Diagnostic value of contrast-enhanced ultrasound parametric imaging in breast tumors

Advantages of contrast-enhanced ultrasound in the localization and diagnostics of sentinel lymph nodes in breast cancer

Sentinel lymph nodes (SLNs) are the first station of lymph nodes that extend from the breast tumor to the axillary lymphatic drainage. The pathological status of these LNs can predict that of the entire axillary lymph node. Therefore, the accurate identification of SLNs is necessary for sentinel lymph node biopsy (SLNB) to replace axillary lymph node dissection (ALND). The quality of life and prognosis of breast cancer patients are related to proper surgical treatment after the precise identification of SLNs. Some of the SLN tracers that have been identified include radioisotope, nano-carbon, indocyanine green (ICG), and methylene blue (MB). However, these tracers have certain limitations, such as pigmentation, radiation dangers, and the requirement for costly detection equipment. Ultrasound contrast agents (UCAs) have good specificity and sensitivity, and thus can compensate for some shortcomings of the mentioned tracers. This technique is also being applied to SLNB in patients with breast cancer, and can even provide an initial judgment on SLN status. Contrast-enhanced ultrasound (CEUS) has the advantages of high distinguishability, simple operation, no radiation harm, low cost, and accurate localization; therefore, it is expected to replace the traditional biopsy methods. In addition, it can significantly enhance the accuracy of SLN localization and shorten the operation time.

Application of Quantitative Parameters of Contrast-Enhanced Ultrasound in Common Benign and Malignant Lesions in Pediatric Livers: A Preliminary Study

We aimed to investigate the diagnostic utility of quantitative parameters of contrast-enhanced ultrasound (CEUS) for benign and malignant liver lesions in pediatric patients. This was a single-center retrospective analysis of children with liver lesions who underwent CEUS at our hospital between July 2019 and February 2023. The CEUS perfusion patterns for all lesions were qualitatively analyzed using histopathology, contrast-enhanced magnetic resonance imaging, contrast-enhanced computed tomography, or long-term clinical follow-up as reference standards. The CEUS images were quantitatively analyzed using SonoLiver® software (TomTec Imaging Systems, Munich, Germany) to obtain data regarding quantitative parameters and dynamic vascular pattern (DVP) parametric images, including rise time (RT), time to peak (TTP), mean transit time (mTT), and maximum intensity (IMAX). Statistical analysis was carried out using Student's t-test and receiver operating characteristic (ROC) curve analysis to evaluate the diagnostic value of quantitative parameters. A total of 53 pediatric cases were included in this study, and 88.57% (31/35) of malignant lesions exhibited hyper-enhancement with rapid washout patterns; the same proportion of DVP parametric images exhibited washout patterns. Conversely, 94.44% (17/18) of benign lesions showed hyper-enhancement with slow washout patterns, and the same proportion of DVP parametric images showed no-washout patterns. RT, TTP, and mTT were significantly shorter in the malignant group than in the benign group (p < 0.05), while IMAX showed no significant difference (p > 0.05). ROC analysis indicated that mTT < 113.34 had the highest diagnostic value, with an area under the curve of 0.82. CEUS quantitative analysis had an accuracy of 98.11%, while qualitative analysis had an accuracy of 92.45%, with no statistically significant difference (p > 0.05). Quantitative analysis of CEUS provides valuable assistance in differentiating benign and malignant liver lesions in children. Among all quantitative parameters, mTT holds promise as a potentially valuable tool for identifying liver tumors.

Imaging of Benign and Malignant Breast Lesions Using Contrast-Enhanced Ultrasound: A Pictorial Essay

ABSTRACT

Contrast-enhanced ultrasound is a promising noninvasive imaging technique for evaluating benign and malignant breast lesions, as contrast provides information about perfusion and microvasculature. Contrast-enhanced ultrasound is currently off-label use in the breast in the United States, but its clinical and investigational use in breast imaging is gaining popularity. It is important for radiologists to be familiar with the imaging appearances of benign and malignant breast masses using contrast-enhanced ultrasound. This pictorial essay illustrates enhancement patterns of various breast masses from our own experience. Pathologies include subtypes of invasive breast cancer, fibroadenomas, papillary lesions, fibrocystic change, and inflammatory processes. Contrast-enhanced ultrasound pitfalls and limitations are discussed.

Quantification and Classification of Contrast Enhanced Ultrasound Breast Cancer Data: A Preliminary Study

This study aimed to investigate which of the two frequently adopted perfusion models better describes the contrast enhanced ultrasound (CEUS) perfusion signal in order to produce meaningful imaging markers with the goal of developing a machine-learning model that can classify perfusion curves as benign or malignant in breast cancer data. Twenty-five patients with high suspicion of breast cancer were analyzed with exponentially modified Gaussian (EMG) and gamma variate functions (GVF). The adjusted R² metric was the criterion for assessing model performance. Various classifiers were trained on the quantified perfusion curves in order to classify the curves as benign or malignant on a voxel basis. Sensitivity, specificity, geometric mean, and AUROC were the validation metrics. The best quantification model was EMG with an adjusted R² of 0.60 ± 0.26 compared to 0.56 ± 0.25 for GVF. Logistic regression was the classifier with the highest performance (sensitivity, specificity, G_mean, and AUROC = 89.2 ± 10.7, 70.0 ± 18.5, 77.1 ± 8.6, and 91.0 ± 6.6, respectively). This classification method obtained similar results that are consistent with the current literature. Breast cancer patients can benefit from early detection and characterization prior to biopsy.

Establishment of a model for predicting sentinel lymph node metastasis in early breast cancer based on contrast-enhanced ultrasound and clinicopathological features

Background

Sentinel lymph node (SLN) biopsy (SLNB) is the standard procedure for axillary staging in clinically node-negative (cN0) breast cancer patients. However, the positive rate of SLNs among cN0 stage patients is 26-35%. The identification of appropriate candidates for SLNB is quite challenging. This study aimed to establish and verify a predictive model of SLN metastasis using contrast-enhanced ultrasound (CEUS) and other clinicopathological indicators.

Methods

The clinicopathological data of 224 patients who had undergone SLNB at the Affiliated Cancer Hospital of Zhengzhou University from June 2018 to July 2019 were analyzed retrospectively. The risk prediction model of SLN metastasis was established by logistic regression analysis. According to the β value of each variable in the model, a risk score system of SLN metastasis was established and verified using the internal population. The predictive model was prospectively applied to 73 patients from July 2019 to September 2019 to evaluate the clinical value of the model in patients with early breast cancer.

Results

Multivariate analysis confirmed that body mass index (BMI), SLN aspect ratio of CEUS mode, SLN aspect ratio of mammography, lympho-vascular invasion, and cytokeratin (CK)5/6 were independent risk factors for SLN metastasis. A scoring system was established according to the above risk factors, and a receiver operating characteristic (ROC) curve was drawn. After internal- and external verification, a corrected ROC curve was drawn, respectively. The ROC curve of the modeling group, internal verification group, and external verification group was 0.9075 (95% CI: 0.8616-0.9534), 0.8766 (95% CI: 0.8192-0.9341), and 0.8505 (95% CI: 0.7333-0.9676), respectively.

Conclusions

We constructed and verified a prediction model of SLN metastasis in early breast cancer. The model has a specific predictive value for preoperative evaluation of SLN status.

Diagnostic Value of Contrast-Enhanced Ultrasonography With SonoVue in the Differentiation of Benign and Malignant Breast Lesions: A Meta-Analysis

PURPOSE

A meta-analysis was conducted to evaluate the diagnostic performance of contrast-enhanced ultrasonography using the contrast agent SonoVue to differentiate benign from malignant breast lesions.

METHOD

A comprehensive search of the literature was performed using the Embase, PubMed, and Web of Science databases to retrieve studies published before February 2020. Data were extracted, and pooled sensitivity, specificity, and diagnostic odds ratios were calculated with meta-analysis software. Heterogeneity was evaluated via the Q test and I² statistic. Meta-regression and subgroup analyses were applied to evaluate potential sources of heterogeneity. Publication bias was assessed using the Deeks' funnel plot asymmetry test. A summary receiver operating characteristic curve (SROC) was constructed.

RESULTS

A total of 27 studies including 5378 breast lesions subjected to CEUS examination with SonoVue were included in the meta-analysis. The pooled sensitivity and specificity values were 0.90 (95% confidence interval [CI], 0.88-0.91; inconsistency index [I²] = 75.7%) and 0.83 (95% CI, 0.82-0.85; I² = 91.0%), respectively. The pooled diagnostic odds ratio was 48.35% (95% CI, 31.22-74.89; I² = 77.6%). The area under the summary receiver operating characteristic curve (AUC) was 0.9354. Meta-regression analysis revealed the region of patient residence and dose of contrast agent as potential sources of heterogeneity (P < .01). Subgroup analysis showed a higher area under the summary receiver operating characteristic curve for European and higher contrast agent dose subgroups (P < .05).

CONCLUSION

Contrast-enhanced ultrasonography with SonoVue displays high sensitivity, specificity, and accuracy when differentiating benign from malignant breast lesions. Despite its current limitations, this technique presents a promising tool for diagnosing breast lesions in clinical practice.

The clinical value of Arrival-time Parametric Imaging using contrast-enhanced ultrasonography in differentiating benign and malignant breast lesions

OBJECTIVES

To evaluate the clinical value of Arrival-time Parametric Imaging (At-PI) in the differentiation of benign and malignant breast lesions.

METHODS

For this ethics committee-approved retrospective study, a total of 184 breast lesions in 176 women were included and gray-scale ultrasound, contrast-enhanced ultrasound (CEUS) and At-PI were performed. In CEUS and At-PI, perfusion patterns, perfusion uniformity and color spatial distribution for lesions were analyzed qualitatively and the maximal diameter ratio of the lesion in accumulated parametric images and that in gray-scale images (MDRAI/GI) and area ratio of the lesion in accumuated parametric images and that in gray-scale images (ARAI/GI) were calculated quantitatively. Kappa and Intraclass Correlation Coefficient were used to evaluate the interobserver reproducibility for CEUS and At-PI and the intraobserver reproducibility for At-PI, respectively. The area under receiver operating characteristic (AUC), sensitivity, specificity, accuracy and positive and negative likelihood ratios (PPV, NPV) were calculated for MDRAI/GI and ARAI/GI.

RESULTS

Good interobserver and intraobserver reproducibility for At-PI were identified. In At-PI, there were statistically significant differences in perfusion patterns, color spatial distribution, MDRAI/GI and ARAI/GI between benign and malignant breast lesions (P < 0.05). The AUCs of MDRAI/GI and ARAI/GI were 0.895 and 0.954, respectively, with no significant difference between them (Z = 1.84, P > 0.05). By using the thresholds of 1.125 for MDRAI/GI and 1.21 for ARAI/GI, the sensitivity, specificity, accuracy, PPV and NPV of At-PI were 84.48%, 88.24%, 85.57%, 92.45% and 76.92%, respectively, for MDRAI/GI and 93.10%, 91.18%, 92.39%, 94.74% and 88.57%, respectively, for ARAI/GI.

CONCLUSIONS

At-PI is helpful to distinguish benign from malignant breast lesions. And MDRAI/GI and ARAI/GI are useful and efficient features for differential diagnosis.

Characterizing Breast Lesions Using Quantitative Parametric 3D Subharmonic Imaging: A Multicenter Study

RATIONALE AND OBJECTIVES

Breast cancer is the leading type of cancer among women. Visualization and characterization of breast lesions based on vascularity kinetics was evaluated using three-dimensional (3D) contrast-enhanced ultrasound imaging in a clinical study.

MATERIALS AND METHODS

Breast lesions (n = 219) were imaged using power Doppler imaging (PDI), 3D contrast-enhanced harmonic imaging (HI), and 3D contrast-enhanced subharmonic imaging (SHI) with a modified Logiq 9 ultrasound scanner using a 4D10L transducer. Quantitative metrics of vascularity derived from 3D parametric volumes (based on contrast perfusion; PER and area under the curve; AUC) were generated by off-line processing of contrast wash-in and wash-out. Diagnostic accuracy of these quantitative vascular parameters was assessed with biopsy results as the reference standard.

RESULTS

Vascularity was observed with PDI in 93 lesions (69 benign and 24 malignant), 3D HI in 8 lesions (5 benign and 3 malignant), and 3D SHI in 83 lesions (58 benign and 25 malignant). Diagnostic accuracy for vascular heterogeneity, PER, and AUC ranged from 0.52 to 0.75, while the best logistical regression model (vascular heterogeneity ratio, central PER, and central AUC) reached 0.90.

CONCLUSION

3D SHI successfully detects contrast agent flow in breast lesions and characterization of these lesions based on quantitative measures of vascular heterogeneity and 3D parametric volumes is promising.

A fuzzy clustering based color-coded diagram for effective illustration of blood perfusion parameters in contrast-enhanced ultrasound videos

BACKGROUND AND OBJECTIVE

Early identification and diagnosis of tumors are of great significance to improve the survival rate of patients. Amongst other techniques, contrast-enhanced ultrasound is an important means to help doctors diagnose tumors. Due to the advantages of high efficiency, accuracy and objectivity, more and more computer-aided methods are used in medical diagnosis. Here we propose, a color-coded diagram based on quantitative blood perfusion parameters for contrast-enhanced ultrasound video. The method realizes the static description of the dynamic blood perfusion process in contrast-enhanced ultrasound videos and reveal the blood perfusion characteristics of all regions of the tissue providing assistance to the doctors in their clinical diagnosis.

METHODS

For effective illustration of the blood perfusion through tissues, we propose (a) an improved block matching algorithm to eliminate the image distortions caused by breathing; (b) compute the time-grayscale intensity curve for each pixel to obtain four different quantitative blood perfusion parameters; and finally (c) employ the fuzzy C-means clustering algorithm to cluster the blood perfusion parameters, where each parameter is associated with a particular color. Thus based on the correspondence between the pixel and the blood perfusion parameters, all the pixels are color-coded to obtain the color-coded diagram.

RESULTS

To the best of our knowledge, the proposed technique is one-of-its-kind to color code the contrast-enhanced ultrasound videos using blood perfusion parameters in order to understand the hemodynamic characteristics of the benign and malignant lesion. In our experiments, various contrast-enhanced ultrasound videos corresponding to several real-world cases were color-coded and the results of the experiments illustrated that the proposed color-coded diagrams are consistent with the diagnosis presented by the physicians.

CONCLUSIONS

The experimental results suggested that the proposed method can comprehensively describe the blood perfusion characteristics of tissues during the angiography process thereby effectively assisting the doctors in diagnosis.

The Value of CEUS in Distinguishing Cancerous Lymph Nodes From the Primary Lymphoma of the Head and Neck

Aim: The purpose of this study was to assess the ability of contrast-enhanced ultrasonography (CEUS) in the differential diagnosis of cancerous lymph nodes. Methods: Contrast-enhanced ultrasonography was performed in the cervical nodules of included patients, and the diagnoses were confirmed by pathological examination. Contrast-enhanced ultrasonography images and parameters of head and neck lymphomas were compared with those of cancerous lymph nodes. Besides, receiver operating characteristic curve was operated to access the diagnostic value of CEUS. Results: Finally, a total of 63 head and neck lymphomas and 80 cervical cancerous lymph nodes were enrolled in this study. Results showed that the CEUS images of lymphoma were mainly characterized by homogeneous enhancement (71.43%), and approximately half of them were centripetal perfusion (58.73%), whereas most CEUS images of cancerous lymph nodes were inhomogeneous enhancement (82.50%) and centripetal perfusion (92.50%). Quantitative analysis of CEUS parameters indicated that PI (derived peak intensity) and AUC (area under the curve) of lymphomas were both lower than those of cancerous lymph nodes (PI: 8.78 vs. 10.51, AUC: 652.62 vs. 784.09, respectively) (P < 0.05). Receiver operating characteristic analysis showed that the sensitivity of CEUS parameters in the differential diagnosis was significant (80.00%), although the specificity was not high (47.62%). When parameters were combined with the image features, the accuracy of diagnosis was greatly improved (from 0.655 to 0.899). Conclusion: Contrast-enhanced ultrasonography could be a promising tool for the differential diagnosis of head and neck lymphomas and cancerous lymph nodes.

Immunohistochemical index prediction of breast tumor based on multi-dimension features in contrast-enhanced ultrasound

Breast cancer is the leading killer of Chinese women. Immunohistochemistry index has great significance in the treatment strategy selection and prognosis analysis for breast cancer patients. Currently, histopathological examination of the tumor tissue through surgical biopsy is the gold standard to determine immunohistochemistry index. However, this examination is invasive and commonly causes discomfort in patients. There has been a lack of noninvasive method capable of predicting immunohistochemistry index for breast cancer patients. This paper proposes a machine learning method to predict the immunohistochemical index of breast cancer patients by using noninvasive contrast-enhanced ultrasound. A total of 119 breast cancer patients were included in this retrospective study. Each patient implemented the pathological examination of immunohistochemical expression and underwent contrast-enhanced ultrasound imaging of breast tumor. The multi-dimension features including 266 three-dimension features and 837 two-dimension dynamic features were extracted from the contrast-enhanced ultrasound sequences. Using the machine learning prediction method, 21 selected multi-dimension features were integrated to generate a model for predicting the immunohistochemistry index noninvasively. The immunohistochemical index of human epidermal growth factor receptor-2 (HER2) was predicted based on multi-dimension features in contrast-enhanced ultrasound sequence with the sensitivity of 71%, and the specificity of 79% in the testing cohort. Therefore, the noninvasive contrast-enhanced ultrasound can be used to predict the immunohistochemical index. To our best knowledge, no studies have been reported about predicting immunohistochemical index by using contrast-enhanced ultrasound sequences for breast cancer patients. Our proposed method is noninvasive and can predict immunohistochemical index by using contrast-enhanced ultrasound in several minutes, instead of relying totally on the invasive and biopsy-based histopathological examination. Graphical abstract Immunohistochemical index prediction of breast tumor based on multi-dimension features in contrast-enhanced ultrasound.

Breast non-mass-like lesions on contrast-enhanced ultrasonography: Feature analysis, breast image reporting and data system classification assessment

BACKGROUND

Breast non-mass-like lesions (NMLs) account for 9.2% of all breast lesions. The specificity of the ultrasound diagnosis of NMLs is low, and it cannot be objectively classified according to the 5^th Edition of the Breast Imaging Reporting and Data System (BI-RADS). Contrast-enhanced ultrasound (CEUS) can help to differentiate and classify breast lesions but there are few studies on NMLs alone.

AIM

To analyze the features of benign and malignant breast NMLs in grayscale ultrasonography (US), color Doppler flow imaging (CDFI) and CEUS, and to explore the efficacy of the combined diagnosis of NMLs and the effect of CEUS on the BI-RADS classification of NMLs.

METHODS

A total of 51 breast NMLs verified by pathology were analyzed in our hospital from January 2017 to April 2019. All lesions were examined by US, CDFI and CEUS, and their features from those examinations were analyzed. With pathology as the gold standard, binary logic regression was used to analyze the independent risk factors for malignant breast NMLs, and a regression equation was established to calculate the efficiency of combined diagnosis. Based on the regression equation, the combined diagnostic efficiency of US combined with CEUS (US + CEUS) was determined. The initial BI-RADS-US classification of NMLs was adjusted according to the independent risk factors identified by CEUS, and the diagnostic efficiency of CEUS combined with BI-RADS (CEUS + BI-RADS) was calculated based on the results. ROC curves were drawn to compare the diagnostic values of the three methods, including US, US + CEUS, and CEUS + BI-RADS, for benign and malignant NMLs.

RESULTS

Microcalcification, enhancement time, enhancement intensity, lesion scope, and peripheral blood vessels were significantly different between benign and malignant NMLs. Among these features, microcalcification, higher enhancement, and lesion scope were identified as independent risk factors for malignant breast NMLs. When US, US + CEUS, and CEUS + BI-RADS were used to identify the benign and malignant breast NMLs, their sensitivity rates were 82.6%, 91.3%, and 87.0%, respectively; their specificity rates were 71.4%, 89.2%, and 92.9%, respectively; their positive predictive values were 70.4%, 87.5%, and 90.9%, respectively; their negative predictive values were 83.3%, 92.6%, and 89.7%, respectively; their accuracy rates were 76.5%, 90.2%, and 90.2%, respectively; and their corresponding areas under ROC curves were 0.752, 0.877 and 0.903, respectively. Z tests showed that the area under the ROC curve of US was statistically smaller than that of US + CEUS and CEUS + BI-RADS, and there was no statistical difference between US + CEUS and CEUS + BI-RADS.

CONCLUSION

US combined with CEUS can improve diagnostic efficiency for NMLs. The adjustment of the BI-RADS classification according to the features of contrast-enhanced US of NMLs enables the diagnostic results to be simple and intuitive, facilitates the management of NMLs, and effectively reduces the incidence of unnecessary biopsy.

Factors influencing the performance of a diagnostic model including contrast-enhanced ultrasound in 1023 breast lesions: comparison with histopathology

Background

We aimed to investigate the influence of patient and lesion characteristics on our diagnostic model for contrast-enhanced ultrasound (CEUS) of the breast, comparing its accuracy with that of histopathology.

Methods

Conducting a study with eight medical centers, we compared 1,023 breast lesions categorized as BI-RADS 4 or 5 with the score from our newly-established CEUS-based diagnostic model, comparing the results with pathological outcomes. Univariate and multivariate logistic regression analyses were conducted to determine the influence of clinicopathological characteristics on the performance of this CEUS model.

Results

Logistic regression analysis showed that patients' age, maximum lesion diameter, and distance from the lesion's deep edge to the pectoralis major were significant independent influencing factors. The model's diagnostic accuracy was greater for patients >35 y (P=0.005), for maximum lesion diameter >20 mm, and for distance from the lesion's deep edge to the pectoralis major ≤3.05 mm. There was no significant difference in accuracy between lesions with maximum lesion diameter 10-20 and <10 mm (P=0.393).

Conclusions

The diagnostic performance of the proposed CEUS model for breast lesions is influenced by patients' age, maximum lesion diameter, and distance from the lesion's deep edge to the pectoralis major. Consideration of influencing factors is required to optimize clinical use of the CEUS model.

Contrast-Enhanced Ultrasound Imaging of Breast Masses: Adjunct Tool to Decrease the Number of False-Positive Biopsy Results

OBJECTIVES

This pilot study evaluated use of contrast-enhanced ultrasound (CEUS) to reduce the number of benign breast masses recommended for biopsy.

METHODS

This prospective study included 131 consenting women, from October 2016 to June 2017, with American College of Radiology Breast Imaging Reporting and Data System category 4a, 4b, and 4c masses detected by mammography, conventional ultrasound (US), or both. Contrast-enhanced US examinations (using intravenous injection of perflutren lipid microspheres or sulfur hexafluoride lipid-type A microspheres) were performed before biopsy. Qualitative and quantitative CEUS parameters were compared with reference standard histopathologic results from biopsy of 131 masses.

RESULTS

There were 109 benign, 6 high-risk, and 16 malignant masses, with a median size of 12 mm (range, 4 to 48 mm) on conventional US imaging. Of 131 masses, 93 (71%) enhanced on CEUS imaging, including 73 of 109 (67%) benign, 6 of 6 (100%) high-risk, and 14 of 16 (87.5%) malignant. Thirty-eight lesions did not enhance, including 36 of 109 (33%) benign and 2 of 16 (12.5%) malignant. Prediction models using recursive petitioning revealed that CEUS may reduce 31% (95% confidence interval, 23%, 40%) of benign biopsies for masses that are: nonenhancing with circumscribed margins or enhancing with an oval shape and homogeneous enhancement. Quantitative parameters indicated that benign masses had the longest time to peak (P = .078), highest time-to-peak ratio of mass to background (P = .036), lowest peak intensity (P = .021), and smallest difference in peak intensity between the mass and background (P = .079) compared to high-risk and malignant lesions.

CONCLUSIONS

Contrast-enhanced US may be a valuable modality that can be used to predict benign pathologic results of breast masses, thereby reducing the number of biopsies.

Monitoring Progression of Ductal Carcinoma In Situ Using Photoacoustics and Contrast-Enhanced Ultrasound

Breast cancer is the leading form of cancer in women, accounting for approximately 41,400 deaths in 2018. While a variety of risk factors have been identified, physical exercise has been linked to reducing both the risk and aggressiveness of breast cancer. Within breast cancer, ductal carcinoma in situ (DCIS) is a common finding. However, less than 25% of DCIS tumors actually progress into invasive breast cancer, resulting in overtreatment. This overtreatment is due to a lack of predictive precursors to assess aggressiveness and development of DCIS. We hypothesize that tissue oxygenation and perfusion measured by photoacoustic and contrast-enhanced ultrasound imaging, respectively, can predict DCIS aggressiveness. To test this, 20 FVB/NJ and 20 SV40Tag mice that genetically develop DCIS-like breast cancers were divided evenly into exercise and control groups and imaged over the course of 6 weeks. Tissue oxygenation was a predictive precursor to invasive breast cancer for FVB/NJ mice (P = 0.015) in the early stages of tumor development. Meanwhile, perfusion results were inconclusive (P > 0.2) as a marker for disease progression. Moreover, voluntary physical exercise resulted in lower weekly tumor growth and significantly improved median survival (P = 0.014).

Diagnostic Value of Arrival Time Parametric Imaging Using Contrast-Enhanced Ultrasonography in Superficial Enlarged Lymph Nodes

OBJECTIVE

The aim of this study was to evaluate the value of arrival-time parametric imaging for differential diagnosis of superficial enlarged lymph nodes.

METHODS

Patients with lymphadenopathy who received contrast-enhanced ultrasonography (CEUS) and biopsy were included in this study. Following CEUS, a prototype software of the arrival-time parametric imaging system was used to analyze the video footage. Arrival-time patterns during the arterial phase were evaluated. The quantitative parameters including arrival time of periphery, arrival time of center, and the travel time (△T) were calculated.

RESULTS

A total of 145 lymph nodes were analyzed. Arrival-time parametric imaging showed that 80.3% of metastatic lymph nodes and 68.4% of lymphoid tuberculosis presented a centripetal perfusion pattern, 76.5% of lymphoma showed complete homogeneous enhancement, and 81.2% of reactive lymph nodes had centrifugal patterns. The arrival time of periphery (sec) of metastatic lymph nodes was substantially earlier than that of lymphoma (11.0 ± 3.1 versus 12.6 ± 3.6; P < .05). The arrival time of center (sec) of metastatic lymph nodes was obviously later than that of lymphoma and reactive lymph nodes (13.4 ± 3.3 versus 10.5 ± 2.9 and 10.6 ± 1.5; P < .05). The travel time (△T) (sec) in metastatic lymph nodes was substantially longer than in reactive lymph nodes and lymphoma (4.2 ± 2.1 versus 2.3 ± 1.6 and 2.9 ± 2.5; P < .05). At a △T cutoff value of 2.75 seconds (using the receiver operating characteristic curve), the sensitivity and specificity in differentiating metastatic lymph nodes from benign lymph nodes (lymphoid tuberculosis and reactive lymph nodes) were 78.9% and 64.7%, respectively.

CONCLUSIONS

Enhanced patterns and parameters of arrival-time parametric imaging during CEUS could provide more information for the differential diagnosis of enlarged superficial lymph nodes.

Comparative Diagnostic Accuracy of Contrast-Enhanced Ultrasound and Shear Wave Elastography in Differentiating Benign and Malignant Lesions: A Network Meta-Analysis

Background: We performed a network meta-analysis to compare the diagnostic accuracy of contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) in differentiating benign and malignant lesions in different body sites.

Methods: A computerized literature search of Medline, Embase, SCOPUS, and Web of Science was performed using relevant keywords. Following data extraction, we calculated sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) for CEUS, and SWE compared to histopathology as a reference standard. Statistical analyses were conducted by MetaDiSc (version 1.4) and R software (version 3.4.3).

Results: One hundred and fourteen studies (15,926 patients) were pooled in the final analyses. Network meta-analysis showed that CEUS had significantly higher DOR than SWE (DOR = 27.14, 95%CI [2.30, 51.97]) in breast cancer detection. However, there were no significant differences between CEUS and SWE in hepatic (DOR = −6.67, 95%CI [−15.08, 1.74]) and thyroid cancer detection (DOR = 3.79, 95%CI [−3.10, 10.68]). Interestingly, ranking analysis showed that CEUS achieved higher DOR in detecting breast and thyroid cancer, while SWE achieved higher DOR in detecting hepatic cancer. The overall DOR for CEUS in detecting renal cancer was 53.44, 95%CI [29.89, 95.56] with an AUROC of 0.95, while the overall DOR for SWE in detecting prostate cancer was 25.35, 95%CI [7.15, 89.89] with an AUROC of 0.89.

Conclusion: Both diagnostic tests showed relatively high sensitivity and specificity in detecting malignant tumors in different organs. Network meta-analysis showed that CEUS had higher diagnostic accuracy than SWE in detecting breast and thyroid cancer, while SWE had higher accuracy in detecting hepatic cancer. However, the results were not statistically significant in hepatic and thyroid malignancies. Further head-to-head comparisons are needed to confirm the optimal imaging technique to differentiate each cancer type.

Incorporating Contrast-Enhanced Ultrasound into the BI-RADS Scoring System Improves Accuracy in Breast Tumor Diagnosis: A Preliminary Study in China

The aim of the study was to develop a scoring model incorporating the Breast Imaging Reporting and Data System (BI-RADS) and the contrast-enhanced ultrasound (CEUS) scoring system to differentiate between malignant and benign breast lesions. A total of 524 solid breast masses in 490 consecutive patients were evaluated with conventional US and CEUS in this prospective study. Each lesion was scored according to BI-RADS, CEUS, and CEUS-rerated BI-RADS. The diagnostic specificity, sensitivity and accuracy of BI-RADS were 77.9%, 88.9% and 84.0%, respectively, and the area under the receiver operating characteristic curve was 0.834. The corresponding values for rerated BI-RADS were 82.1%, 96.9%, 90.3% and 0.895. The area under the receiver operating characteristic curve of BI-RADS alone was significantly smaller than that of CEUS and the rerated BI-RADS (p = 0.008 compared with CEUS, p = 0.002 compared with rerated BI-RADS). This study indicates that rerating BI-RADS with the CEUS scoring system improves its diagnostic accuracy.

Diagnostic Value of Contrast-Enhanced Sonography for Differentiation of Breast Lesions: A Meta-analysis

OBJECTIVES

The purpose of this study was to systematically review and evaluate the diagnostic accuracy of contrast-enhanced sonography in the differential diagnosis of benign and malignant breast lesions.

METHODS

The scientific literature databases PubMed and Embase were comprehensively searched for relevant studies before January 2015. Data were pooled to yield the summary sensitivity, specificity, and diagnostic odds ratio using meta-analysis software.

RESULTS

A total of 29 studies with 2296 lesions were included in the analysis. The pooled sensitivity and specificity were 0.88 (95% confidence interval [CI], 0.86-0.90; inconsistency index [I(2)] = 77.9%) and 0.80 (95% CI, 0.78-0.83; I(2) = 84.0%), respectively. The pooled diagnostic odds ratio was 30.35 (95% CI, 15.75-58.48; I(2)= 82.1%), and the area under the summary receiver operating characteristic curve was 0.9115 (SE, 0.0243).

CONCLUSIONS

The comprehensive results suggest that contrast-enhanced sonography could be a potentially effective method for differential diagnosis of benign and malignant breast lesions.

Qualitative, quantitative and combination score systems in differential diagnosis of breast lesions by contrast-enhanced ultrasound

OBJECTIVE

To assess the feasibility of score systems in differential diagnosis of breast lesions by contrast-enhanced ultrasound (CEUS).

METHODS

CEUS was performed in 121 patients with 127 breast lesions by Philips iU22 with Sonovue as contrast agent. Pearson Chi-square χ(2) test, binary logistic regression analysis and Student's t-test are used to identify significant CEUS parameters in differential diagnosis. Based on these significant CEUS parameters, qualitative, quantitative and combination score systems were built by scoring 1 for benign characteristic and scoring 2 for malignant characteristic. Receiver operating characteristic (ROC) curve was applied to evaluate the diagnostic efficacy of different analytical methods.

RESULTS

Pathological results showed 41 benign and 86 malignant lesions. Qualitative analysis and logistic regression analysis showed that there are significant differences in enhancement degree, enhancement order, internal homogeneity, enhancement margin, surrounding vessels and enlargement of diameters (P<0.05) between benign and malignant lesions. Quantitative analysis indicated that malignant lesions tended to show higher peak intensity (PI), larger area under the curve (AUC) and shorter time to peak (TTP) than benign ones (P<0.05). Qualitative score systems showed higher diagnostic efficacy than single quantitative CEUS parameters. The corresponding area under the ROC curve for qualitative, quantitative and combination score systems were 0.897, 0.716 and 0.903 respectively. Z test showed that area under the ROC curve of quantitative score system was statistically smaller than that of other score systems.

CONCLUSIONS

Quantitative score system helps little in improving the diagnostic efficacy of CEUS. While qualitative score system improves the performance of CEUS greatly in discrimination of benign and malignant breast lesions. The application of qualitative could develop the diagnostic performance of CEUS which is clinically promising.

Contrast-Enhanced Color Doppler Ultrasonography for Preoperative Evaluation of Sentinel Lymph Node in Breast Cancer Patients

BACKGROUND

Sentinel lymph node (SLN) biopsy is the standard of care for breast cancer patients with non-palpable axillary lymph nodes. We evaluated the usefulness of contrast-enhanced ultrasonography in preoperative detection of malignant SLNs.

METHODS

50 patients with breast cancer (median age: 60 years) underwent a color power Doppler ultrasonography with intravenous contrast (Sonovue®) preoperatively, and findings suggestive of metastatic disease to the SLN were documented. The final histopathological report and the radiological preoperative record were compared. Finally, the sensitivity, specificity and diagnostic accuracy of this evolving diagnostic modality were calculated.

RESULTS

Contrast-enhanced ultrasound scan identified a negative SLN in the axilla of 27 patients and final histopathology was negative for 30 cases in total, so negative predictive value was calculated as 90% and positive predictive value was 75%. Overall sensitivity was 83.33% and specificity was 84.38%. Moreover, the ability of contrast-enhanced ultrasound to differentiate between SLN status was only statistically significantly correlated with the actual final histopathological report (p < 0.001), while successful ultrasound prediction was not correlated with any factor.

CONCLUSIONS

SLN status can be evaluated preoperatively using contrast-enhanced color Doppler ultrasonography with high accuracy.

Review of ultrasound image guidance in external beam radiotherapy: I. Treatment planning and inter-fraction motion management

In modern radiotherapy, verification of the treatment to ensure the target receives the prescribed dose and normal tissues are optimally spared has become essential. Several forms of image guidance are available for this purpose. The most commonly used forms of image guidance are based on kilovolt or megavolt x-ray imaging. Image guidance can also be performed with non-harmful ultrasound (US) waves. This increasingly used technique has the potential to offer both anatomical and functional information.This review presents an overview of the historical and current use of two-dimensional and three-dimensional US imaging for treatment verification in radiotherapy. The US technology and the implementation in the radiotherapy workflow are described. The use of US guidance in the treatment planning process is discussed. The role of US technology in inter-fraction motion monitoring and management is explained, and clinical studies of applications in areas such as the pelvis, abdomen and breast are reviewed. A companion review paper (O'Shea et al 2015 Phys. Med. Biol. submitted) will extensively discuss the use of US imaging for intra-fraction motion quantification and novel applications of US technology to RT.

Diffuse angiomatosis of the breast--sonographic appearance

Angiomatosis or diffuse hemangioma is a very rare benign vascular tumor, consisting of blood and lymphatic channels growing diffusely in the breast parenchyma. We report a case of diffuse breast angiomatosis in a 34-year-old woman with pubertal anisomastia. Ultrasound raised the suspicion of vascular tumor, by showing large cystic spaces separated by septae with moderate blood flow, similar to those found in cystic lymphangioma. We discuss the imaging (mammography, ultrasound, and MRI) and pathologic findings, with a brief review of the literature.

Correlation between maximum intensity and microvessel density for differentiation of malignant from benign thyroid nodules on contrast-enhanced sonography

OBJECTIVES

The purpose of this study was to retrospectively evaluate contrast-enhanced sonography for differentiation of benign and malignant thyroid nodules by analyzing the correlation between maximum intensity and microvessel density.

METHODS

From February 2010 to May 2012, 122 patients (85 female and 37 male; mean age ± SD, 45 ± 9.1 years) with thyroid nodules (62 papillary thyroid carcinomas, 30 nodular goiters, and 30 adenomas) that underwent routine thyroid sonography and were diagnosed by surgery were included in this study. Contrast-enhanced sonography was performed, and enhancement patterns were classified into 3 groups: high, equal, and low enhancement. As a time-intensity curve parameter, the correlation of maximum intensity with CD31 and CD34 microvessel density counts was analyzed.

RESULTS

On contrast-enhanced sonography, most patients with papillary thyroid carcinomas showed a heterogeneous low enhancement pattern, whereas most patients with nodular goiters showed an equal enhancement pattern, and patients with adenomas showed a high enhancement pattern. The detection of papillary thyroid carcinomas with low enhancement had sensitivity of 96.8%, specificity of 95.0%, and accuracy of 95.9%. Compared with the papillary thyroid group, the mean microvessel density counts were significantly higher in the nodular goiter and adenoma groups (P< .05). We also found that the maximum intensity was significantly associated with CD31 and CD34 counts (CD31, r = 0.963; P < .01; CD34, r = 0.968; P < .01).

CONCLUSIONS

Maximum intensity has a significant relationship with microvessel density. Contrast-enhanced sonography is a practical and convenient means for differentiating benign from malignant thyroid nodules.

Contrast-enhanced ultrasound analysis of tissue perfusion in tumor-bearing mice following treatment with endostatin combined with radiotherapy

The aim of this study was to observe the effects of Endostar (recombinant human endostatin) and radiotherapy, singly or in combination, on blood flow in mouse tumour tissue using ultrasound imaging. The ultrasound contrast agent, SonoVue, was used for the contrast-enhanced ultrasound examinations. SonoLiver software was used to analyse dynamic vascular patterns (DVPs) in the contrast process. Blood perfusion data were collected and statistical analysis was performed for data processing. Results were presented as DVP curves and quantitative parameters. Quantitative parameters showed statistically significant (P<0.05) differences in peak strength, rise time, time to peak and mean transit time among the various treatment groups. Changes in tumour blood perfusion were quantified by the assessment of contrast-enhanced ultrasound parameters. The results indirectly reflected the degree of change in angiogenesis in the tumour following experimental intervention. Ultrasound contrast imaging effectively showed the extent of the changes in vascularity and flow state. Therefore, contrast-enhanced ultrasound is suitable for use as an indicator of blood flow changes in an experimental model.

Antiangiogenesis therapy for breast cancer: an update and perspectives from clinical trials

The development of new blood vessels is a crucial step in breast cancer growth, progression and dissemination, making it a promising therapeutic target. Breast cancer has a heterogeneous nature and the diversity of responsible angiogenic pathways between different tumors has been studied for many years. Inhibiting different targets in these pathways has been under investigation in preclinical and clinical studies for more than decades, among which antibody against vascular endothelial growth factor is the most studied. However, the clinical impact from antiangiogenic treatment alone or in combination with standard chemotherapeutic regimens has been relatively small till today. In this review, we summarize the most clinically relevant data from breast cancer treatment clinical trials and discuss safety and efficacy of common antiangiogenic therapies as well as biological predictive markers.