent risk factors such as RI (≥0.72), SWE's Emax (≥65.2 kPa), and centripetal enhancement, demonstrated an AUC of 0.867.

CONCLUSION

Multi-parametric ultrasonography demonstrates good discriminatory ability in predicting both DCIS with invasion and micro-invasion through the analysis of lesion morphology, stiffness, neovascular architecture, and perfusion. The use of a nomogram based on ultrasonographic images offers an intuitive and effective method for assessing the risk of invasion in DCIS. Although the nomogram is not currently considered a clinically applicable diagnostic tool due to its AUC being below the threshold of 0.9, further research and development are anticipated to yield positive outcomes and enhance its viability for clinical utilization.

A Model Combining Conventional Ultrasound Characteristics, Strain Elastography and Clinicopathological Features to Predict Ki-67 Expression in Small Breast Cancer

To establish a predictive model incorporating conventional ultrasound, strain elastography and clinicopathological features for Ki-67 expression in small breast cancer (SBC) which defined as maximum diameter less than2 cm. In this retrospective study, 165 SBC patients from our hospital were allocated to a high Ki-67 group (n = 104) and a low Ki-67 group (n = 61). Multivariate regression analysis was performed to identify independent indicators for developing predictive models. The area under the receiver operating characteristic (AUC) curve was also determined to establish the diagnostic performance of different predictive models. The corresponding sensitivities and specificities of different models at the cutoff value were compared. Conventional ultrasound parameters (spiculated margin, absence of posterior shadowing and Adler grade 2-3), strain elastic scores and clinicopathological information (HER2 positive) were significantly correlated with high expression of Ki-67 in SBC (all p < .05). Model 2, which incorporated conventional ultrasound features and strain elastic scores, yielded good diagnostic performance (AUC = 0.774) with better sensitivity than model 1, which only incorporated ultrasound characteristics (78.85%vs. 55.77%, p = .000), with specificities of 77.05% and 62.30% (p = .035), respectively. Model 3, which incorporated conventional ultrasound, strain elastography and clinicopathological features, yielded better performance (AUC = 0.853) than model 1 (AUC = 0.694) and model 2 (AUC = 0.774), and the specificity was higher than model 1 (86.89% vs. 77.05%, p = .001). The predictive model combining conventional ultrasound, strain elastic scores and clinicopathological features could improve the predictive performance of Ki-67 expression in SBC.

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 Force-Matched Approach to Large-Strain Nonlinearity in Elasticity Imaging for Breast Lesion Characterization

OBJECTIVE

Ultrasound elasticity imaging is a class of ultrasound techniques with applications that include the detection of malignancy in breast lesions. Although elasticity imaging traditionally assumes linear elasticity, the large strain elastic response of soft tissue is known to be nonlinear. This study evaluates the nonlinear response of breast lesions for the characterization of malignancy using force measurement and force-controlled compression during ultrasound imaging.

METHODS

54 patients were recruited for this study. A custom force-instrumented compression device was used to apply a controlled force during ultrasound imaging. Motion tracking derived strain was averaged over lesion or background ROIs and matched with compression force. The resulting force-matched strain was used for subsequent analysis and curve fitting.

RESULTS

Greater median differences between malignant and benign lesions were observed at higher compressional forces (p-value < 0.05 for compressional forces of 2-6N). Of three candidate functions, a power law function produced the best fit to the force-matched strain. A statistically significant difference in the scaling parameter of the power function between malignant and benign lesions was observed (p-value = 0.025).

CONCLUSIONS

We observed a greater separation in average lesion strain between malignant and benign lesions at large compression forces and demonstrated the characterization of this nonlinear effect using a power law model. Using this model, we were able to differentiate between malignant and benign breast lesions.

SIGNIFICANCE

With further development, the proposed method to utilize the nonlinear elastic response of breast tissue has the potential for improving non-invasive lesion characterization for potential malignancy.

Diagnostic performance of renal cortical elasticity by supersonic shear wave imaging in pediatric glomerular disease

PURPOSE

To explore the diagnostic performance of renal cortical elasticity expressed by Young's modulus (YM) using the supersonic shear wave imaging (SSI) technique in pediatric glomerular disease.

MATERIALS AND METHODS

Seventy-one children with glomerular disease confirmed by renal biopsy and sixty healthy volunteers were enrolled in this study. Conventional and SSI ultrasound examinations were performed in all individuals for both kidneys. We measured renal length, renal width, renal thickness, parenchyma thickness, interlobar arterial resistive index (RI) and the YM of the middle and lower pole.

RESULTS

Regardless of which pole and which side of the kidney, the YM in the disease group was significantly higher than that in the control group (P < 0.001). The YM of the middle pole in the left kidney demonstrated the largest AUC (0.936, P < 0.001), and the corresponding cut-off value was 15.48 kPa with a sensitivity of 87.3% and a specificity of 86.7%. There was no significant difference in the YM among different pathological types of pediatric glomerular disease in the disease group, and the same in different grades of patients with Immunoglobulin A (IgA) nephropathy by Lee classification and the Oxford Classification as well as Henoch-Schonlein purpura nephritis (HSPN) by International Study of Kidney Disease in Children (ISKDC) classification (P > 0.05). We found positive but weak correlations between the YM and renal length (r = 0.299, P = 0.001), renal width (r = 0.408, P < 0.001), renal thickness (r = 0.299, P = 0.001), and parenchyma thickness (r = 0.212, P = 0.015), whereas the YM had no significant correlations with age, sex, BMI, interlobar arterial RI, and laboratory findings (P > 0.05).

CONCLUSIONS

SSI technology is a non-invasive and feasible method for the diagnosis of pediatric glomerular disease. However, SSI did not show good performance in distinguishing different pathological types and disease grades in our study.

The value of shear wave elasticity and shear wave dispersion imaging to evaluate the viscoelasticity of renal parenchyma in children with glomerular diseases

BACKGROUND

To study the value of shear wave elasticity and shear wave dispersion imaging to evaluate the viscoelasticity of renal parenchyma in children with glomerular diseases.

METHODS

Forty-three children with glomerular diseases were prospectively evaluated by shear wave elasticity (SWE) and shear wave dispersion imaging (SWD); 43 healthy volunteers served as the control group. The shear wave velocities (SWV) and the dispersion slopes were measured at the upper, middle, and lower poles of both kidneys. The analysis of mean SWV and mean dispersion slope in control and patient groups was used to further evaluate the value of SWE and SWD in the viscoelasticity of renal parenchyma in children with glomerular disease.

RESULTS

The mean SWV in children with glomerular disease was higher than that in the healthy control group (1.61 ± 0.09 m/s vs. 1.43 ± 0.07 m/s, p < 0.001). Compared with healthy group, the mean dispersion slope in children with glomerular disease was significantly increased (13.5 ± 1.39 (m/s)/kHz vs. 12.4 ± 1.40 (m/s)/kHz, p < 0.001). Correlation analysis showed absence of correlation between the SWV and dispersion slope of occult blood, serum creatinine, 24-h urine protein, blood albumin, BMI and ROI box depth of children with glomerular disease.

CONCLUSIONS

The present study shows that it is feasible to use SWE and SWD to evaluate the difference of viscoelasticity of the renal parenchyma between healthy children and those with glomerular disease.

Correlation between Baseline Conventional Ultrasounds, Shear-Wave Elastography Indicators, and Neoadjuvant Therapy Efficacy in Triple-Negative Breast Cancer

In patients with triple-negative breast cancer (TNBC)-the subtype with the poorest prognosis among breast cancers-it is crucial to assess the response to the currently widely employed neoadjuvant treatment (NAT) approaches. This study investigates the correlation between baseline conventional ultrasound (US) and shear-wave elastography (SWE) indicators and the pathological response of TNBC following NAT, with a specific focus on assessing predictive capability in the baseline state. This retrospective analysis was conducted by extracting baseline US features and SWE parameters, categorizing patients based on postoperative pathological grading. A univariate analysis was employed to determine the relationship between ultrasound indicators and pathological reactions. Additionally, we employed a receiver operating characteristic (ROC) curve analysis and multivariate logistic regression methods to evaluate the predictive potential of the baseline US indicators. This study comprised 106 TNBC patients, with 30 (28.30%) in a nonmajor histological response (NMHR) group and 76 (71.70%) in a major histological response (MHR) group. Following the univariate analysis, we found that T staging, dmax values, volumes, margin changes, skin alterations (i.e., thickening and invasion), retromammary space invasions, and supraclavicular lymph node abnormalities were significantly associated with pathological efficacy (p < 0.05). Combining clinical information with either US or SWE independently yielded baseline predictive abilities, with AUCs of 0.816 and 0.734, respectively. Notably, the combined model demonstrated an improved AUC of 0.827, with an accuracy of 76.41%, a sensitivity of 90.47%, a specificity of 55.81%, and statistical significance (p < 0.01). The baseline US and SWE indicators for TNBC exhibited a strong relationship with NAT response, offering predictive insights before treatment initiation, to a considerable extent.

The Diagnostic Role of Shear Wave Elastography and Superb Microvascular Imaging in the Evaluation of Suspicious Microcalcifications

OBJECTIVES

The aim of this study was to investigate the role of superb microvascular imaging (SMI) and shear wave elastography (SWE) in the prediction of malignancy and invasiveness of isolated microcalcifications (MC) that can be visualized by ultrasonography (US).

MATERIAL AND METHODS

Sixty-seven women with MC, who were considered suspicious on mammography were evaluated. Only those lesions that could be visualized by US and presented as non-mass lesion were included. They were evaluated by B-mode US, SMI, and SWE before US-guided core-needle biopsy. B-mode US, SMI (vascular index (SMIvi)), and SWE (E-mean, E-ratio) findings were compared with histopathologic features.

RESULTS

Pathology confirmed 45 malignant (21 invasive and 24 in situ carcinomas) and 22 benign lesions. There was a statistically significant difference between malignant and benign groups in terms of size (P = .015), distortion (P = .028), cystic component (P < .001), E-mean (P < .001), E-ratio (P < .001), and SMIvi (P = .006). For differentiation of invasiveness E-mean (P = .002), E-ratio (P = .002), and SMIvi (P = .030) were statistically significant. According to ROC analysis E-mean (cut-off point at 38 kPa) was the most sensitive (78%) and the most specific (95%) value among four numeric parameters (size, SMI, E-mean, and E-ratio) with AUC = 0.895, PPV = 97%, and NPV = 68% in detecting malignancy. In the evaluation of invasiveness, the most sensitive (71.4%) method was SMI (cut-off point at 3.4) and the most specific (72%) method was E-mean (cut-off point at 91.5 kPa).

CONCLUSION

Our study shows that adding SWE and SMI to the sonographic evaluation of MC would be an advantage for US-guided biopsy. Including suspicious areas according to SMI and SWE in the sampling area can help target the invasive part of the lesion and avoid underestimation of core biopsy.

Improved Breast 2D SWE Algorithm to Eliminate False-Negative Cases

OBJECTIVES

Two-dimensional shear wave elastography (SWE) has been limited in breast lesion characterization due to false-negative results from artifacts. The aim of this study was to evaluate an updated Food and Drug Administration-approved breast 2D-SWE algorithm and compare with the standard algorithm (SA).

MATERIALS AND METHODS

This prospective, single-center study was approved by our local institutional review board and Health Insurance Portability and Accountability Act compliant. From April 25, 2019 to May 2, 2022, raw shear wave data were saved on patients having screening or diagnostic breast ultrasound on a Siemens Sequoia US. After removing duplicate images and those without biopsy diagnosis or stability over 2 years, there were 298 patients with 394 lesions with biopsy-proven pathology or >2-year follow-up. Raw data were processed using the SA and a new algorithm (NA). Five-millimeter regions of interest were placed in the highest stiffness in the lesion or adjacent 3 mm on the SA. Stiffness values (shear wave speed, max) in this location from both algorithms were recorded. Statistics were calculated for comparing the 2 algorithms.

RESULTS

The mean patient age was 56.3 ± 16.1 years (range, 21-93 years). The mean benign lesion size was 10.7 ± 8.0 mm (range, 2-46 mm), whereas the mean malignant lesion size was 14.9 ± 7.8 mm (range, 4-36 mm). There were 201 benign (>2-year follow-up) and 193 biopsied lesions (65 benign; 128 malignant). The mean maximum stiffness for benign lesions was 2.37 m/s (SD 1.26 m/s) for SA and 3.51 m/s (SD 2.05 m/s) for NA. For malignant lesions, the mean maximum stiffness was 4.73 m/s (SD, 1.71 m/s) for SA and 8.45 m/s (SD, 1.42 m/s) for NA. The area under the receiver operating characteristic curve was 0.87 SA and 0.95 NA when using the optimal cutoff value. Using a threshold value of 5.0 m/s for NA and comparing to SA, the sensitivity increased from 0.45 to 1.00 and the specificity decreased from 0.94 to 0.81; the positive predictive value was 0.72, the negative predictive value was 1.00, and the negative likelihood ratio was 0.00.

CONCLUSIONS

Using a new breast SWE algorithm significantly improves the sensitivity of the technique with a small decrease in specificity, virtually eliminating the "soft" cancer artifact. The new 2D-SWE algorithm significantly increases the sensitivity and negative predictive value in characterizing breast lesions as benign or malignant and allows for downgrading all BI-RADS 4 lesions.

Clinical applications of shear wave dispersion imaging for breast lesions: a pictorial essay

Shear wave dispersion (SWD) imaging is a newly developed ultrasound technology designed to evaluate the dispersion slope of shear waves, which is related to tissue viscosity. This advanced imaging technique holds potential for distinguishing malignant lesions from benign lesions and normal breast tissue. The SWD slope, as determined by shear wave elastography (SWE), could offer crucial insights into the characterization of breast lesions. This article presents SWE and SWD images of both malignant and benign breast lesions, in addition to normal breast tissue.

The use of real time strain endometrial elastosonography plus endometrial thickness and vascularization flow index to predict endometrial receptivity in IVF treatments: a pilot study

BACKGROUND

The usefulness of endometrium strain elastosonography (SE) for the evaluation of endometrial receptivity in women undergoing in vitro fertilization (IVF) remains controversial. The objective of this prospective, observational study was to evaluate the correlation between endometrial thickness (EMT) and its related strain (ESR) on the day of ovulation triggering (hCG-d) and in vitro fertilization outcomes. Additionally, 3D Power Doppler vascular indices (3DPDVI) were also analysed.

METHODS

We included all the patients undergoing fresh IVF-single blastocyst transfer cycle from January 2021 to August 2021 at our center. On hCG-d, after B-mode scanning was completed to measure the EMT, the mode was changed to elastosonography to evaluate the ESR (ratio between endometrial tissue and the myometrium below). At the end of examination, the Endometrial Volume (EV) and 3DPDVI (vascularization index [VI], flow index [FI] and vascularization flow index [VFI]), were assessed. Statistical analysis was completed using STATA MP16 software.

RESULTS

A total number of 57 women were included. Based on the EMT on hCG-d, women were divided into two groups, Group 1: <7 mm and Group 2 ≥ 7 mm. Women with EMT < 7 mm had a significantly higher ESR (p = 0.004) and lower pregnancy rate (p = 0.04). Additionally, low ESR values were correlated with high VFI values (rho = -0.8; 95% CI = -0.9- -0.6; p < 0.0001) and EMT ≥ 7 mm could be predicted by low ESR (OR = 0.01; 95% CI = 0.01-0.30; p = 0.008, area under the ROC curve: 0.70). After all, in multiple logistic regression analysis, low values of ESR (p = 0.050) and high values of EMT (p = 0.051) on hCG-d had borderline statistical effects on pregnancy rate.

CONCLUSIONS

The ESR may be useful to improve the ultrasound evaluation of the endometrial quality in infertile women candidates to IVF/ICS. Given the small sample size of our study, the usefulness of strain elastosonography in this patients, needs further investigation.

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.

Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer

RATIONALE AND OBJECTIVES

To carry out radiomics analysis/deep convolutional neural network (CNN) based on B-mode ultrasound (BUS) and shear wave elastography (SWE) to predict response to neoadjuvant chemotherapy (NAC) in breast cancer patients.

MATERIALS AND METHODS

In this prospective study, 255 breast cancer patients who received NAC between September 2016 and December 2021 were included. Radiomics models were designed using a support vector machine classifier based on US images obtained before treatment, including BUS and SWE. And CNN models also were developed using ResNet architecture. The final predictive model was developed by combining the dual-modal US and independently associated clinicopathologic characteristics. The predictive performances of the models were assessed with five-fold cross-validation.

RESULTS

Pretreatment SWE performed better than BUS in predicting the response to NAC for breast cancer for both the CNN and radiomics models (P < 0.001). The predictive results of the CNN models were significantly better than the radiomics models, with AUCs of 0.72 versus 0.69 for BUS and 0.80 versus 0.77 for SWE, respectively (P = 0.003). The CNN model based on the dual-modal US and molecular data exhibited outstanding performance in predicting NAC response, with an accuracy of 83.60% ± 2.63%, a sensitivity of 87.76% ± 6.44%, and a specificity of 77.45% ± 4.38%.

CONCLUSION

The pretreatment CNN model based on the dual-modal US and molecular data achieved excellent performance for predicting the response to chemotherapy in breast cancer. Therefore, this model has the potential to serve as a non-invasive objective biomarker to predict NAC response and aid clinicians with individual treatments.

Early Prediction of Gestational Diabetes Mellitus Using Placental Strain Elastography and Subcutaneous Adipose Tissue Thickness

AIM

The aim of this study is to investigate the efficacy of placental strain ratio values measured by real-time sonoelastography and maternal subcutaneous adipose tissue thickness measured by two-dimensional ultrasonography in predicting gestational diabetes mellitus in the first trimester.

MATERIALS AND METHODS

The population of the study consisted of the first 210 consecutive singleton pregnant women who applied for routine first-trimester screening between the 11th and 14th week of gestation.B-mode subcutaneous adipose tissue thickness sonography and real-time placental strain elastography scanning were performed.All patients underwent 75 g oral glucose tolerance test between the 24th and 28th week of gestation. American Diabetes Association criteria were used in the diagnosis of gestational diabetes mellitus.

RESULTS

It was observed that body mass index (p<0.001), first-trimester fasting blood sugar (p<0.001), subcutaneous adipose tissue thickness (p<0.001), and placental strain ratio value (p<0.001) affected the development of gestational diabetes mellitus statistically. The multivariate analysis shown that subcutaneous adipose tissue thickness (odds ratio=1.271, 95% CI=1.142-1.416, p<0.001) and placental strain ratio value (odds ratio=3.664, 95% CI=1.927-6.969, p<0.001) were the independent risk factors affecting the development of gestational diabetes mellitus.

CONCLUSIONS

The findings of this study indicated a positive correlation between 75 g oral glucose tolerance test application and first trimester subcutaneous adipose tissue thickness and placental strain ratio. Using the cut-off values of>11.5 mm for subcutaneous adipose tissue and>0.986 for placental strain ratio, the development of gestational diabetes mellitus may be predicted in the early weeks of pregnancy.

Evaluation of Tumor Fibrosis in Pancreatic Ductal Adenocarcinoma by 2-D Shear Wave Elastography: A Pilot Study

OBJECTIVE

The aim of the work described here was to investigate the association of the stromal proportion with the elasticity obtained by 2-D shear wave elastography (SWE) and the diagnostic value of elasticity in evaluating tumor stromal fibrosis in pancreatic ductal adenocarcinoma (PDAC).

METHODS

Patients who met inclusion criteria underwent pre-operative 2-D SWE examination and intra-operative determination of hardness by palpation from July 2021 to November 2022, and the post-operative specimens were used to evaluate pathological features including the tumor stromal proportion. A receiver operating characteristic curve was created to evaluate its diagnostic value in differentiating the degree of tumor stromal fibrosis.

RESULTS

The 2-D SWE measurements in pancreatic lesions were successful in 62 of 69 patients (89.9%). A total of 52 eligible participants were enrolled for subsequent correlation analysis. Elasticity correlated well with tumor stromal proportion (r_s = 0.646) and number of tumor cells (r_s = -0.585) in PDAC. Moreover, pancreatic elasticity determined by 2-D SWE, palpation-determined hardness and tumor stromal proportion were well correlated with each other. Two-dimensional SWE could clearly distinguish mild and severe stromal fibrosis, and its diagnostic performance was better than that determined by palpation even though the difference was not statistically significant (p = 0.103).

CONCLUSION

The elasticity of PDAC obtained using 2-D SWE was closely related to stromal proportion and tumor cellularity and could clearly be used to diagnose the degree of stromal fibrosis, which indicates that 2-D SWE can be a non-invasive predictive imaging biomarker in personalization of therapy and monitoring of treatment.

Peritumoural Strain Elastography of Newly Diagnosed Breast Tumours: Does Maximum Peritumoural Halo Depth Correlate with Tumour Differentiation and Grade?

To evaluate the diagnostic utility of the maximum ultrasound strain elastography (SE) halo depth in newly diagnosed and histologically confirmed breast lesions, a retrospective study approval was granted by the local Ethical Review Board. Overall, the maximum strain elastography peritumoural halos (SEPHmax)-the maximum distance between the SE stiffening area and the B-mode lesion size-in 428 cases with newly diagnosed breast lesions were retrospectively analysed alongside patient age, affected quadrant, tumour echogenicity, size, acoustic shadowing, and vascularity. Statistical analysis included an ordinary one-way ANOVA to compare the SEPHmax between BI-RADS 2, 3, and 5 groups and between tumour grades 1, 2, and 3. A binary regression analysis was used to determine the correlation between tumour malignancy and the above-mentioned demographic and imaging factors. SEPHmax was significantly higher in BI-RADS 5 tumours (5.5 ± 3.9 mm) compared to BI-RADS 3 (0.9 ± 1.7 mm, p < 0.0001) and 2 (0.6 ± 1.4 mm, p < 0.0001). The receiver operating characteristic area under the curve was 0.933 for the detection of BI-RADS 5 lesions. Furthermore, tumour grades 2 (5.6 ± 3.6 mm, p = 0.001) and 3 (6.8 ± 4.2 mm, p < 0.0001) exhibited significantly higher SEPHmax than grade 1 tumours (4.0 ± 3.9 mm). Similarly, St. Gallen Ki67-stratified low-risk (p = 0.005) and intermediate-risk (p = 0.013) tumours showed smaller SEPHmax than high-risk tumours. Multivariate analysis revealed a significant correlation between malignant differentiation and SEPHmax (standardized regression coefficient 3.17 [95% confidence interval (CI) 2.42-3.92], p < 0.0001), low tumour echogenicity (1.68 [95% CI 0.41-3.00], p = 0.03), and higher patient age (0.89 [95% CI 0.52-1.26], p < 0.0001). High SEPHmax is a strong predictor for tumour malignancy and a higher tumour grade and can be used to improve tumour characterisation before histopathological evaluation. It may also enable radiologists to identify lesions warranting observation rather than immediate biopsy.

Diagnostic value of multimodal ultrasound strategies in the differentiation of non-mass-like breast lesions

PURPOSE

The study aims to assess the diagnostic performance of convention ultrasound (US), Angio PLUS microvascular US imaging (AP), and shear-wave elastography (SWE) in differentiating malignant and benign non-mass-like (NML) breast lesions.

METHODS

Sixty patients aged 21-70 years with 60 NML lesions were recruited. All patients were examined by conventional US, AP, and SWE. According to the pathological results, the performances of the multimodal US strategies were analyzed, while the diagnostic efficiency of AP and SWE in serial and parallel was also explored.

RESULTS

Age, together with posterior features, microcalcification, and architectural distortion were considered significant in evaluating NML lesions. The sensitivity, specificity, PPV, NPV, and accuracy of AP combined SWE in serial were 72.7, 96.3, 96.0, 74.3, and 83.3%, while those in parallel were 90.9, 63.0, 75.0, 85.0, and 78.3%, respectively. The two in serial indicated the highest specificity, PPV, accuracy, and AUC value, which could increase the true positive rate and reduce the chance of misdiagnosis, while the two in parallel exhibited the best sensitivity and NPV, which might serve as an effective tool to avoid excessive or nonessential biopsy.

CONCLUSION

The multimodal US strategies could provide precise and reliable diagnostic results for NML breast lesions.

A multiparametric clinic-ultrasomics nomogram for predicting extremity soft-tissue tumor malignancy: a combined retrospective and prospective bicentric study

OBJECTIVE

We aimed at building and testing a multiparametric clinic-ultrasomics nomogram for prediction of malignant extremity soft-tissue tumors (ESTTs).

MATERIALS AND METHODS

This combined retrospective and prospective bicentric study assessed the performance of the multiparametric clinic-ultrasomics nomogram to predict the malignancy of ESTTs, when compared with a conventional clinic-radiologic nomogram. A dataset of grayscale ultrasound (US), color Doppler flow imaging (CDFI), and elastography images for 209 ESTTs were retrospectively enrolled from one hospital, and divided into the training and validation cohorts. A multiparametric ultrasomics signature was built based on multimodal ultrasomic features extracted from the grayscale US, CDFI, and elastography images of ESTTs in the training cohort. Another conventional radiologic score was built based on multimodal US features as interpreted by two experienced radiologists. Two nomograms that integrated clinical risk factors and the multiparameter ultrasomics signature or conventional radiologic score were respectively developed. Performance of the two nomograms was validated in the retrospective validation cohort, and tested in a prospective dataset of 51 ESTTs from the second hospital.

RESULTS

The multiparametric ultrasomics signature was built based on seven grayscale ultrasomic features, three CDFI ultrasomic features, and one elastography ultrasomic feature. The conventional radiologic score was built based on five multimodal US characteristics. Predictive performance of the multiparametric clinic-ultrasomics nomogram was superior to that of the conventional clinic-radiologic nomogram in the training (area under the receiver operating characteristic curve [AUC] 0.970 vs. 0.890, p = 0.006), validation (AUC: 0.946 vs. 0.828, p = 0.047) and test (AUC: 0.934 vs. 0.842, p = 0.040) cohorts, respectively. Decision curve analysis of combined training, validation and test cohorts revealed that the multiparametric clinic-ultrasomics nomogram had a higher overall net benefit than the conventional clinic-radiologic model.

CONCLUSION

The multiparametric clinic-ultrasomics nomogram can accurately predict the malignancy of ESTTs.

Shear Wave Elastography for Assessment of Biopsy-Proven Renal Fibrosis: A Systematic Review and Meta-analysis

The purpose of this meta-analysis was to evaluate the diagnostic performance of shear wave elastography (SWE) for the staging of renal fibrosis in patients with chronic kidney disease (CKD). Classification of CKD into mild, moderate and severe fibrosis was based on renal biopsy pathology (glomerulosclerosis, tubulointerstitial injury and vascular sclerosis). The Cochrane Library, Medline, PubMed, Web of Science, EMBASE and CNKI databases were searched from January 1, 2009, to April 20, 2022. Pooled sensitivity, specificity, diagnostic odds ratio and area under the receiver operating characteristic curve (AUROC) were calculated using random effects models. A total of 1394 patients from 14 studies were included in the final analysis. For mild, moderate and severe renal fibrosis, SWE had a sensitivity of 0.79 (95% confidence interval [CI]: 0.67-0.88), 0.73 (95% CI: 0.65-0.80) and 0.87 (95% CI: 0.71-0.95); a specificity of 0.82 (95% CI: 0.75-0.87), 72% (95% CI: 0.67-0.77) and 0.83 (95% CI: 0.80-0.86); an AUROC of 0.87 (95% CI: 0.84-0.90), 0.78 (95% CI: 0.75-0.82) and 0.86 (95% CI: 0.82-0.88); and a diagnostic odds ratio of 17 (95% CI: 7-43), 7 (95% CI: 4-12) and 34 (95% CI: 13-88), respectively. Meta-regressions revealed that the publication date, system used and number of valid measurements of SWE were the main causes of heterogeneity. SWE is a good technique for diagnosing mild and severe renal fibrosis, as well as a fair technique for diagnosing moderate fibrosis.

Photoacoustic remote sensing elastography

The mechanical properties of organisms are important indicators for clinical disputes and disease monitoring, yet most existing elastography techniques are based on contact measurements, which are limited in many application scenarios. Photoacoustic remote sensing elastography (PARSE) is the first, to the best of our knowledge, elastography modality based on acoustic pressure monitoring, where elastic contrast information is obtained by using an all-optical non-contact and non-coherent intensity monitoring method through the time-response properties of laser-induced photoacoustic pressure. To validate PARSE, sections of different elastic organs were measured and this modality was applied to differentiate between bronchial cartilage and soft tissue to confirm the validity of the elasticity evaluation. PARSE, through a mathematical derivation process, has a 9.5-times greater distinction detection capability than photoacoustic remote sensing (PARS) imaging in stained bronchial sections, expands the scope of conventional PARS imaging, and has potential to become an important complementary imaging modality.

Evaluating Variability of Commercial Liver Fibrosis Elastography Phantoms

OBJECTIVE

Liver fibrosis has been found to increase the mechanical stiffness of the liver. To mimic different stages of liver fibrosis, commercially available phantoms (Model 039, CIRS, Inc.) have been produced for clinical quality assurance and research purposes. The purpose of this study was to investigate the mechanical property variability of the phantoms in two lots of CIRS Model 039 phantoms.

METHODS

Each lot consisted of phantoms of four stiffness types, and there were 8-10 phantoms of each type. Shear wave elastography measurements were conducted on each phantom at 10 different angles. Group velocity measurements and phase velocity curves were calculated for every SWE acquisition. Multilevel functional principal component analysis (MFPCA) was performed on phase velocity data, which decomposes each phase velocity curve into the sum of eigenfunctions of two levels. The variance of the component scores of levels 1 and 2 were used to represent inter-phantom and intra-phantom variability, respectively. The 95% confidence intervals of phase velocity in a phantom type were calculated to reflect curve variability.

DISCUSSION

The standard deviations of the group velocity for phantoms of any type were less than 0.04 and 0.02 m/s for lots 1 and 2, respectively. For both lots, in every type, the phase velocity curves of most individual phantoms fall within the 95% confidence interval.

CONCLUSION

MFPCA is an effective tool for analyzing the inter- and intra-phantom variability of phase velocity curves. Given the known variability of a fully tested lot, estimation of the variability of a new lot can be performed with a reduced number of phantoms tested.

The Role of a Deep Learning-Based Computer-Aided Diagnosis System and Elastography in Reducing Unnecessary Breast Lesion Biopsies

OBJECTIVES

Ultrasound examination has inter-observer and intra-observer variability and a high false-positive rate. The aim of this study was to evaluate the value of the combined use of a deep learning-based computer-aided diagnosis (CAD) system and ultrasound elastography with conventional ultrasound (US) in increasing specificity and reducing unnecessary breast lesions biopsies.

MATERIALS AND METHODS

Conventional US, CAD system, and strain elastography (SE) were retrospectively performed on 216 breast lesions before biopsy or surgery. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and biopsy rate were compared between conventional US and the combination of conventional US, SE, and CAD system.

RESULTS

Of 216 lesions, 54 were malignant and 162 were benign. The addition of CAD system and SE to conventional US increased the AUC from 0.716 to 0.910 and specificity from 46.9% to 85.8% without a loss in sensitivity while 89.2% (66 of 74) of benign lesions in Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions would avoid unnecessary biopsies.

CONCLUSION

The addition of CAD system and SE to conventional US improved specificity and AUC without loss of sensitivity, and reduced unnecessary biopsies.

Correlation of androgen receptor with ultrasound, clinicopathological features and clinical outcomes in breast cancer

BACKGROUND

This study aimed to explore whether there is an association between androgen receptor (AR) expression and ultrasound, clinicopathological features and prognosis of breast cancer.

METHODS

A total of 141 breast cancer patients were included in this retrospective study. AR expression was analyzed by immunohistochemistry. The images of B-mode, color Doppler and strain elastography from 104 patients were collected continuously, and the corresponding ultrasound characteristics were obtained. The differences in ultrasound and clinicopathological features in different AR status were analyzed. Progression-free survival (PFS) of patients was obtained through up to 90 months of follow-up; then, the effect of AR on PFS was analyzed. Subsequently, a nomogram was constructed to predict the AR status. The predictive accuracy was calculated using C-index.

RESULTS

The positive expression of AR (AR +) was associated with lower histological grade (p = 0.034) and lower Ki-67 level (p = 0.029). Triple-negative breast cancer (TNBC) had the lowest probability of AR + (p < 0.001). The AR + group mostly showed unsmooth margin (p < 0.001), posterior acoustic shadowing (p = 0.002) and higher elasticity score (p = 0.022) on ultrasound. The echo pattern of most tumors with AR + was heterogeneous (p = 0.024) in Luminal A subtype. AR + could be a sign of a better prognosis in overall breast cancer (p < 0.001), as well as in human epidermal growth factor receptor 2 (HER2) overexpression and Luminal B subtypes (p = 0.001 and 0.025). The nomogram showed relatively reliable performance with a C-index of 0.799.

CONCLUSION

Our research demonstrated that AR expression was closely related to ultrasound, clinicopathological features and prognosis of breast cancer.

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.

Measurement of the stiffness of the normal terminal ileum mesentery using shear-wave elastography

OBJECTIVE

To investigate the feasibility of using shear-wave elastography (SWE) to measure the stiffness of the normal terminal ileum mesentery, and to establish its normal reference range.

METHODS

Ninety-five normal subjects and 22 patients with mesentery-related disease were included. The average Young's modulus of the normal terminal ileal mesentery was measured by SWE ultrasound. The thickness and the extent to which mesenteric fat extended around the intestinal circumference of the normal terminal ileum were also recorded. The normal reference range was established and the SWE values of normal and diseased subjects were compared.

RESULTS

Transabdominal SWE examination of the terminal ileum mesentery was successfully performed on 91 subjects (95.8 %). The mean extent range, thickness, and SWE value of the normal terminal ileum mesentery were 1/4 (1/5-1/3), 6.8 ± 2.4 mm, and 4.3 ± 2.1 kPa, respectively. These parameters did not differ significantly between genders, and across age and body mass index groups (all P > 0.05). The intra- and inter-operator consistencies were excellent for the replicated SWE measurements (0.801 [95 % confidence interval: 0.560-0.916] and 0.751 (95 % confidence interval: 0.388-0.900], respectively). The mean mesenteric elasticity in diseased subjects was 21.9 ± 10.7 kPa, which was significantly higher than that in normal subjects (P < 0.001). The cut-off value for mesenteric elasticity was 9.3 kPa, with a sensitivity of 90 % and a specificity of 100 % (P < 0.001).

CONCLUSION

SWE can be used to reliably evaluate the stiffness of the terminal ileum mesentery in normal subjects.

Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis

Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing a deep neural network to synthesize virtual EUS (V-EUS) from conventional B-mode images. A total of 4580 breast tumor cases were collected from 15 medical centers, including a main cohort with 2501 cases for model establishment, an external dataset with 1730 cases and a portable dataset with 349 cases for testing. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5% after V-EUS is equipped.

Assessment of ultrasound shear wave elastography: An animal ex-vivo study

OBJECTIVES

To explore the influence of the surrounding environment of the target tissue, lesion size, and rectangular sampling box size on shear wave speed (SWS).

METHODS

The tendon SWS was acquired ex-vivo. Then the tendons were dissected and buried in the couplant (gel) and evaluated by two-dimensional shear wave elastography (2D-SWE). Finally, the tendons were placed in the isolated muscles to simulate the intramuscular lesions, and their elasticity was tested under two rectangular sampling box conditions. The isolated complete liver SWS was acquired. Similarly, the large and small pieces of livers were cut out, placed in the muscles, and assessed by SWE under two rectangular sampling box conditions. The SWS acquired under different conditions was compared. Variability was evaluated using the coefficient of variation (CV). The intraclass correlation coefficient (ICC) was used to evaluate repeatability.

RESULTS

The SWS of the tendons ex-vivo, buried in the couplant and placed in the isolated muscles showed significant differences (p < 0.001). The ex-vivo condition produced the highest SWS and CV values. There were significant differences in SWS of livers with different sizes placed in muscles (p < 0.001). The highest SWS value was associated with small pieces of livers. No significant difference was found in SWS acquired under different rectangular box sizes (p > 0.05).

CONCLUSIONS

Under the present study conditions, the surrounding environment of the target tissue makes a big difference to lesion SWS values. The lesion size will affect the assessment of its inherent elasticity. The size of the sampling frame has no significant effect on the tissue SWS.

Elastography Assisted BI-RADS in the Preoperative Breast Magnetic Resonance Imaging 4a Lesions in China

OBJECTIVES

A considerable number of benign lesions, especially category 4a lesions on Breast Imaging Reporting and Data System Magnetic Resonance Imaging (BI-RADS-MRI), were biopsied according to BI-RADS-MRI, which was a diagnostic imaging challenge. This study aimed to evaluate the diagnostic performance of ultrasound elastography (UE) assisted Breast Imaging Reporting and Data System (BI-RADS) for BI-RADS-MRI category 4a lesions.

METHODS

Between January 2017 and December 2019, 228 breast lesions categorized as BI-RADS-MRI 4a were included. Conventional ultrasound (US) and UE were performed to evaluate each lesion. Pathology results were used as the gold standard. The diagnostic performances of different UE methods and our re-assessment proposal were evaluated.

RESULTS

When BI-RADS-MRI category 4a, BI-RADS-US category 3-4a, the stiffness of soft or intermediate in elasticity assessment according to the fifth edition of the BI-RADS atlas, strain ratio < 1.335, age ≤ 52 years, and the maximum diameter of lesion ≤20 mm were simultaneously met, an ultrasound-guided empty needle biopsy was not recommended, but short-term ultrasound follow-up for 3-6 months was recommended, and biopsy was performed after changes in evaluation. In this way, 95 of 228 BI-RADS-MRI category 4a lesions avoided biopsies, and the number of patients with biopsies decreased by 41.7%.

CONCLUSIONS

UE offers benefits in the characterization of BI-RADS-MRI category 4a lesions. Ultrasound and elastography can help optimize therapy recommendations for BI-RADS-MRI category 4a lesions by our re-assessment proposal.

Ultrasound shear wave elastography for patients with sialolithiasis undergoing interventional sialendoscopy

Objective

Ultrasound shear wave elastography is an objective tool to evaluate the stiffness of human tissues. Patients with sialolithiasis could be treated by interventional sialendoscopy with a high success rate. Sialolithiasis could be extracted, and the diseased gland could be preserved and evaluated after treatment. Whether ultrasound shear wave elastography could be used for objective outcome measurement and short-term follow-up of the parenchyma of gland in patients with sialolithiasis remains unclear.

Methods

This retrospective self-controlled study was conducted. Patients with sialolithiasis treated by interventional sialendoscopy and followed by high-resolution ultrasound shear wave elastography were selected between January and September 2017.

Results

Seventeen patients with sialolithiasis (mean age: 39.63 ± 12.49 years), including 10 women and 7 men, were enrolled. Fifteen patients had sialolithiasis in the submandibular gland and two in the parotid gland. The preoperative value of shear wave velocity was significantly higher in the diseased gland than in the contralateral normal gland (p < .001; 95% confidence interval [CI], 0.3915-0.6046). After successful treatment by interventional sialendoscopy surgery, the shear wave velocity of the diseased gland decreased significantly (p = 0.001; 95% CI, -0.38792 to -0.20474). However, there was a significant difference between the diseased and contralateral normal glands (p = 0.001; 95% CI, 0.0423-0.2895) after 1.55 months of surgery.

Conclusion

Ultrasound shear wave elastography could be an adjuvant tool to distinguish sialolithiasis-affected diseased glands from contralateral normal glands and assess the short-term treatment outcome objectively. The changing trend of shear wave velocity could help monitor the healing process of the parenchyma in the diseased gland after treatment.

Level of Evidence

4.

Prediction of Molecular Subtypes Using Superb Microvascular Imaging and Shear Wave Elastography in Invasive Breast Carcinomas

RATIONALE AND OBJECTIVES

To investigate the efficacy of the advanced imaging methods, superb microvascular imaging (SMI) and shear wave elastography (SWE) in predicting molecular subtypes in invasive breast carcinomas.

MATERIALS AND METHODS

A total of 210 biopsy-proven breast carcinomas in 200 patients who underwent ultrasound (US) imaging with SMI and SWE were included in this study. Quantitative analyses were performed using mean elasticity (E_mean) score by SWE and vascular index (VI) by SMI. For qualitative assessment of microvascularity, first, lesions were graded according to Adler's classification in four types. Then, a new morphological model was used to classify the microvascular architecture into six patterns: type one, no signal; type two, penetrant; type three, rim-like; type four, dot-like/linear/regional; type five, wheel-like and type six, irregular signals. The correlation between these variables and molecular subtypes, nuclear grade, the Ki-67 levels and axillary status was investigated.

RESULTS

The average VI and E_mean values were relatively higher in non-luminal subtypes (VI, p = 0.002; E_mean, p > 0.05). The two microvascularisation models were significantly able to differentiate the molecular subtypes according to the Kruskal Wallis test (p < 0.05). Rim-like, penetrant and regional patterns were primarily observed in luminal subtypes. The dominant pattern in non-luminal subtypes was wheel-like pattern. VI, E_mean, Adler's classification and morphological vascularisation model were not significantly correlated with the nuclear grade, Ki-67 index or axillary status.

CONCLUSION

The proposed microvascular categorization model may be more valuable in predicting molecular subtypes of breast carcinomas compared to VI and E_mean and may contribute to the management of breast carcinomas as a non-invasive variable.

Quantitative Assessment of Breast-Tumor Stiffness Using Shear-Wave Elastography Histograms

Purpose: Shear-wave elastography (SWE) measures tissue elasticity using ultrasound waves. This study proposes a histogram-based SWE analysis to improve breast malignancy detection. Methods: N = 22/32 (patients/tumors) benign and n = 51/64 malignant breast tumors with histological ground truth. Colored SWE heatmaps were adjusted to a 0−180 kPa scale. Normalized, 250-binned RGB histograms were used as image descriptors based on skewness and area under curve (AUC). The histogram method was compared to conventional SWE metrics, such as (1) the qualitative 5-point scale classification and (2) average stiffness (SWEavg)/maximal tumor stiffness (SWEmax) within the tumor B-mode boundaries. Results: The SWEavg and SWEmax did not discriminate malignant lesions in this database, p > 0.05, rank sum test. RGB histograms, however, differed between malignant and benign tumors, p < 0.001, Kolmogorov−Smirnoff test. The AUC analysis of histograms revealed the reduction of soft-tissue components as a significant SWE biomarker (p = 0.03, rank sum). The diagnostic accuracy of the suggested method is still low (Se = 0.30 for Se = 0.90) and a subject for improvement in future studies. Conclusions: Histogram-based SWE quantitation improved the diagnostic accuracy for malignancy compared to conventional average SWE metrics. The sensitivity is a subject for improvement in future studies.

Intelligent multi-modal shear wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002): a retrospective, international, multicentre analysis

BACKGROUND

Breast ultrasound identifies additional carcinomas not detected in mammography but has a higher rate of false-positive findings. We evaluated whether use of intelligent multi-modal shear wave elastography (SWE) can reduce the number of unnecessary biopsies without impairing the breast cancer detection rate.

METHODS

We trained, tested, and validated machine learning algorithms using SWE, clinical, and patient information to classify breast masses. We used data from 857 women who underwent B-mode breast ultrasound, SWE, and subsequent histopathologic evaluation at 12 study sites in seven countries from 2016 to 2019. Algorithms were trained and tested on data from 11 of the 12 sites and externally validated using the additional site's data. We compared findings to the histopathologic evaluation and compared the diagnostic performance between B-mode breast ultrasound, traditional SWE, and intelligent multi-modal SWE.

RESULTS

In the external validation set (n = 285), intelligent multi-modal SWE showed a sensitivity of 100% (95% CI, 97.1-100%, 126 of 126), a specificity of 50.3% (95% CI, 42.3-58.3%, 80 of 159), and an area under the curve of 0.93 (95% CI, 0.90-0.96). Diagnostic performance was significantly higher compared to traditional SWE and B-mode breast ultrasound (P < 0.001). Unlike traditional SWE, positive-predictive values of intelligent multi-modal SWE were significantly higher compared to B-mode breast ultrasound. Unnecessary biopsies were reduced by 50.3% (79 versus 159, P < 0.001) without missing cancer compared to B-mode ultrasound.

CONCLUSION

The majority of unnecessary breast biopsies might be safely avoided by using intelligent multi-modal SWE. These results may be helpful to reduce diagnostic burden for patients, providers, and healthcare systems.

Clinical use and adjustment of ultrasound elastography for breast lesions followed WFUMB guidelines and recommendations in the real world

Objective

This study aimed to explore the value of strain elastography (SE) and shear wave elastography (SWE) following the World Federation of Ultrasound in Medicine and Biology (WFUMB) guidelines and recommendations in the real world in distinguishing benign and malignant breast lesions and reducing biopsy of BI-RADS (Breast Imaging Reporting and Data System) 4a lesions.

Methods

This prospective study included 274 breast lesions. The elastography score (ES) by the Tsukuba score, the strain ratio (SR) for SE, and Emax for SWE of the lesion(A) and the regions(A') included the lesion and the margin (0.5-5 mm) surrounding the lesion were measured. The sensitivity, specificity, and AUC were calculated and compared by the cutoff values recommended by WFUMB guidelines.

Results

When scores of 1 to 3 were classified as probably benign by WFUMB recommendation, the ES was significantly higher in malignant lesions compared to benign lesions (p < 0.05) in all lesions. For the cohort by size >20 mm, the sensitivity was 100%, and the specificity was 45.5%. ES had the highest AUC: 0.79(95% CI 0.72-0.86) with a sensitivity of 96.2%, and a specificity of 61.8% for the cohort by size ≤20 mm. For the Emax-A'-S2.5mm, when the high stiffness would be considered with Emax above 80 kPa in SWE, the malignant lesions were diagnosed with a sensitivity of 95.8%, a specificity of 43.3% for all lesions, a sensitivity of 88.5% for lesions with size ≤20 mm, and sensitivity of 100.0% for lesions with size >20 mm. In 84 lesions of BI-RADS category 4a, if category 4a lesions with ES of 1-3 points or Emax-A'-S2.5 less than 80 kPa could be downgraded to category 3, 52 (61.9%) lesions could be no biopsy, including two malignancies. If category 4a lesions with ES of 1-3 points and Emax-A'-S2.5 less than 80kPa could be downgraded to category 3, 23 (27.4%) lesions could be no biopsy, with no malignancy.

Conclusions

The elastography score for SE and Emax-A' for SWE after our modification were beneficial in the diagnosis of breast cancer. The combination of SWE and SE could effectively reduce the biopsy rate of BI-RADS category 4a lesions.

An AI model of sonographer’s evaluation+ S-Detect + elastography + clinical information improves the preoperative identification of benign and malignant breast masses

Purpose

The purpose of the study was to build an AI model with selected preoperative clinical features to further improve the accuracy of the assessment of benign and malignant breast nodules.

Methods

Patients who underwent ultrasound, strain elastography, and S-Detect before ultrasound-guided biopsy or surgical excision were enrolled. The diagnosis model was built using a logistic regression model. The diagnostic performances of different models were evaluated and compared.

Results

A total of 179 lesions (101 benign and 78 malignant) were included. The whole dataset consisted of a training set (145 patients) and an independent test set (34 patients). The AI models constructed based on clinical features, ultrasound features, and strain elastography to predict and classify benign and malignant breast nodules had ROC AUCs of 0.87, 0.81, and 0.79 in the test set. The AUCs of the sonographer and S-Detect were 0.75 and 0.82, respectively, in the test set. The AUC of the combined AI model with the best performance was 0.89 in the test set. The combined AI model showed a better specificity of 0.92 than the other models. The sonographer’s assessment showed better sensitivity (0.97 in the test set).

Conclusion

The combined AI model could improve the preoperative identification of benign and malignant breast masses and may reduce unnecessary breast biopsies.

Semiautomated Software to Improve Stability and Reduce Operator-Induced Variation in Vascular Ultrasound Speckle Tracking

OBJECTIVES

Ultrasound is useful in predicting arteriovenous fistula (AVF) maturation, which is essential for hemodialysis in end-stage renal disease patients. We developed ultrasound software that measures circumferential vessel wall strain (distensibility) using conventional ultrasound Digital Imaging and Communications in Medicine (DICOM) data. We evaluated user-induced variability in measurement of arterial wall distensibility and upon finding considerable variation we developed and tested 2 methods for semiautomated measurement.

METHODS

Ultrasound scanning of arteries of 10 subjects scheduled for AVF surgery were performed. The top and bottom of the vessel wall were tracked using the Kanade-Lucas-Tomasi (KLT) feature-tracking algorithm over the stack of images in the DICOM cine loops. The wall distensibility was calculated from the change of vessel diameter over time. Two semiautomated methods were used for comparison.

RESULTS

The location of points selected by users for the cine loops varied significantly, with a maximum spread of up to 120 pixels (7.8 mm) for the top and up to 140 pixels (9.1 mm) for the bottom of the vessel wall. This variation in users' point selection contributed to the variation in distensibility measurements (ranging from 5.63 to 41.04%). Both semiautomated methods substantially reduced variation and were highly correlated with the median distensibility values obtained by the 10 users.

CONCLUSIONS

Minimizing user-induced variation by standardizing point selection will increase reproducibility and reliability of distensibility measurements. Our recent semiautomated software may help expand use in clinical studies to better understand the role of vascular wall compliance in predicting the maturation of fistulas.