Combined use of strain elastography and superb microvascular imaging with grayscale ultrasound according to the BI-RADS classification for differentiating benign from malignant solid breast masses

Role of Combining Grayscale Findings With Superb Microvascular Imaging and Shear Wave Elastography in Standardization and Management of NON-MASS Breast Lesions

ABSTRACT

The non-mass breast lesions on ultrasound (US) are a group of challenging pathology. We aimed to standardize these grayscale findings and investigate the effectiveness of superb microvascular imaging (SMI) and shear wave elastography (SWE). A total of 195 lesions were evaluated by B-mode US, SWE, and SMI in the same session. A "NON-MASS model" was built on grayscale US to group the lesions only as areas and those with associated features: microcalcifications, architectural distortion, ductal changes, and microcysts. The mean stiffness parameters Emean, Eratio, and mean vascular index (VI) were recorded following consecutive measurements. Besides, the microvascularity was graded based on Adler's classification (grades 0 to 3). Lesions were divided into 3 groups: benign, category B3, and malignant. One hundred twelve (57.4%) lesions were benign, 23 (11.8%) were B3, and 60 were (30.8%) in the malignant category. Thirty-eight (19.5%) lesions were observed only as an area, whereas associated features were present in 157 lesions (80.5%). Distortion was the only associated feature predicting malignancy among the grayscale findings (P < 0.001). There was a significant difference between malignant and nonmalignant (benign and B3) groups in terms of Adler's grade, Emean, Eratio, and VI values (P < 0.001). Sensitivity, specificity, and accuracy increased when advanced imaging parameters were added to grayscale findings (P < 0.001). In the presence of microcalcifications, architectural distortion, high elasticity, and hypervascularity in the "NON-MASS" imaging model, the suspicion of malignancy increases. The non-mass findings and advanced imaging techniques have the potential to find greater coverage in the following versions of BI-RADS atlas.

Breast Imaging Reporting and Data System evaluation of breast lesions improved with virtual touch tissue imaging average grayscale values

BACKGROUND

Early breast cancer diagnosis is of great clinical importance for selecting treatment options, improving prognosis, and enhancing the quality of patients' survival.

OBJECTIVE

We investigated the value of virtual touch tissue imaging average grayscale values (VAGV) helper Breast Imaging Reporting and Data System (BI-RADS) in diagnosing breast malignancy.

METHODS

We retrospectively analyzed 141 breast tumors in 134 patients. All breast lesions were diagnosed pathologically by biopsy or surgical excision. All patients first underwent conventional ultrasound (US) followed by virtual touch tissue imaging (VTI). The measurement of the VAGV of the lesion was performed by Image J software. BI-RADS classification was performed for each lesion according to the US. We performed a two-by-two comparison of the diagnostic values of VAGV, BI-RADS, and BI-RADS+VAGV.

RESULTS

VAGV was lower in malignant tumors than in benign ones (35.82 ± 13.39 versus 73.58 ± 42.69, P< 0.001). The area under the receiver operating characteristic curve (AUC) value, sensitivity, and specificity of VAGV was 0.834, 84.09%, and 69.07%, respectively. Among BI-RADS, VAGV, and BI-RADS+VAGV, BI-RADS+VAGV had the highest AUC (0.926 versus 0.882, P= 0.0066; 0.926 versus 0.834, P= 0.0012). There was perfect agreement between the two radiologists using VAGV (ICC= 0.9796) and substantial agreement using BI-RADS (Kappa= 0.725).

CONCLUSION

Our study shows that VAGV can accurately diagnose breast cancer. VAGV effectively improves the diagnostic performance of BI-RADS.

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

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

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.

Is there a potential biomarker in distinguishing invasive breast carcinomas with elevated Ki-67 levels among 2D-SWE, SMI, and 18F-FDG PET-CT/MRI-derived parameters?: A single-center data

PURPOSE

To investigate the strength of quantitative imaging and metabolic parameters in differentiating invasive breast carcinomas with elevated Ki-67 levels.

MATERIALS AND METHODS

A total of 123 patients with 129 breast lesions confirmed as invasive breast carcinoma underwent shear wave elastography (SWE), superb microvascular imaging (SMI) and positron emission tomography (PET)/CT or MRI. Adler's grade (classifying the microvascularity into four types) and Vascular Index (VI) was obtained by SMI as microvascular parameters. In addition, the stiffness value (E_mean ) was evaluated in kilopascal by SWE. The average of consecutive measurements was recorded as mean VI and mean E_mean . PET scan parameters were obtained as SUV_max and SUL_peak . Lesions were divided into two groups according to the Ki-67 expression, low as ≤ 14 and high as >14.

RESULTS

Adler's grading was the most correlated imaging parameter with high Ki-67 expression (p < 0.05), while VI and E_mean had poor correlation (p > 0.05). SUV_max and SUL_peak indicated a significant linear correlation with Ki-67 but a moderate correlation with the high levels of Ki-67 (p < 0,001). The sensitivity of VI, E_mean , SUV_max and SUL_peak was 64.6%, 66.7%, 65.7%, and 66.7% when the cut-off point was set to 5.25, 102.5, 6.59, and 2.63, respectively. SUV_max had the highest AUC value of 0.740, according to the ROC curve analysis.

CONCLUSIONS

Our results suggest that the quantitative parameters obtained by advanced imaging methods may be useful in predicting the high proliferation in invasive breast carcinomas. But none of them is eligible to be used as an independent biomarker in distinguishing aggressive behavior. Nevertheless, as a noninvasive method, visual assessment of microvascular morphology using SMI increases the prognostic efficiency in invasive breast carcinomas.

Diagnostic Value of Superb Microvascular Imaging in Differentiating Benign and Malignant Breast Tumors: A Systematic Review and Meta-Analysis

PURPOSE

We performed a systematic review and meta-analysis of studies that investigated the diagnostic performance of Superb Microvascular Imaging (SMI) in differentiating between benign and malignant breast tumors.

METHODS

Studies published between January 2010 and March 2022 were retrieved by online literature search conducted in PubMed, Embase, Cochrane Library, Web of Science, China Biology Medicine Disc, China National Knowledge Infrastructure, Wanfang, and Vip databases. Pooled sensitivity, specificity, and diagnostic odd ratios were calculated using Stata software 15.0. Heterogeneity among the included studies was assessed using I2 statistic and Q test. Meta-regression and subgroup analyses were conducted to investigate potential sources of heterogeneity. Influence analysis was conducted to determine the robustness of the pooled conclusions. Deeks' funnel plot asymmetry test was performed to assess publication bias. A summary receiver operating characteristic curve (SROC) was constructed.

RESULTS

Twenty-three studies involving 2749 breast lesions were included in our meta-analysis. The pooled sensitivity and specificity were 0.80 (95% confidence interval [CI], 0.77-0.84, inconsistency index [I2] = 28.32%) and 0.84 (95% CI, 0.79-0.88, I2 = 89.36%), respectively. The pooled diagnostic odds ratio was 19.95 (95% CI, 14.84-26.82). The area under the SROC (AUC) was 0.85 (95% CI, 0.81-0.87).

CONCLUSION

SMI has a relatively high sensitivity, specificity, and accuracy for differentiating between benign and malignant breast lesions. It represents a promising supplementary technique for the diagnosis of breast neoplasms.

Which combination of different ultrasonography modalities is more appropriate to diagnose breast cancer?: A network meta-analysis (a PRISMA-compliant article)

BACKGROUND

Abundant amount of literature that analyze the various detection of different ultrasound methods, no comprehensive literature that investigates the diagnostic values of breast cancer (BC) by different ultrasonography modalities through a network meta-analysis (NMA) has been made available. Each imaging diagnostic examination has its own advantages and disadvantages, and any imaging examination is not enough to make an accurate diagnosis of the disease. Thus, this study aimed to compare diagnostic values among different ultrasonography modalities, including the information of 2-dimension, stiffness and blood flow, by a network meta-analysis in the hopes of understanding which imaging methods are better and which combination of different ultrasonography modalities is more appropriate to diagnose BC.

METHODS

We made use of Cochrane Library, PubMed, and Embase in order to obtain literature and papers. The combination analysis of both direct and indirect evidence in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value(NPV) and accuracy was conducted so as to assess the odds ratios (ORs) and surface under the cumulative ranking curve (SUCRA) values of the 8 different ultrasound methods.

RESULTS

A total of 36 eligible diagnostic tests regarding 8 ultrasound methods were included in the study. According to this network meta-analysis, Breast Imaging Reporting and Data System (BI-RADS) 4b exhibited higher specificity, PPV, and accuracy and lower sensitivity and NPV than BI-RADS 4a. Contrast-enhanced ultrasound (CEUS) had the highest sensitivity, PPV, NPV and accuracy and superb microvascular imaging (SMI) had the highest specificity among color Doppler flow imaging (CDFI), power Doppler imaging(PDI), SMI and CEUS. There was no significant difference in diagnostic indexes between SMI and CEUS. Shear wave elastrography (SWE) had higher PPV and accuracy and lower sensitivity, specificity NPV than strain elastography (SE).

CONCLUSION

The results of this network meta-analysis suggested more appropriate combination of different ultrasound modalities is BI-RADS 4b, SMI, and SWE for the diagnosis of breast cancer.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

The association between conventional ultrasound and contrast-enhanced ultrasound appearances and pathological features in small breast cancer

OBJECTIVE

To investigate the association between ultrasound appearances and pathological features in small breast cancer.

MATERIALS AND METHODS

A total of 186 small breast cancers in 186 patients were analyzed in this retrospective study from January 2015 to December 2019 according to pathological results. Forty-seven cases of axillary lymph node metastasis were found. All patients underwent radical axillary surgery following conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) examinations. The association between ultrasound appearances and pathological features was analyzed using univariate distributions and multivariate analysis. Then, a logistic regression model was established using the pathological diagnosis of lymph node metastasis and biochemical indicators as the dependent variable and the ultrasound appearances as independent variables.

RESULTS

In small breast cancer, risk factors of axillary lymph node metastasis were crab claw-like enhancement on CEUS and abnormal axillary lymph nodes on US. The logistic regression model was established as follows: (axillary lymph node metastasis) = 1.100×(crab claw-like enhancement of CEUS) + 2.749×(abnormal axillary lymph nodes of US) -5.790. In addition, irregular shape on CEUS and posterior echo attenuation on US were risk factors for both positive estrogen receptor and progesterone receptor expression, whereas calcification on US was a risk factor for positive Her-2 expression. A specific relationship could be found using the following logistic models: (positive ER expression) = 1.367×(irregular shape of CEUS) + 1.441×(posterior echo attenuation of US) -5.668; (positive PR expression) = 1.265×(irregular shape of CEUS) + 1.136×(posterior echo attenuation of US) -4.320; (positive Her-2 expression) = 1.658×(calcification of US) -0.896.

CONCLUSION

Logistic models were established to provide significant value for the prediction of pre-operative lymph node metastasis and positive biochemical indicators, which may guide clinical treatment.

Correlation and agreement between superb micro-vascular imaging and contrast-enhanced ultrasound for assessing radiofrequency ablation treatment of thyroid nodules: a preliminary study

Background

To evaluate the correlation and agreement between superb micro-vascular imaging (SMI) mode and the contrast-enhanced ultrasound (CEUS) mode for the ablative completeness and the volumes of ablation lesions to determine the clinical application value of SMI in follow-up after radiofrequency ablation.

Methods

From April 2020 to June 2020, two radiologists used SMI and CEUS mode to measure the volume of the ablation lesion. We use intra-class correlation coefficient (ICC), scatter plots and Bland–Altman plots to evaluate the correlation and agreement of the two techniques. In addition, intra- and inter-observer reliability in volume measurement of ablation lesions with SMI mode was assessed.

Results

SMI mode and CEUS mode have good agreement in the evaluation of ablative completeness. The ICC was 0.876 and 0.928 of reader A and reader B between SMI mode and CEUS mode in terms of ablation lesions volume measurement. There was a strong correlation between the two modes in both reader A and reader B (r_A = 0.808; r_B = 0.882). The ICC was 0.836 for the inter-observer reliability of SMI technique. The scatter plot showed a good linear relation (r = 0.715). In the Bland–Altman plot, 4.35% (1/23) of the points was outside the 95% limits of agreement. The ICC was 0.965 for the intra-observer reliability of SMI technique, the scatter plot also showed a strong linear correlation (r = 0.965). In the Bland–Altman plot, 8.70% (2/23) of the points was outside the 95% limits of agreement.

Conclusions

SMI and CEUS have good agreement and correlation in the ablation volume measurement. SMI technology is expected to be applied as an alternative to CEUS in the clinical follow-up of ablation lesions.

The value of color-map virtual touch tissue imaging (CMV) in the differential diagnosis of benign and malignant breast lesions

BACKGROUND

Researchers have evaluated the virtual touch tissue imaging (VTI) value in the diagnosis of breast lesions, mostly based on gray-scale.

PURPOSE

This study aimed to evaluate the value of color-map virtual touch tissue imaging (CMV) in the diagnosis of breast lesions.

METHODS

We retrospectively analyzed the VTI images of 55 breast lesions in 49 female patients who underwent an examination of breast lesions in our hospital from January 2019 to December 2019. The pathological results were taken as the gold standard. The receiver operating characteristic (ROC) curve of CMV was analyzed, and its diagnostic performance was evaluated. Weighted Kappa (k) statistics were used to assess the inter-observer agreement for CMV.

RESULTS

A total of 55 breast lesions were included, including 19 malignant lesions and 36 benign lesions. Multivariate analysis showed that patients with higher CMV scores (P = 0.014, odds ratio [OR] = 13.667, 95% confidence interval = 1.702-109.773) were independent predictors of breast cancer. The sensitivity, specificity, and the area under curve (AUC) of CMV were 94.47%, 72.22%, and 0.912. The CMV's inter-observer agreement was almost perfect among radiologists with different work experience (k = 0.854, standard error = 0.049, 95% CI = 0.758-0.950).

CONCLUSIOS

CMV has high accuracy and repeatability in the diagnosis of malignant breast lesions.