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Conventional US and 2-D Shear Wave Elastography of Virtual Touch Tissue Imaging Quantification: Correlation with Immunohistochemical Subtypes of Breast Cancer
Abstract
Our study aimed to investigate the correlation of the imaging features obtained using conventional ultrasound (US) and elastography (conventional strain elastography of elasticity imaging [EI], virtual touch tissue imaging [VTI] and 2-D shear wave elastography [2-D-SWE] of virtual touch tissue imaging quantification [VTIQ]) with the clinicopathologic features and immunohistochemical (IHC) subtypes of breast cancer. The sample consisted of images from 202 patients with 206 breast lesions that were confirmed as breast cancers. Lesions with HER2 overexpression (luminal B HER2+ or HER2+) had higher mean shear wave velocity (SWV) values than the others. Older patients, lower histologic grade, no lymphovascular invasion and no lymph node metastasis were associated with luminal A (p < 0.001). There were significant differences in SWV values, histologic grade and lymph node status among the different pathologic types. This association may allow the use of 2-D-SWE in the pre-operative prediction of tumor characteristics and biologic activity, which may determine the prognosis in a non-invasive manner.
... Diagnostic workup of BC utilizes ultrasound scans (US), mammography (MMG) including contrast-enhanced spectral mammography and magnetic resonance imaging (MRI). The above-mentioned BC subtypes are characterized by specific features in different imaging modalities [2][3][4][5][6][7][8][9][10][11][12][13]. (Table 1). ...
... High grade (G3) TNBC type more commonly appears as lesions with irregular shape [9] • Tumors with HER2 overexpression exhibit higher Young's modulus values in shear wave elastography (SWE) than LA tumors [10] • Similar, high Young's modulus values in SWE for all molecular BC subtypes, with the exception of tubular BC [11] • Particular sets of features for individual breast cancer types [12] MR • MRI reveals stronger background parenchymal enhancement (BPE) in TNBC, weaker in luminal B (HER2−) type [4] • ...
... Sonoelastography scans demonstrated that tumors with HER2 overexpression exhibit higher Young's modulus values in shear wave elastography (SWE) than LA tumors in one study [10], while other study [11] revealed similar, high Young's modulus values in SWE for all molecular BC subtypes, with the exception of tubular BC [12]. ...
Molecular profile of breast cancer provides information about its biological activity, prognosis and treatment strategies. The purpose of our study was to investigate the correlation between ultrasound features and molecular subtypes of breast cancer. From June 2019 to December 2019, 86 patients (median age 57 years; range 32–88) with 102 breast cancer tumors were included in the study. The molecular subtypes were classified into five types: luminal A (LA), luminal B without HER2 overexpression (LB HER2−), luminal B with HER2 overexpression (LB HER2+), human epidermal growth factor receptor 2 positive (HER2+) and triple negative breast cancer (TNBC). Histopathological verification was obtained in core biopsy or/and post-surgery specimens in all cases. Univariate logistic regression analysis was performed to assess the association between the subtypes and ultrasound imaging features. Experienced radiologists assessed lesions according to the BIRADS-US lexicon. The ultrasound scans were performed with a Supersonic Aixplorer and Supersonix. Based on histopathological verification, the rates of LA, LB HER2−, LB HER2+, HER2+, and TNBC were 33, 17, 17, 16, 19, respectively. Both LB HER2+ and HER2+ subtypes presented higher incidence of calcification (OR = 3.125, p = 0.02, CI 0.0917–5.87) and HER2+ subtype presented a higher incidence of posterior enhancement (OR = 5.75, p = 0.03, CI 1.2257–32.8005), compared to other subtypes. The calcifications were less common in TNBC (OR = 0.176, p = 0.0041, CI 0.0469–0.5335) compared to other subtypes. There were no differences with regard to margin, shape, orientation, elasticity values and vascularity among five molecular subtypes. Our results suggest that there is a correlation between ultrasonographic features assessed according to BIRADS-US lexicon and BC subtypes with HER2 overexpression (both LB HER2+ and HER2+). It may be useful for identification of these aggressive subtypes of breast cancer.
... Greyscale US, colour Doppler flow imaging (CDFI), and shear-wave elastography (SWE) examinations have been widely used to characterize breast lesions in clinical practice. Some studies found that some US image features from radiologists' visual interpretation were related to certain molecular subtypes of breast cancer [15,16]. However, there exists high inter-and intraobserver variability in the interpretation of US images, and there is currently no practicable method to directly predict molecular subtypes of breast cancer [17]. ...
... Ko et al. reported that triple negative cancer was more likely to exhibit circumscribed and markedly hypoechoic shadowing but less likely to have posterior acoustic shadowing on greyscale US [38]. Furthermore, some studies have shown that blood vessel distribution and lesion stiffness varied among different molecular subtypes [11,16,39]. HER-2 positive cancers are more likely to have internal vessels, while luminal cancers are internal vessel poor with prominent external vessels [11]. ...
... HER-2 positive cancers are more likely to have internal vessels, while luminal cancers are internal vessel poor with prominent external vessels [11]. Shear wave velocity shows significant differences in different subtypes of breast cancer [16,39]. These findings supported a strong link between intrinsic biological properties and imaging manifestations. ...
Background:
Preoperative determination of breast cancer molecular subtypes facilitates individualized treatment plan-making and improves patient prognosis. We aimed to develop an assembled convolutional neural network (ACNN) model for the preoperative prediction of molecular subtypes using multimodal ultrasound (US) images.
Methods:
This multicentre study prospectively evaluated a dataset of greyscale US, colour Doppler flow imaging (CDFI), and shear-wave elastography (SWE) images in 807 patients with 818 breast cancers from November 2016 to February 2021. The St. Gallen molecular subtypes of breast cancer were confirmed by postoperative immunohistochemical examination. The monomodal ACNN model based on greyscale US images, the dual-modal ACNN model based on greyscale US and CDFI images, and the multimodal ACNN model based on greyscale US and CDFI as well as SWE images were constructed in the training cohort. The performances of three ACNN models in predicting four- and five-classification molecular subtypes and identifying triple negative from non-triple negative subtypes were assessed and compared. The performance of the multimodal ACNN was also compared with preoperative core needle biopsy (CNB).
Finding:
The performance of the multimodal ACNN model (macroaverage area under the curve [AUC]: 0.89-0.96) was superior to that of the dual-modal ACNN model (macroaverage AUC: 0.81-0.84) and the monomodal ACNN model (macroaverage AUC: 0.73-0.75) in predicting four-classification breast cancer molecular subtypes, which was also better than that of preoperative CNB (AUC: 0.89-0.99 vs. 0.67-0.82, p < 0.05). In addition, the multimodal ACNN model outperformed the other two ACNN models in predicting five-classification molecular subtypes (AUC: 0.87-0.94 vs. 0.78-0.81 vs. 0.71-0.78) and identifying triple negative from non-triple negative breast cancers (AUC: 0.934-0.970 vs. 0.688-0.830 vs. 0.536-0.650, p < 0.05). Moreover, the multimodal ACNN model obtained satisfactory prediction performance for both T1 and non-T1 lesions (AUC: 0.957-0.958 and 0.932-0.985).
Interpretation:
The multimodal US-based ACNN model is a potential noninvasive decision-making method for the management of patients with breast cancer in clinical practice.
Funding:
This work was supported in part by the National Natural Science Foundation of China (Grants 81725008 and 81927801), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), and the Science and Technology Commission of Shanghai Municipality (Grants 19441903200, 19DZ2251100, and 21Y11910800).
... Various studies have shown that US features differ according to prognostic markers and these findings help us to gain a deeper understanding of tumor biology. 13,14 However, inconsistent results obtained from studies and the subjective nature of the US make it harder to generalize the findings and implement routine practice. ...
... In our study, no significant correlation was found between IHC-subtype and elasticity values, which is in agreement with previous reports. 12,14,18,19 Youk et al 12 revealed that negative ER, negative PR and higher Ki-67 index were associated with higher E mean values. However, no independent association was found between IHC-subtype and SWE parameters in multivariate analysis. ...
In this study, we aimed to investigate the correlation of stiffness values of shear-wave elastography (SWE) and histopathological prognostic factors in patients with breast cancer. Between January 2021 and June 2022, SWE images of 138 core-biopsy proven breast cancer lesions from 132 patients were retrospectively reviewed. Histopathogic prognostic factors, including tumor size, histologic grade, histologic type, hormone receptor positivity, human epidermal growth factor receptor (HER2) status, immunohistochemical subtype and Ki-67 index were documented. Elasticity values including mean and maximum elasticity (Emean and Emax) and lesion-to-fat ratio (Eratio) were recorded. The association between histopathological prognostic factors and elasticity values were assessed using Mann-Whitney U and Kruskal-Wallis test, and multiple linear regression analysis. Tumor size, histological grade, and Ki-67 index were significantly associated with the Eratio (P < 0.05). Larger tumor size and higher Ki-67 index also showed significantly higher Emean and Emax values (P < 0.05). However, hormone receptor positivity, HER2 status, and immunohistochemical subtype were not significantly associated with elasticity values (P > 0.05). Multivariate logistic regression analysis revealed that tumor size was significantly associated with Emean, Emax, and Eratio values (P < 0.05). A high Ki-67 index was also significantly associated with high Eratio values. Larger tumor size and higher Ki-67 index are independently associated with high Eratio values. Preoperative SWE may improve the performance of conventional ultrasound in predicting prognosis and treatment planning.
... Owing to a certain degree of overlap in the atypical features and morphological characteristics of BI-RADS 3-5 breast diseases, it is not enough to identify benign and malignant lesions by the conventional US alone (9). In recent years, based on traditional US, some relatively new ultrasonic technologies, such as contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE), have gradually been developed to make up for the lack to compensate for the limitations of conventional US (10,11). Numerous studies have suggested that with the gradual improvement of CEUS and SWE, their diagnostic accuracy for breast diseases has improved considerably (12,13). ...
... for the qualitative analysis of breast lesions (14). SWE, another new related ultrasonographic technique, utilizes the elastic properties of tissue to assess the stiffness of lesions (11). With its high repeatability, SWE has also been widely applied for diagnosing substantial superficial diseases, especially breast neoplasms (21). ...
Background:
Mass-like (ML) and non-mass-like (NML) are two manifestations of breast lesions on ultrasound. Contrast-enhanced ultrasound (CEUS) can make up for the limitation of B-ultrasound (US) in the observation of focal blood flow, and shear wave elastography (SWE) can supplement the hardness information of the lesion. The present study aimed to analyze the characteristic manifestations of US, CEUS, and SWE in NML and ML breast and evaluate whether the diagnostic performance of these three ultrasound techniques differs in terms of differentiating between benign and malignant breast lesions.
Methods:
From January to August 2021, 382 patients (417 breast lesions) underwent US, CEUS, and SWE examinations. Of these, 204 women (218 breast lesions) were included in our study due to subsequent biopsy or surgery with pathological findings. The patients were divided into ML and NML groups according to the ultrasound characteristics, and the differences in multimodal ultrasound performance between benign and malignant NML and benign and malignant ML breast lesions were compared. The diagnostic performance of US, US + CEUS, US + SWE, US + CEUS + SWE for ML, NML and all breast lesions was evaluated by analyzing sensitivity, specificity and area under receiver operating characteristic (ROC) curve (AUC).
Results:
Pathologically, the 218 lesions included 96 malignant and 122 benign breast lesions. The sensitivity and specificity of US + CEUS + SWE in all lesion groups, ML group and NML group were 92.7% and 90.2%, 95.9% and 90.3%, 91.3% and 79.3%, respectively. In all breast group, AUCs of US + CEUS, US + SWE, US + CEUS + SWE were statistically different from AUC of US (P=0.0010, 0.0001, 0.0001). In the ML group, the AUC of US + CEUS, US + SWE, US + CEUS + SWE were statistically different from that of US (P=0.0120, 0.0008, 0.0002). In the NML group, there was a statistical difference between US + SWE and US AUC (P=0.0149).
Conclusions:
US, CEUS, and SWE have an important diagnostic value for benign and malignant ML and NML breast lesions. Multimodal ultrasound combined with US, CEUS, and SWE can improve the diagnostic efficacy in distinguishing between benign and malignant ML and NML lesions.
... There is a limited document in the literature examining the effectiveness of elastography on molecular subtypes. No satisfactory data have been reported to distinguish subtypes with high accuracy (13,29,30,31). In these studies, high stiffness was associated with aggressive subtypes such as HER2 positive and TNBC. ...
... Zheng et al. reported that aggressive molecular subtypes correlated with E mean (29). Liu H et al. emphasized that HER2 positive tumors have higher E mean values than the other subtypes (30). Cho et al., in their study in which the lesions were scored qualitatively by strain elastography, high elasticity scores were correlated with lymph node involvement. ...
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 (Emean) 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 Emean values were relatively higher in non-luminal subtypes (VI, p = 0.002; Emean, 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, Emean, 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 Emean and may contribute to the management of breast carcinomas as a non-invasive variable.
... Previous studies have shown that the size of benign and malignant breast lesions can affect the elasticity of the lesion; the size of the lesion is positively correlated with the maximum elasticity of breast cancer. 19,20 Antonio Bulum et al. divided all lesions into small (diameter < 1.5 cm) and large lesions (diameter ≥ 1.5 cm) to investigate the effect of lesion size on the performance of SWE in differentiating malignant breast lesions, and found that larger lesions had significantly higher E mean and E max values. 21 However, these studies are limited to In vivo breast lesions, and there will be histopathological differences between lesions of different sizes, which will also affect the elasticity of the tissue. ...
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.
... The incidence of CLNM and LLNM in patients with papillary thyroid microcarcinoma was lower than that of PTC, which was also confirmed from the side [24,25]. Second, the lower elasticity indicates less deposition of collagen in the tumor [26] thus facilitating the higher aggressiveness of the biological behavior [27]. The reason may be that they receive efferent lymphatics from the thyroid gland through DLNs, pretracheal, perithyroidal and paratracheal nodal chains before draining to the lateral neck nodes, providing abundant transportation channels for cancer cell metastasis [28,29]. ...
Background
Delphian lymph node (DLN) has been considered to be a gate that predicts widespread lymph node involvement, higher recurrence and mortality rates of head and neck cancer.
Objective
This study aimed to establish a preoperative ultrasonography integrated machine learning prediction model to predict Delphian lymph node metastasis (DLNM) in patients with diagnosed papillary thyroid carcinoma (PTC).
Methods
Ultrasonographic and clinicopathologic variables of PTC patients from 2014 to 2021 were retrospectively analyzed. The risk factors associated with DLNM were identified and validated through a developed random forest (RF) algorithm model based on machine learning and a logistic regression (LR) model.
Results
A total of 316 patients with 402 thyroid lesions were enrolled for the training dataset and 280 patients with 341 lesions for the validation dataset, with 170 (28.52%) patients developed DLNM. The elastography score of ultrasonography, central lymph node metastasis, lateral lymph node metastasis, and serum calcitonin were predictive factors for DLNM in both models. The RF model has better predictive performance in the training dataset and validation dataset (AUC: 0.957 vs. 0.890) than that in the LR model (AUC: 0.908 vs. 0.833).
Conclusion
The preoperative ultrasonography integrated RF model constructed in this study could accurately predict DLNM in PTC patients, which may provide clinicians with more personalized clinical decision-making recommendations preoperatively. Machine learning technology has the potential to improve the development of DLNM prediction models in PTC patients.
... These methods of automation and data analysis also require time and hardware. However, their efficiency and speed are better than manual diagnostics based on biomarkers [5][6][7][8][9]. There are several problems with the accuracy and reliability of the dataset, data gaps, noise, anomalies, etc. ...
The paper explored the problem of automatic diagnosis based on immunohistochemical image analysis. The issue of automated diagnosis is a preliminary and advisory statement for a diagnostician. The authors studied breast cancer histological and immunohistochemical images using the following biomarkers progesterone, estrogen, oncoprotein, and a cell proliferation biomarker. The authors developed a breast cancer diagnosis method based on immunohistochemical image analysis. The proposed method consists of algorithms for image preprocessing, segmentation, and the determination of informative indicators (relative area and intensity of cells) and an algorithm for determining the molecular genetic breast cancer subtype. An adaptive algorithm for image preprocessing was developed to improve the quality of the images. It includes median filtering and image brightness equalization techniques. In addition, the authors developed a software module part of the HIAMS software package based on the Java programming language and the OpenCV computer vision library. Four molecular genetic breast cancer subtypes could be identified using this solution: subtype Luminal A, subtype Luminal B, subtype HER2/neu amplified, and basalt-like subtype. The developed algorithm for the quantitative characteristics of the immunohistochemical images showed sufficient accuracy in determining the cancer subtype “Luminal A”. It was experimentally established that the relative area of the nuclei of cells covered with biomarkers of progesterone, estrogen, and oncoprotein was more than 85%. The given approach allows for automating and accelerating the process of diagnosis. Developed algorithms for calculating the quantitative characteristics of cells on immunohistochemical images can increase the accuracy of diagnosis.
... 9 Similarly, this principle has been also applied to differentiate extracapsular invasion in breast invasive lobular carcinoma and malignant thyroid nodules. 10,11 Strong signals indicating soft edges are also frequently seen in abdominal imaging of the pancreas and bowel, representing natural slip surfaces such as the peritoneum or luminal surface of the bowel. 12,13 ...
... Recently, SWE has been included as a complementary technique to BMUS in the BI-RADS. The use of quality map in SWE could improve the diagnostic performance for breast lesions [8,9,[19][20][21][22]. Nonetheless, interpretation of breast lesions seen on US image is based on visual assessment, which is inevitably subject to interobserver variability. ...
Purpose
To develop a nomogram incorporating B-mode ultrasound (BMUS) and shear-wave elastography (SWE) radiomics to predict malignant status of breast lesions seen on US non-invasively.
Methods
Data on 278 consecutive patients from Hospital #1 (training cohort) and 123 cases from Hospital #2 (external validation cohort) referred for breast US with subsequent histopathologic analysis between May 2017 and October 2019 were retrospectively collected. Using their BMUS and SWE images, we built a radiomics nomogram to improve radiology workflow for management of breast lesions. The performance of the algorithm was compared with a consensus of three ACR BI-RADS committee experts and four individual radiologists, all of whom interpreted breast US images in clinical practice.
Results
Twelve features from BMUS and three from SWE were selected finally to construct the respective radiomic signature. The nomogram based on the dual-modal US radiomics achieved good diagnostic performance in the training (AUC 0.96; 95% confidence intervals [CI], 0.94–0.98) and the validation set (AUC 0.92; 95% CI, 0.87–0.97). For the 123 test lesions, the algorithm achieved 105 of 123 (85%) accuracy, comparable to the expert consensus (104 of 123 [85%], P = 0.86) and four individual radiologists (93, 99, 95 and 97 of 123, with P value of 0.05, 0.31, 0.10 and 0.18 respectively). Furthermore, the model also performed well in the BI-RADS 4 and 5 categories.
Conclusions
Performance of a dual-model US radiomics nomogram based on SWE for breast lesion classification may comparable to that of expert radiologists who used ACR BI-RADS guideline.
... Histological grade, lymph node involvement and mass diameter are all important prognostic factors (Carter et al. 1989;Elston and Ellis 1991;S anchez-Muñoz et al. 2008;Tang et al. 2009;Youk et al. 2013;Liu et al. 2019). In addition, the status of immunohistochemical (IHC) biomarkers, usually obtained from core biopsies or from the surgical specimen, including estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status and Ki-67 proliferation index, are used for subtype classification (Tang et al. 2009;Tamaki et al. 2013). ...
This purpose of this study is to correlate a new shear-wave elastography (SWE) parameter, mass characteristic frequency (fmass) and other elasticity measure with the prognostic histological factors and immunohistochemical (IHC) biomarkers for the evaluation of heterogeneous breast carcinomas. The new parameter, fmass, first introduced in this paper, is defined as the ratio of the averaged minimum shear wave speed taken spatially within regions of interest to the largest mass dimension. 264 biopsy-proven breast cancerous masses were included in this study. Mean (Emean), maximum (Emax), minimum (Emin) shear wave elasticity and standard deviation (Esd) of shear wave elasticity were found significantly correlated with tumor size, axillary lymph node (ALN) status, histological subtypes and IHC subtypes. The areas under the curve for the ALN prediction are 0.73 (95% confidence interval [CI]: 0.67–0.80) and 0.75 (95% CI: 0.69–0.81) for the combination of Emean with Breast Imaging Reporting and Data System (BI-RADS) score and Emax with BI-RADS score, respectively. fmass was significantly correlated with the presence of calcifications, ALN status, histological grade, the expressions of IHC biomarkers and IHC subtypes. To conclude, poor prognostic factors were associated with high shear wave elasticity values and low mass characteristic frequency value. Therefore, SWE provides valuable information that may help with prediction of breast cancer invasiveness.
... 9 Similarly, this principle has been also applied to differentiate extracapsular invasion in breast invasive lobular carcinoma and malignant thyroid nodules. 10,11 Strong signals indicating soft edges are also frequently seen in abdominal imaging of the pancreas and bowel, representing natural slip surfaces such as the peritoneum or luminal surface of the bowel. 12,13 ...
OBJECTIVE
Providing new tools to improve surgical planning is considered a main goal in meningioma treatment. In this context, two factors are crucial in determining operating strategy: meningioma-brain interface and meningioma consistency. The use of intraoperative ultrasound (ioUS) elastosonography, a real-time imaging technique, has been introduced in general surgery to evaluate similar features in other pathological settings such as thyroid and prostate cancer. The aim of the present study was to evaluate ioUS elastosonography in the intraoperative prediction of key intracranial meningioma features and to evaluate its application in guiding surgical strategy.
METHODS
An institutional series of 36 meningiomas studied with ioUS elastosonography is reported. Elastographic data, intraoperative surgical findings, and corresponding preoperative MRI features were classified, applying a score from 0 to 2 to both meningioma consistency and meningioma-brain interface. Statistical analysis was performed to determine the degree of agreement between meningioma elastosonographic features and surgical findings, and whether intraoperative elastosonography was a better predictor than preoperative MRI in assessing meningioma consistency and slip-brain interface, using intraoperative findings as the gold standard.
RESULTS
A significantly high degree of reliability and agreement between ioUS elastographic scores and surgical finding scores was reported (intraclass correlation coefficient = 0.848, F = 12.147, p < 0.001). When analyzing both consistency and brain-tumor interface, ioUS elastography proved to have a rather elevated sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive (LR+) and negative likelihood ratio (LR−). This consideration was true especially for meningiomas with a hard consistency (sensitivity = 0.92, specificity = 0.96, PPV = 0.92, NPV = 0.96, LR+ = 22.00, LR− = 0.09) and for those presenting with an adherent slip-brain interface (sensitivity = 0.76, specificity = 0.95, PPV = 0.93, NPV = 0.82, LR+ = 14.3, LR− = 0.25). Furthermore, predictions derived from ioUS elastography were found to be more accurate than MRI-derived predictions, as demonstrated by McNemar’s test results in both consistency (p < 0.001) and interface (p < 0.001).
CONCLUSIONS
While external validation of the data is needed to transform ioUS elastography into a fully deployable clinical tool, this experience confirmed that it may be integrated into meningioma surgical planning, especially because of its rapidity and cost-effectiveness.
... Although analyzing breast cancer molecular subtypes based on US images is a relatively new area of exploration, there is some evidence to support an ultrasonic and biological basis for our findings. For example, significant differences in shear wave velocity values among different molecular subtypes were detected in a previous report [37]. Besides, noncircumscribed margins and posterior shadowing were observed more commonly in luminal disease, but luminal B cancers have higher vascularity than the luminal A subtype [38]. ...
Objectives:
To evaluate the prediction performance of deep convolutional neural network (DCNN) based on ultrasound (US) images for the assessment of breast cancer molecular subtypes.
Methods:
A dataset of 4828 US images from 1275 patients with primary breast cancer were used as the training samples. DCNN models were constructed primarily to predict the four St. Gallen molecular subtypes and secondarily to identify luminal disease from non-luminal disease based on the ground truth from immunohistochemical of whole tumor surgical specimen. US images from two other institutions were retained as independent test sets to validate the system. The models' performance was analyzed using per-class accuracy, positive predictive value (PPV), and Matthews correlation coefficient (MCC).
Results:
The model achieved good performance in identifying the four breast cancer molecular subtypes in the two test sets, with accuracy ranging from 80.07% (95% CI, 76.49-83.23%) to 97.02% (95% CI, 95.22-98.16%) and 87.94% (95% CI, 85.08-90.31%) to 98.83% (95% CI, 97.60-99.43) for the two test cohorts for each sub-category, respectively. In terms of 4-class weighted average MCC, the model achieved 0.59 for test cohort A and 0.79 for test cohort B. Specifically, the DCNN also yielded good diagnostic performance in discriminating luminal disease from non-luminal disease, with a PPV of 93.29% (95% CI, 90.63-95.23%) and 88.21% (95% CI, 85.12-90.73%) for the two test cohorts, respectively.
Conclusion:
Using pretreatment US images of the breast cancer, deep learning model enables the assessment of molecular subtypes with high diagnostic accuracy.
Trial registration:
Clinical trial number: ChiCTR1900027676 KEY POINTS: • Deep convolutional neural network (DCNN) helps clinicians assess tumor features with accuracy. • Multicenter retrospective study shows that DCNN derived from pretreatment ultrasound imagine improves the prediction of breast cancer molecular subtypes. • Management of patients becomes more precise based on the DCNN model.
... In SWE, the probe emits safe acoustic radiation pulses, which can focus on tissues at different depths and continuously induce the tissue particles to vibrate and produce transverse shear wave which is then accurately measured [12]. SWE has been used to evaluate the elasticity of a variety of normal and/or injured tissues [13][14][15]. ...
Objectives:
This study aimed to investigate the application of shear wave elastography (SWE) in the early damage detection through assessing the endometrial elasticity after artificial abortion.
Methods:
A total of nulliparous women (20-30 years) who received ultrasonography in our hospital were recruited between January 2017 and December 2017. These women were divided into normal control group (NC; n = 65), after once artificial abortion group (AOAA; n = 68), after twice artificial abortion group (ATAA; n = 61), and after three times or more (range, 3-6) artificial abortion group (ATTMAA; n = 60). SWE was performed to evaluate the endometrium; Young's modulus of the endometrium was determined and then the endometrial thickness was measured.
Results:
Young's modulus of the endometrium increased in the order of NC group, AOAA group, ATAA group, and ATTMAA group, and Young's modulus increased with the increase in the number of artificial abortions (p < 0.05). The endometrial thickness in the ATTMAA group was significantly lower than in the NC group, AOAA group, and ATAA group (p < 0.05), but there was no marked difference among the NC group, AOAA group, and ATAA group (p > 0.05).
Conclusions:
SWE increases with increasing number of abortions, which may indicate the damage that is done to the endometrium earlier than measurement of the endometrial thickness do.
Objectives
Our study aims to investigate the impact of B‐mode ultrasound (B‐US) imaging, color Doppler flow imaging (CDFI), strain elastography (SE), and patient age on the prediction of molecular subtypes in breast lesions.
Methods
Totally 2272 multimodal ultrasound imaging was collected from 198 patients. The ResNet‐18 network was employed to predict four molecular subtypes from B‐US imaging, CDFI, and SE of patients with different ages. All the images were split into training and testing datasets by the ratio of 80%:20%. The predictive performance on testing dataset was evaluated through 5 metrics including mean accuracy, precision, recall, F1‐scores, and confusion matrix.
Results
Based on B‐US imaging, the test mean accuracy is 74.50%, the precision is 74.84%, the recall is 72.48%, and the F1‐scores is 0.73. By combining B‐US imaging with CDFI, the results were increased to 85.41%, 85.03%, 85.05%, and 0.84, respectively. With the integration of B‐US imaging and SE, the results were changed to 75.64%, 74.69%, 73.86%, and 0.74, respectively. Using images from patients under 40 years old, the results were 90.48%, 90.88%, 88.47%, and 0.89. When images from patients who are above 40 years old, they were changed to 81.96%, 83.12%, 80.5%, and 0.81, respectively.
Conclusion
Multimodal ultrasound imaging can be used to accurately predict the molecular subtypes of breast lesions. In addition to B‐US imaging, CDFI rather than SE contribute further to improve predictive performance. The predictive performance is notably better for patients under 40 years old compared with those who are 40 years old and above.
Background:
Endometrial receptivity is crucial for the establishment of a healthy pregnancy outcome. Previous research on endometrial receptivity primarily examined endometrial thickness, endometrial echo types, and endometrial blood supply.
Objective:
To explore the differences in the elastic modulus of the endometrium in women with various pregnancy outcomes by real-time shear wave elastography (SWE) and to investigate its application value in evaluation of endometrial receptivity.
Methods:
A total of 205 pregnant women who were admitted at Wenzhou People's Hospital between January 2021 and December 2022 were selected. Three-dimensional transvaginal sonography and real-time shear wave elastography were performed in the proliferative phase and receptive phase of the endometrium, and the average elastic modulus of the endometrium in the two phases was obtained and compared. According to whether the pregnancy was successful or not, the participants were divided into the pregnancy group (n= 72) and non-pregnancy group (n= 133), and the differences in intimal thickness, 3D blood flow parameters, and average elastic modulus of intima were compared between the two groups.
Results:
The average elastic modulus of the endometrium in the proliferative phase and receptive phase was (23.92 ± 2.31) kPa and (11.82 ± 2.24) kPa, respectively, and the difference was statistically significant P< 0.05. The average elastic modulus of the endometrium in the pregnancy group and non-pregnancy group was (9.97 ± 1.08) kPa and (12.82 ± 2.06) kPa, respectively, and the difference was statistically significant P< 0.05. The area under the curve of predicting pregnancy by the average elastic modulus of the endometrium in the receptive phase was 0.888 (0.841∼0.934), with corresponding P value < 0.05. The critical value was 11.15, with a corresponding sensitivity of 81.7% and specificity of 78.2%.
Conclusion:
Real-time shear wave elastography can quantitatively evaluate endometrial elasticity, indirectly reflect the endometrial phase, and provide a new diagnostic concept for evaluating endometrial receptivity and predicting pregnancy outcome in infertile patients.
Objective
To explore the correlation between ultrasound images and molecular typing of invasive breast cancer, so as to analyze the predictive value of preoperative ultrasound for invasive breast cancer.
Methods
302 invasive breast cancer patients were enrolled in Heping Hospital affiliated to Changzhi Medical College in Shanxi, China during 2020 to 2022. All patients accepted ultrasonic and pathological examination, and all pathological tissues received molecular typing with immunohistochemical (IHC) staining. The relevance between different molecular typings and ultrasonic image, pathology were evaluated.
Results
Univariate analysis: among the four molecular typings, there were significant differences in tumor size, shape, margin, lymph node and histological grade ( P <0.05). 1. Size: Luminal A tumor was smaller (69.4%), Basal -like type tumors are mostly larger (60.9%); 2. Shape: Basal-like type is more likely to show regular shape (45.7%); 3. Margin: Luminal A and Luminal B mostly are not circumscribed (79.6%, 74.8%), Basal -like type shows circumscribed(52.2%); 4. Lymph nodes: Luminal A type tends to be normal (87.8%), Luminal B type,Her-2+ type and Basal-like type tend to be abnormal (35.6%,36.4% and 39.1%). There was no significant difference in mass orientation, echo pattern, rear echo and calcification ( P >0.05). Multivariate analysis: Basal-like breast cancer mostly showed regular shape, circumscribed margin and abnormal lymph nodes ( P <0.05).
Conclusion
There are differences in the ultrasound manifestations of different molecular typings of breast cancer, and ultrasound features can be used as a potential imaging index to provide important information for the precise diagnosis and treatment of breast cancer.
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 micro�vascular 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 (Emean)
was evaluated in kilopascal by SWE. The average of consecutive measurements was
recorded as mean VI and mean Emean. PET scan parameters were obtained as SUVmax
and SULpeak. 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 Emean had poor correlation (p > 0.05). SUVmax and
SULpeak indicated a significant linear correlation with Ki-67 but a moderate correla�tion with the high levels of Ki-67 (p < 0,001). The sensitivity of VI, Emean, SUVmax and
SULpeak 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. SUVmax 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 inva�sive 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.
Breast cancer is a heterogeneous disease both in its clinical and radiological manifestations and response to treatment. This is largely due to the polymorphism of the histological types as well as diversified molecular profiles of individual breast cancer types. Progress in the understanding of the biology of breast cancer was made with the introduction of immunohistochemical research into the common practice. On this basis, four main breast cancer subtypes were distinguished: luminal A, luminal B, HER2 positive (human epidermal growth factor receptor-2 positive), and triple negative cancer. The classification of a tumour to an appropriate subtype allows for the optimisation of treatment (surgery or pre-operative chemotherapy). In this study, the authors present different patterns of breast cancer subtypes in ultrasound examination and differences in their treatment, with particular emphasis on aggressive breast cancer subtypes, such as triple negative or HER2 positive. They can, unlike the luminal subtypes, create diagnostic problems. Based on multifactorial analysis of the ultrasound image, with the assessment of lesion margins, orientation, shape, echogenicity, vascularity, the presence of calcifications or assessment by sonoelastography, it is possible to initially differentiate individual subtypes.
Background:
This study examined the effects of different ultrasound imaging technologies in the identification and prediction of axillary lymph node metastasis of breast cancer. It also investigated the relationship between human papilloma virus (HPV) infection and axillary lymph node metastasis.
Methods:
Eighty-five female patients diagnosed with breast masses participated in this study. Each patient underwent a conventional ultrasound, ultrasonic elastography, and virtual touch tissue imaging quantification (VTIQ). The differential diagnosis efficiency of a conventional ultrasound, ultrasound elastography, VTIQ, and ultrasound elastography combined with VTIQ technology was compared with a pathological diagnosis, which represents the gold standard. 85 axillary lymph node tissues and 25 normal breast tissues were used to detect HPV positive infection rate differences in different tissues.
Results:
The results showed that metastatic lymph nodes and reactive lymph node hyperplasia accounted for 54.12% and 45.88% of the 85 axillary lymph nodes of breast cancer, respectively. The conventional ultrasound, ultrasound elastography, and VTIQ scores of metastatic lymph nodes were significantly higher than those of reactive lymph node hyperplasia (P<0.05). The diagnostic sensitivity (Se) (91.30%), specificity (Sp) (92.31%), accuracy (Ac) (91.76%), positive predictive value (PPV) (93.33%), and negative predictive value (NPV) (90.00%) of ultrasound elastography combined with VTIQ technology were the highest among the diagnostic efficiency test results of different computer ultrasound imaging technologies. The positive infection rate of HPV in metastatic lymph node tissues was significantly higher than that in reactive lymph node hyperplasia and normal breast tissues (P<0.05).
Conclusions:
Combining ultrasound elastography with VTIQ technology has high value in the differential diagnosis of axillary lymph nodes of breast cancer. Further, it appears that HPV infection may have an etiological role in lymph node metastasis in breast cancer patients.
Objectives
This study aimed to evaluate the stiffness of 2‐dimensional (2D) shear wave elastography (SWE) in preoperatively predicting the prognostic stage groups of invasive ductal carcinoma (IDC).
Methods
Eighty‐six newly diagnosed lesions on 83 patients with IDCs were analyzed. All parameters from conventional ultrasound and stiffness to virtual touch tissue imaging and quantification were collected, and mean shear wave velocity (SWVmean) was calculated. Data on maximum diameter, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), histologic grading system and Tumor Node Metastasis (TNM) stages were collected. The levels of maximum shear wave velocity (SWVmax), minimum shear wave velocity (SWVmin) and SWVmean were compared. In receiver operating characteristic (ROC) curves analysis, the diagnostic efficacy was found in area under the curve (AUC). Parallel mode was used to improve the predictive value of sensitivity.
Results
The median stiffness of SWVmax and SWVmean for IDCs were 9.38 and 6.32 m/s for late stage (stages II, III, IV) and 6.39 m/s and 4.72 m/s for early stage (stage I) of the prognostic stage groups, respectively. The median stiffness values in the late stage were significantly higher than those in the early stage (P = .003, P = .005). The optimal cutoff stiffness of SWVmax and SWVmean were 8.62 and 6.13 m/s, respectively. In ROC curves analysis, the AUC for SWVmax was 0.742, and it showed a better diagnostic value than SWVmean (0.725). In predictive diagnosis, the sensitivity for SWVmax and SWVmean were both 62.50%. The parallel mode improved the prediction power of sensitivity to 68.75%.
Conclusions
Preoperative SWV level may serve as a promising prognostic imaging indicator for breast IDCs.
Résumé
Le cancer du sein est une maladie hétérogène. En mammographie, les opacités spiculées correspondent aux cancers de type luminal A, les moins agressifs. Les sous-types de cancer du sein les plus agressifs sont plus difficiles à diagnostiquer car leurs caractéristiques en imagerie imitent des lésions bénignes et peuvent ainsi être négligées sur l’imagerie standard. Une analyse rigoureuse de ces lésions pseudo bénignes à la recherche d’éléments subtils doit être systématiquement réalisée. L’échographie peut compléter la mammographie de dépistage, en particulier chez les patientes aux seins denses. Enfin, ces cancers à l’aspect pseudo bénin en mammographie et en échographie sont très suspects de malignité sur l’IRM lorsque celle-ci est réalisée. Nous détaillerons dans cet article les aspects en imagerie les plus fréquents du cancer du sein en fonction du sous-type moléculaire.
The aim of the present study was to evaluate to what extent the combination of standard histopathological parameters determines the biology of breast cancer and the effect on therapy and prognosis. The Clinical Cancer Registry Regensburg (Bavaria, Germany) included n = 4,480 female patients with primary, non-metastatic (M0) invasive breast cancer diagnosed between 2000 and 2012. Immuno-histochemical analyses, i.e., estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki-67 (4-IHC), defined the tumor biological subtypes Luminal A, Luminal B, HER2-like, and Basal-like. Subtype-related differences in therapies and overall survival (OS) were analyzed using multivariable statistical methods. 4344 patients (97.0 %) could be classified into the four common tumor biological subtypes. The two most frequent entities were Luminal A (48.4 %), Luminal B (24.8 %), HER2-like (17.8 %), and Basal-like subtype (9.0 %). A multivariable Cox regression model showed that the best 7-year OS was seen in Luminal A patients and that OS of Luminal B and HER2-like patients was comparable (HR = 1.59, P < 0.001 versus HR = 1.51, P = 0.03). Lowest OS was seen in patients with Basal-like tumors (HR = 2.18, P < 0.001). In conclusion, the classification of tumor biological subtypes by the ER, PR, HER2, and Ki-67 biomarkers is practical in routine clinical work. Providing that quality assurance of these markers is ensured, this classification is useful for making therapy decisions in the routine clinical management of breast cancer patients.
The 13th St Gallen International Breast Cancer Conference (2013) Expert Panel reviewed and endorsed substantial new evidence on aspects of the local and regional therapies for early breast cancer, supporting less extensive surgery to the axilla and shorter durations of radiation therapy. It refined its earlier approach to the classification and management of luminal disease in the absence of amplification or overexpression of the Human Epidermal growth factor Receptor 2 (HER2) oncogene, while retaining essentially unchanged recommendations for the systemic adjuvant therapy of HER2-positive and 'triple-negative' disease. The Panel again accepted that conventional clinico-pathological factors provided a surrogate subtype classification, while noting that in those areas of the world where multi-gene molecular assays are readily available many clinicians prefer to base chemotherapy decisions for patients with luminal disease on these genomic results rather than the surrogate subtype definitions. Several multi-gene molecular assays were recognized as providing accurate and reproducible prognostic information, and in some cases prediction of response to chemotherapy. Cost and availability preclude their application in many environments at the present time. Broad treatment recommendations are presented. Such recommendations do not imply that each Panel member agrees: indeed, among more than 100 questions, only one (trastuzumab duration) commanded 100% agreement. The various recommendations in fact carried differing degrees of support, as reflected in the nuanced wording of the text below and in the votes recorded in supplementary Appendix S1, available at Annals of Oncology online. Detailed decisions on treatment will as always involve clinical consideration of disease extent, host factors, patient preferences and social and economic constraints.
To evaluate the correlations of maximum stiffness (Emax) and mean stiffness (Emean) of invasive carcinomas on shear-wave elastography (SWE) with St. Gallen consensus tumor phenotypes.
We used an ultrasound system with SWE capabilities to prospectively study 190 women with 216 histologically confirmed invasive breast cancers. We obtained one elastogram for each lesion. We correlated Emax and Emean with tumor size, histologic type and grade, estrogen and progesterone receptors, HER2 expression, the Ki67 proliferation index, and the five St. Gallen molecular subtypes: luminal A, luminal B without HER2 overexpression (luminal B HER2-), luminal B with HER2 overexpression (luminal B HER2+), HER2, and triple negative.
Lesions larger than 20mm had significantly higher Emax (148.04kPa) and Emean (118.32kPa) (P=0.005) than smaller lesions. We found no statistically significant correlations between elasticity parameters and histologic type and grade or molecular subtypes, although tumors with HER2 overexpression regardless whether they expressed hormone receptors (luminal B HER2+ and HER2 phenotypes) and triple-negative tumors had lower Emax and Emean than the others. We assessed the B-mode ultrasound findings of the lesions with some of the Emax or Emean values less than or equal to 80kPa; only four of these had ultrasound findings suggestive of a benign lesion (two with luminal A phenotype and two with HER2 phenotype).
We were unable to demonstrate statistically significant differences among the subtypes of invasive tumors, although there appears to be a trend toward lower Emax and Emean in the aggressive phenotypes.
Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Objectives:
To evaluate virtual touch tissue imaging quantification (VTIQ) as a new elastography method concerning its intra- and interexaminer reliability and its ability to differentiate benign from malignant breast lesions in comparison to and in combination with ultrasound (US) B-mode breast imaging reporting and data system (BI-RADS) assessment.
Materials and methods:
US and VTIQ were performed by two examiners in 103 women with 104 lesions. Intra- and interexaminer reliability of VTIQ was assessed. The area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of BIRADS, VTIQ, and combined data were compared.
Results:
Fifty-four of 104 lesions were malignant. Intraexaminer reliability was consistent, and interexaminer agreement showed a strong positive correlation (r = 0.93). The mean VTIQ values in malignant lesions were significantly higher than those in benign (7.73 m/s ± 1.02 versus 4.46 m/s ± 1.87; P < 0.0001). The combination of US-BIRADS with the optimal cut-off for clinical decision making of 5.18 m/s yielded a sensitivity of 98%, specificity of 82%, PPV of 86%, and NPV of 98%. The combination of BIRADS and VTIQ led to improved test validity.
Conclusion:
VTIQ is highly reliable and reproducible. There is a significant difference regarding the mean maximum velocity of benign and malignant lesions. Adding VTIQ to BIRADS assessment improves the specificity.
Purpose:
To present a method for identifying intrinsic imaging phenotypes in breast cancer tumors and to investigate their association with prognostic gene expression profiles.
Materials and methods:
The authors retrospectively analyzed dynamic contrast material-enhanced (DCE) magnetic resonance (MR) images of the breast in 56 women (mean age, 55.6 years; age range, 37-74 years) diagnosed with estrogen receptor-positive breast cancer between 2005 and 2010. The study was approved by the institutional review board and compliant with HIPAA. The requirement to obtain informed consent was waived. Primary tumors were assayed with a validated gene expression assay that provides a score for the likelihood of recurrence. A multiparametric imaging phenotype vector was extracted for each tumor by using quantitative morphologic, kinetic, and spatial heterogeneity features. Multivariate linear regression was performed to test associations between DCE MR imaging features and recurrence likelihood. To identify intrinsic imaging phenotypes, hierarchical clustering was performed on the extracted feature vectors. Multivariate logistic regression was used to classify tumors at high versus low or medium risk of recurrence. To determine the additional value of intrinsic phenotypes, the phenotype category was tested as an additional variable. Receiver operating characteristic analysis and the area under the receiver operating characteristic curve (Az) were used to assess classification performance.
Results:
There was a moderate correlation (r = 0.71, R(2) = 0.50, P < .001) between DCE MR imaging features and the recurrence score. DCE MR imaging features were predictive of recurrence risk as determined by the surrogate assay, with an Az of 0.77 (P < .01). Four dominant imaging phenotypes were detected, with two including only low- and medium-risk tumors. When the phenotype category was used as an additional variable, the Az increased to 0.82 (P < .01).
Conclusion:
Intrinsic imaging phenotypes exist for breast cancer tumors and correlate with recurrence likelihood as determined with gene expression profiling. These imaging biomarkers could ultimately help guide treatment decisions.
Molecular subtyping of breast cancer may provide additional prognostic information regarding patient outcome. However, its clinical significance remains to be established. In this study, the main aims were to discover whether reclassification of breast cancer into molecular subtypes provides more precise information regarding outcome compared to conventional histopathological grading and to study breast cancer-specific survival in the different molecular subtypes. Cases of breast cancer occurring in a cohort of women born between 1886 and 1928 with long-term follow-up were included in the study. Tissue microarrays were constructed from archival formalin-fixed, paraffin-embedded tissue from 909 cases. Using immunohistochemistry and in situ hybridisation as surrogates for gene expression analyses, all cases were reclassified into the following molecular subtypes: Luminal A; Luminal B (HER2-); Luminal B (HER2+); HER2 subtype; Basal phenotype; and five negative phenotype. Kaplan-Meier survival curves and Cox proportional hazards models were used in the analyses. During the first 5 years after diagnosis, there were significant differences in prognosis according to molecular subtypes with the best survival for the Luminal A subtype and the worst for HER2 and five negative phenotype. In this historic cohort of women with breast cancer, differences in breast cancer-specific survival according to subtype occur almost exclusively amongst the histopathological grade 2 tumours. From 5 years after time of diagnosis until the end of follow-up, there appears to be no difference in survival according to molecular subtype or histopathological grade.
The purpose of this article is to correlate various ultrasound features of breast cancer with tumor grade, and with estrogen, progesterone, and ERRB2 (formerly HER2) receptor status as well as to assess the predictive value of these features.
The features of breast cancers found by using ultrasound between January 2010 and June 2011 were reviewed for tumor size, margins, and posterior acoustic features. The tumor margins were classified into spiculated, angular, indistinct, lobulated or microlobulated, and circumscribed. The posterior acoustic features were classified into shadowing, enhancement, mixed pattern, and no change. The individual features were correlated with the estrogen receptor (ER)-progesterone receptor (PR) and ERRB2 receptor status and tumor grade.
Among 160 patients with breast cancer, 102 (63.8%) were ER-positive/PR-positive, 32 (20.0%) were ER-positive/PR-negative, and 26 (16.3%) were ER-negative/PR-negative (22 were triple-negative). Tumors with posterior shadowing have greater than nine times the odds of having ER-positive findings (95% CI, 2.09-40.81; p = 0.011) and greater than 13 times the odds of having a lower-grade tumor (I or II vs III; 95% CI, 4.90-36.54; p < 0.001) than those without posterior shadowing. Tumors with posterior enhancement have greater than eight times the odds of having at least one negative receptor (95% CI, 3.97-18.11; p < 0.001) and 24 times the odds of having a high-grade tumor (95% CI, 9.91-58.14; p < 0.001) than those without posterior enhancement.
The presence of posterior shadowing is strongly associated with an ER-positive and low-grade tumor, whereas the presence of posterior enhancement is strongly associated with a high-grade tumor and with moderate risk of being receptor negative.
The St Gallen International Expert Consensus 2011 has proposed a new classification system for breast cancer. The purpose of this study was to elucidate the relationship between the breast cancer subtypes determined by the new classification system and genomic characteristics.
Invasive breast cancers (n = 363) were immunohistochemically classified as follows: 111 (30.6%) as luminal A, 95 (26.2%) as luminal B (HER2 negative), 69 (19.0%) as luminal B (HER2 positive), 41 (11.3%) as HER2, and 47 (12.9%) as basal-like subtypes.
The high expression of Ki-67 antigen was detected in 236 tumors; no cases of luminal A subtype showed high expression of the Ki-67 antigen, but more than 85% of tumors of the other subtypes showed high expression. In addition, DNA ploidy and chromosomal instability (CIN) were assessed using imaging cytometry and FISH, respectively. In this series, 336 (92.6%) tumors consisted of 129 diploid/CIN- and 207 aneuploid/CIN + tumors. Diploid/CIN- and aneuploid/CIN+ features were detected in 64.9% and 27.9% of luminal A, 41.1% and 49.5% of luminal B (HER2-), 11.6% and 81.2% of luminal B (HER2+), 4.9% and 90.2% of HER2, and 17.0% and 76.6% of basal-like subtypes, respectively. Unlike the luminal B (HER2+), HER2 and basal-like subtypes, the luminal A and luminal B (HER2-) subtypes were heterogeneous in terms of DNA ploidy and CIN.
It is reasonable to propose that the luminal A and luminal B (HER2-) subtypes should be further divided into two subgroups, diploid/CIN- and aneuploid/CIN+, based on their underlying genomic status.
Objective:
The objective of our study was to retrospectively evaluate the imaging findings of patients with breast cancer negative for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)-so-called "triple receptor-negative cancer"-and to compare the mammographic findings and clinical characteristics of triple receptor-negative cancer with non-triple receptor-negative cancers (i.e., ER-positive, PR-positive, or HER2-positive or two of the three markers positive).
Conclusion:
Triple receptor-negative cancer was most commonly an irregular noncalcified mass with ill-defined or spiculated margins on mammography and a hypoechoic or complex mass with an irregular shape and noncircumscribed margins on ultrasound. Most triple receptor-negative cancers were discovered on physical examination. Compared with non-triple receptor-negative cancers, triple receptor-negative cancers were found in younger women and were a higher pathologic grade.
The aim of this study was to assess the performance of shear wave elastography combined with BI-RADS classification of greyscale ultrasound images for benign/malignant differentiation in a large group of patients.
One hundred and seventy-five consecutive patients with solid breast masses on routine ultrasonography undergoing percutaneous biopsy had the greyscale findings classified according to the American College of Radiology BI-RADS. The mean elasticity values from four shear wave images were obtained.
For mean elasticity vs greyscale BI-RADS, the performance results against histology were sensitivity: 95% vs 95%, specificity: 77% vs 69%, Positive Predictive Value (PPV): 88% vs 84%, Negative Predictive Value (NPV): 90% vs 91%, and accuracy: 89% vs 86% (all P>0.05). The results for the combination (positive result from either modality counted as malignant) were sensitivity 100%, specificity 61%, PPV 82%, NPV 100%, and accuracy 86%. The combination of BI-RADS greyscale and shear wave elastography yielded superior sensitivity to BI-RADS alone (P=0.03) or shear wave alone (P=0.03). The NPV was superior in combination compared with either alone (BI-RADS P=0.01 and shear wave P=0.02).
Together, BI-RADS assessment of greyscale ultrasound images and shear wave ultrasound elastography are extremely sensitive for detection of malignancy.
To determine whether adding shear-wave (SW) elastographic features could improve accuracy of ultrasonographic (US) assessment of breast masses.
From September 2008 to September 2010, 958 women consented to repeat standard breast US supplemented by quantitative SW elastographic examination in this prospective multicenter institutional review board-approved, HIPAA-compliant protocol. B-mode Breast Imaging Reporting and Data System (BI-RADS) features and assessments were recorded. SW elastographic evaluation (mean, maximum, and minimum elasticity of stiffest portion of mass and surrounding tissue; lesion-to-fat elasticity ratio; ratio of SW elastographic-to-B-mode lesion diameter or area; SW elastographic lesion shape and homogeneity) was performed. Qualitative color SW elastographic stiffness was assessed independently. Nine hundred thirty-nine masses were analyzable; 102 BI-RADS category 2 masses were assumed to be benign; reference standard was available for 837 category 3 or higher lesions. Considering BI-RADS category 4a or higher as test positive for malignancy, effect of SW elastographic features on area under the receiver operating characteristic curve (AUC), sensitivity, and specificity after reclassifying category 3 and 4a masses was determined.
Median participant age was 50 years; 289 of 939 (30.8%) masses were malignant (median mass size, 12 mm). B-mode BI-RADS AUC was 0.950; eight of 303 (2.6%) BI-RADS category 3 masses, 18 of 193 (9.3%) category 4a lesions, 41 of 97 (42%) category 4b lesions, 42 of 57 (74%) category 4c lesions, and 180 of 187 (96.3%) category 5 lesions were malignant. By using visual color stiffness to selectively upgrade category 3 and lack of stiffness to downgrade category 4a masses, specificity improved from 61.1% (397 of 650) to 78.5% (510 of 650) (P<.001); AUC increased to 0.962 (P=.005). Oval shape on SW elastographic images and quantitative maximum elasticity of 80 kPa (5.2 m/sec) or less improved specificity (69.4% [451 of 650] and 77.4% [503 of 650], P<.001 for both), without significant improvement in sensitivity or AUC.
Adding SW elastographic features to BI-RADS feature analysis improved specificity of breast US mass assessment without loss of sensitivity.
To evaluate intra- and interobserver reproducibility of shear wave elastography (SWE) for breast masses.
For intraobserver reproducibility, each observer obtained three consecutive SWE images of 758 masses that were visible on ultrasound. 144 (19%) were malignant. Weighted kappa was used to assess the agreement of qualitative elastographic features; the reliability of quantitative measurements was assessed by intraclass correlation coefficients (ICC). For the interobserver reproducibility, a blinded observer reviewed images and agreement on features was determined.
Mean age was 50 years; mean mass size was 13 mm. Qualitatively, SWE images were at least reasonably similar for 666/758 (87.9%). Intraclass correlation for SWE diameter, area and perimeter was almost perfect (ICC ≥ 0.94). Intraobserver reliability for maximum and mean elasticity was almost perfect (ICC = 0.84 and 0.87) and was substantial for the ratio of mass-to-fat elasticity (ICC = 0.77). Interobserver agreement was moderate for SWE homogeneity (κ = 0.57), substantial for qualitative colour assessment of maximum elasticity (κ = 0.66), fair for SWE shape (κ = 0.40), fair for B-mode mass margins (κ = 0.38), and moderate for B-mode mass shape (κ = 0.58), orientation (κ = 0.53) and BI-RADS assessment (κ = 0.59).
SWE is highly reproducible for assessing elastographic features of breast masses within and across observers. SWE interpretation is at least as consistent as that of BI-RADS ultrasound B-mode features.
• Shear wave ultrasound elastography can measure the stiffness of breast tissue • It provides a qualitatively and quantitatively interpretable colour-coded map of tissue stiffness • Intraobserver reproducibility of SWE is almost perfect while intraobserver reproducibility of SWE proved to be moderate to substantial • The most reproducible SWE features between observers were SWE image homogeneity and maximum elasticity.
Breast cancer is the second leading cause of cancer death in women in the United States. While mammography and breast magnetic resonance imaging (MRI) improve detection of early disease, there remains an unmet need for biomarkers for risk stratification, early detection, prediction, and disease prognosis. A number of early breast lesions, from atypical hyperplasias to carcinomas in situ, are associated with an increased risk of developing subsequent invasive breast carcinoma. The recent development of genomic, epigenomic, and proteomic tools for tissue biomarker detection, including array CGH, RNA expression microarrays, and proteomic arrays have identified a number of potential biomarkers that both identify patients at increased risk, as well as provided insights into the pathology of early breast cancer development. This chapter focuses on the detection and application of tissue and serum biomarkers for the identification and risk stratification of early breast cancer lesions.
Triple receptor-negative (TRN) breast cancer is associated with high risk of recurrence and poor prognosis. The present study assessed the clinicopathologic characteristics and ultrasound (US) features of TRN breast cancers.
Pathological and biological data were reviewed for 558 breast cancer patients treated at Kangbuk Samsung Hospital, between January 2003 and December 2009. The patients were separated into TRN breast cancer and non-TRN breast cancer groups, based on the results of immunohistochemical prognostic panels. Clinical and pathologic features were compared for the two groups. US features, including shape, orientation, margins, boundaries, echo patterns, posterior acoustic features, surrounding tissues, and microcalcifications, were determined for 41 TRN patients and 189 non-TRN controls (ER+/PR+/HER2-).
Of 558 cases, 58 (10.4%) had the TRN phenotype. Four hundred and thirty-four cases (77.8%) were invasive ductal carcinomas. TRN cancer was significantly associated with specific characteristics of tumor size, nuclear grade, histologic grade, venous invasion, and lymphatic invasion. With respect to US features, TRN cancers were more likely to have an oval shape, a circumscribed margin, and marked hypoechogenicity.
Tumor characteristics were different between TRN and non-TRN breast cancers, although US cannot differentiate the subtype of breast cancers TRN cancer tend to show somewhat different US morphology.
Shear wave elastography is a new method of obtaining quantitative tissue elasticity data during breast ultrasound examinations. The aims of this study were (1) to determine the reproducibility of shear wave elastography (2) to correlate the elasticity values of a series of solid breast masses with histological findings and (3) to compare shear wave elastography with greyscale ultrasound for benign/malignant classification.
Using the Aixplorer® ultrasound system (SuperSonic Imagine, Aix en Provence, France), 53 solid breast lesions were identified in 52 consecutive patients. Two orthogonal elastography images were obtained of each lesion. Observers noted the mean elasticity values in regions of interest (ROI) placed over the stiffest areas on the two elastography images and a mean value was calculated for each lesion. A sub-set of 15 patients had two elastography images obtained by an additional operator. Reproducibility of observations was assessed between (1) two observers analysing the same pair of images and (2) findings from two pairs of images of the same lesion taken by two different operators. All lesions were subjected to percutaneous biopsy. Elastography measurements were correlated with histology results. After preliminary experience with 10 patients a mean elasticity cut off value of 50 kilopascals (kPa) was selected for benign/malignant differentiation. Greyscale images were classified according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS). BI-RADS categories 1-3 were taken as benign while BI-RADS categories 4 and 5 were classified as malignant.
Twenty-three benign lesions and 30 cancers were diagnosed on histology. Measurement of mean elasticity yielded an intraclass correlation coefficient of 0.99 for two observers assessing the same pairs of elastography images. Analysis of images taken by two independent operators gave an intraclass correlation coefficient of 0.80. Shear wave elastography versus greyscale BI-RADS performance figures were sensitivity: 97% vs 87%, specificity: 83% vs 78%, positive predictive value (PPV): 88% vs 84%, negative predictive value (NPV): 95% vs 82% and accuracy: 91% vs 83% respectively. These differences were not statistically significant.
Shear wave elastography gives quantitative and reproducible information on solid breast lesions with diagnostic accuracy at least as good as greyscale ultrasound with BI-RADS classification.
The purpose of this study was to correlate sonographic and mammographic findings with prognostic factors in patients with node-negative invasive breast cancer.
Sonographic and mammographic findings in 710 consecutive patients (age range 21-81 years; mean age 49 years) with 715 node-negative invasive breast cancers were retrospectively evaluated. Pathology reports relating to tumour size, histological grade, lymphovascular invasion (LVI), extensive intraductal component (EIC), oestrogen receptor (ER) status and HER-2/neu status were reviewed and correlated with the imaging findings. Statistical analysis was performed using logistic regression analysis and intraclass correlation coefficient (ICC).
On mammography, non-spiculated masses with calcifications were associated with all poor prognostic factors: high histological grade, positive LVI, EIC, HER-2/neu status and negative ER. Other lesions were associated with none of these poor prognostic factors. Hyperdense masses on mammography, the presence of mixed echogenicity, posterior enhancement, calcifications in-or-out of masses and diffusely increased vascularity on sonography were associated with high histological grade and negative ER. Associated calcifications on both mammograms and sonograms were correlated with EIC and HER-2/neu overexpression. The ICC value for the disease extent was 0.60 on mammography and 0.70 on sonography.
Several sonographic and mammographic features can have a prognostic value in the subsequent treatment of patients with node-negative invasive breast cancer. Radiologists should pay more attention to masses that are associated with calcifications because on both mammography and sonography associated calcifications were predictors of positive EIC and HER-2/neu overexpression.
The American Joint Committee on Cancer and the International Union for Cancer Control update the tumor-node-metastasis (TNM) cancer staging system periodically. The most recent revision is the 7th edition, effective for cancers diagnosed on or after January 1, 2010. This editorial summarizes the background of the current revision and outlines the major issues revised. Most notable are the marked increase in the use of international datasets for more highly evidenced-based changes in staging, and the enhanced use of nonanatomic prognostic factors in defining the stage grouping. The future of cancer staging lies in the use of enhanced registry data standards to support personalization of cancer care through cancer outcome prediction models and nomograms.
Ultrasound is a useful adjunct to mammography for the characterisation and biopsy of solid breast lesions. Protein expression profiling of breast cancer has identified specific subgroups with potential clinical, biological and therapeutic implications. The aim of this study was to determine the ultrasound correlates of these novel molecular classes of invasive breast cancer. The ultrasound findings in 358 patients with operable breast cancer were correlated with the previously described protein expression classes identified by our group using immunohistochemical (IHC) assessment of a large series of breast cancer cases in which 25 proteins of known relevance in breast cancer were assessed, including hormone receptors, HER2 status, basal and luminal markers, p53 and e-cadherin. The proportion of occult lesions was not significantly different in the two groups. Significant differences were noted between the two groups expressing luminal epithelial markers and hormone receptors (1 and 2), including a greater proportion of ill-defined, irregular and distally attenuating tumours in group 2. Tumours characterised by c-erbB2/MUC1 expression, with weak hormone receptor positivity (group 3) were also more likely to be ill defined. Tumours expressing basal markers (group 5) were less likely to have an echogenic halo. The ultrasound features of breast cancer show areas of significant correlation with molecular classes of invasive breast cancer identified by IHC analysis. The biological reasons for these findings and their implications regarding imaging protocols require further study and may enable improved detection of these lesions.
To evaluate the diagnostic performance of real-time freehand elastography by using the extended combined autocorrelation method (CAM) to differentiate benign from malignant breast lesions, with pathologic diagnosis as the reference standard.
This study was approved by the University of Tsukuba Human Subjects Institutional Review Board; all patients gave informed consent. Conventional ultrasonography (US) and real-time US elastography with CAM were performed in 111 women (mean age, 49.4 years; age range, 27-91 years) who had breast lesions (59 benign, 52 malignant). Elasticity images were assigned an elasticity score according to the degree and distribution of strain induced by light compression. The area under the curve and cutoff point, both of which were obtained by using a receiver operating characteristic curve analysis, were used to assess diagnostic performance. Mean scores were examined by using a Student t test. Sensitivity, specificity, and accuracy were compared by using the standard proportion difference test or the Delta-equivalent test.
For elasticity score, the mean +/- standard deviation was 4.2 +/- 0.9 for malignant lesions and 2.1 +/- 1.0 for benign lesions (P < .001). When a cutoff point of between 3 and 4 was used, elastography had 86.5% sensitivity, 89.8% specificity, and 88.3% accuracy. When a best cutoff point of between 4 and 5 was used, conventional US had 71.2% sensitivity, 96.6% specificity, and 84.7% accuracy. Elastography had higher sensitivity than conventional US (P < .05). By using equivalence bands for noninferiority or equivalence, it was shown that the specificity of elastography was not inferior to that of conventional US and that the accuracy of elastography was equivalent to that of conventional US.
For assessing breast lesions, US elastography with the proposed imaging classification, which was simple compared with that of the Breast Imaging Recording and Data System classification, had almost the same diagnostic performance as conventional US.
Purpose
To determine the diagnostic performance of Acoustic Radiation Force Impulse (ARFI) Virtual Touch IQ shear wave elastography in the discrimination of benign and malignant breast lesions.
Materials and methods
Conventional B-mode and elasticity imaging were used to evaluate 110 breast lesions. Elastographic assessment of breast tissue abnormalities was done using a shear wave based technique, Virtual Touch IQ (VTIQ), implemented on a Siemens Acuson S3000 ultrasound machine. Tissue mechanical properties were interpreted as two dimensional qualitative and quantitative colour maps displaying relative shear wave velocity. Wave speed measurements in m/s were possible at operator defined regions of interest. The pathologic diagnosis was established on samples obtained by ultrasound guided core biopsy or fine needle aspiration.
Results
BIRADS based B-mode evaluation of the 48 benign and 62 malignant lesions achieved 92% sensitivity and 62.5% specificity. Subsequently performed VTIQ elastography relying on visual interpretation of the colour overlay displaying relative shear wave velocities managed similar standalone diagnostic performance with 92% sensitivity and 64.6% specificity. Lesion and surrounding tissue shear wave speed values were calculated and a significant difference was found between the benign and malignant populations (Mann Whitney U test, p< .0001). By selecting a lesion cut-off value of 3.31 m/s we achieved 80.4% sensitivity and 73% specificity. Applying this threshhold only to BIRADS 4a masses, we reached overall levels of 92% sensitivity and 72.9% specificity.
Conclusion
VTIQ qualitative and quantitative elastography has the potential to further characterise B-mode detected breast lesions, increasing specificity and reducing the number of unnecessary biopsies.
Objective:
To evaluate the correlation between stiffness values obtained by shear-wave elastography (SWE) and breast cancer subtypes.
Methods:
This was an institutional review board-approved retrospective study with a waiver of informed consent. The stiffness of 337 invasive breast cancers in 337 women was evaluated by SWE and mean stiffness values (kPa) and qualitative colour scores (1-5) of tumours were obtained. The results were analysed according to BI-RADS category, tumour size, grade and tumour subtype (triple-negative [TN], human epidermal growth factor receptor 2 [HER2]-positive, and oestrogen receptor [ER]-positive) using a multiple linear regression analysis.
Results:
The mean stiffness values and colour scores were: 146.8 kPa ± 57.0 and 4.1 ± 1.1; 165.8 kPa ± 48.5 and 4.6 ± 0.7 for TN tumours (n = 64), 160.3 kPa ± 56.2 and 4.3 ± 1.0 for HER2-positive tumours (n = 55) and 136.9 kPa ± 57.2 and 4.0 ± 1.1 for ER-positive tumours (n = 218; P < 0.0001). All three breast cancers classified as BI-RADS category 3 on B-mode ultrasound were TN subtype. A multiple linear regression analysis revealed that tumour size, histological grade and tumour subtype were independent factors that influenced the stiffness values.
Conclusion:
High stiffness values correlated with aggressive subtypes of breast cancer.
Key points:
• Shear-wave elastography is increasingly used to measure the stiffness of breast tumours. • Triple-negative and HER2-positive tumours showed greater stiffness than ER-positive tumours. • All breast cancers classified as BI-RADS 3 on B-mode ultrasound were triple-negative subtype. • Tumour size, histological grade and subtype were independent factors influencing SWE stiffness.
Background:
Elastographpy is a newly developed noninvasive imaging technique that uses ultrasound (US) to evaluate tissue stiffness. The interpretation of the same elastographic images may be variable according to reviewers. Because breast lesions are usually reported according to American College of Radiology Breast Imaging and Data System (ACR BI-RADS) lexicons and final category, we tried to compare observer variability between lexicons and final categorization of US BI-RADS and the elasticity score of US elastography.
Methods:
From April 2009 to February 2010, 1356 breast lesions in 1330 patients underwent ultrasound-guided core biopsy. Among them, 63 breast lesions in 55 patients (mean age, 45.7 years; range, 21-79 years) underwent both conventional ultrasound and elastography and were included in this study. Two radiologists independently performed conventional ultrasound and elastography, and another three observers reviewed conventional ultrasound images and elastography videos. Observers independently recorded the elasticity score for a 5-point scoring system proposed by Itoh et al., BI-RADS lexicons and final category using ultrasound BI-RADS. The histopathologic results were obtained and used as the reference standard. Interobserver variability was evaluated.
Results:
Of the 63 lesions, 42 (66.7 %) were benign, and 21 (33.3 %) were malignant. The highest value of concordance among all variables was achieved for the elasticity score (k = 0.59), followed by shape (k = 0.54), final category (k = 0.48), posterior acoustic features (k = 0.44), echogenecity and orientation (k = 0.43). The least concordances were margin (k = 0.26), lesion boundary (k = 0.29) and calcification (k = 0.3).
Conclusion:
Elasticity score showed a higher level of interobserver agreement for the diagnosis of breast lesions than BI-RADS lexicons and final category.
To compare the mean elasticity value, as measured by shear-wave elastography (SWE), with immunohistochemical profile of invasive breast cancer. This was an institutional review board-approved retrospective study, with a waiver of informed consent. A total of 166 invasive breast cancers in 152 women undergoing preoperative SWE and surgery were included. Quantitative mean elasticity values in kPa were measured for each lesion by using SWE. Medical records were reviewed to determine palpability, invasive size, lymphovascular invasion, histologic grade, and axillary lymph node status. Based on the immunohistochemical profiles, tumor subtypes were categorized as triple-negative (TN), luminal A and B, or human epidermal growth factor receptor 2-enriched cancer. The mean elasticity value was correlated with clinicopathological features using univariate regression models and multivariate linear regression analysis. Palpability (P < 0.0001), larger size (P = 0.013), lymphovascular invasion (P < 0.0001), higher histologic grade (P < 0.0001), and lymph node involvement (P = 0.018) were significantly associated with the mean elasticity value. For the immunohistochemical profiles and tumor subtypes, the estrogen receptor (P = 0.015), progesterone receptor (P = 0.002), Ki-67 (P = 0.009), and the TN (P = 0.009) tumor subtype were correlated with the mean elasticity value. Multivariate logistic regression analysis showed that the following variables were significantly associated with the mean elasticity value: palpable abnormality, histologic grade, and lymphovascular invasion. No immunohistochemical profile of the cancers was independently correlated with the mean elasticity value. For invasive breast cancers, clinicopathological features of poor prognosis showed higher mean elasticity values than those of good prognosis. However, the immunohistochemical profile showed no independent association with the mean elasticity value.
The purpose of this study was to compare the efficacy of the sonographic features in the BI-RADS lexicon for predicting malignancy grade of invasive ductal breast carcinoma in women assigned a BI-RADS category of 4 or 5.
Two radiologists retrospectively evaluated 299 consecutive cases of grades 1-3 invasive ductal breast carcinoma presenting as a mass in consensus by using the BI-RADS sonographic lexicon. Histologic grade was established on surgical specimens. Effect sizes were calculated via the Goodman and Kruskal tau, an asymmetric measure of strength of nominal association, and results were interpreted in terms of proportional reduction in error.
Thirty-eight lesions (13%) were grade 1, 153 (51%) were grade 2, and 108 (36%) were grade 3, with the majority of all masses showing an irregular shape (84%) and hypoechoic echotexture (82%). Of the sonographic features examined, malignancy grade was best predicted by posterior acoustics (τ = 0.13, p < 0.001), lesion boundary (τ = 0.05, p < 0.001), and margin (τ = 0.04, p = 0.001). Among grade 3 lesions, there were significantly more lesions with posterior enhancement (53 vs 27.6; adjusted standardized residuals (z(res)) = 7; p < 0.001), abrupt interfaces (68 vs 51.2; z(res) = 4; p < 0.001), and microlobulated margins (12 vs 5.8; z(res) = 3; p = 0.001) than would be expected.
Malignancy grade was slightly to moderately predicted by margin, lesion boundary, and acoustic sonographic features. In particular, grade 3 invasive ductal breast carcinomas were more likely than expected to display microlobulated margins, abrupt interfaces, and posterior enhancement.
We evaluated the diagnostic performance of elastography and tissue quantification using acoustic radiation force impulse (ARFI) technology for differential diagnosis of breast masses.
There were 161 mass lesions. First, lesion correspondence on ARFI elastographic images to those on the B-mode images was evaluated: no findings on ARFI images (pattern 1), lesions that were bright inside (pattern 2), lesions that were dark inside (pattern 4), lesions that contained both bright and dark areas (pattern 3). In addition, pattern 4 was subdivided into 4a (dark area same as B-mode lesion) and 4b (dark area larger than lesion). Next, shear wave velocity (SWV) was measured using virtual touch tissue quantification.
There were 13 pattern 1 lesions and five pattern 2 lesions; all of these lesions were benign, whereas all pattern 4b lesions (n = 43) were malignant. When the value of 3.59 m/s was chosen as the cutoff value, the combination of elastography and tissue quantification showed 91 % (83-91) sensitivity, 93 % (65-70) specificity, and 92 % (148-161) accuracy.
The combination of elastography and tissue quantification is thought to be a promising ultrasound technique for differential diagnosis of breast-mass lesions.
To determine the MRI features of triple-negative invasive breast cancer (TNBC) on dynamic contrast-enhanced MR imaging (DCE-MRI) and diffusion-weighted MR imaging (DWI) in comparison with ER-positive/HER2-negative (ER+) and HER2-positive cancer (HER2+).
A total of 271 invasive cancers in 269 patients undergoing preoperative MRI and surgery were included. Two radiologists retrospectively assessed morphological and kinetic characteristics on DCE-MRI and tumour detectability on DWI. Apparent diffusion coefficient (ADC) values of lesions were measured. Clinical and MRI features of the three subtypes were compared.
Compared with ER+ (n = 119) and HER2+ (n = 94), larger size, round/oval mass shape, smooth mass margin, and rim enhancement on DCE-MRI were significantly associated with TNBC (n = 58; P < 0.0001). On DWI, mean ADC value (× 10(-3) mm(2)/s) of TNBC (1.03) was higher than the mean ADC values for ER+ and HER2+ (0.89 and 0.84; P < 0.0001). There was no difference in tumour detectability (P = 0.099). Tumour size (P = 0.009), mass margin (smooth, P < 0.0001; irregular, P = 0.020), and ADC values (P = 0.002) on DCE-MRI and DWI were independent features of TNBC.
In addition to the morphological features, higher ADC values on DWI were independently associated with TNBC and could be useful in differentiating TNBC from ER+ and HER2+.
• Triple-negative breast cancers (TNBC) lack oestrogen/progesterone receptors and HER2 expression/amplification. • TNBCs are larger, better defined and more necrotic than conventional cancers. • On MRI, necrosis yields high T2-weighted signal intensity and ADCs. • High ADC values can be useful in diagnosing TNBC.
To compare the histologic prognostic feature of invasive breast cancer with mean stiffness as measured with shear-wave elastography.
This retrospective study was exempted from ethical committee review. Patient consent for use of images for research was obtained. The study group comprised 101 consecutive women (age range, 38-91 years) with solid lesions identified during routine breast ultrasonography (US) performed between April 2010 and March 2011 and subsequently confirmed at histologic examination to be invasive cancers. Four elastographic images in two orthogonal planes were obtained of each lesion, and mean stiffness values were obtained from each image. Histologic findings following surgery were used for comparison, namely histologic grade, tumor type, invasive size, vascular invasion status, and lymph node status. Relationship between mean stiffness and histologic parameters was investigated by using a general linear model and multiple regression analysis.
High histologic grade (P < .0001), large invasive size (P < .0001), lymph node involvement (P < .0001), tumor type (P < .0001), and vascular invasion (P = .0077) all showed statistically significant positive association with high mean stiffness values. Multiple linear regression indicated that invasive size is the strongest pathologic determinant of mean stiffness (P < .0001), with histologic grade also having significant influence (P = .022).
In this study, breast cancers with higher mean stiffness values at shear-wave elastography had poorer prognostic features.
Triple-negative breast cancers generally occur in young women and they have the potential to be aggressive. It is important for this subtype of tumour to be detected early. We studied the appearance of 73 tumours on mammography, sonography and MRI in order to determine what specific features they showed on imaging.
From July 2009 to December 2010, we retrospectively reviewed mammogram and sonogram images of 73 triple-negative cancers. Colour Doppler had been used to depict vascularisation in 34 cases and elastography score calculated in 17 cases. Sixteen patients had undergone MRI. The radiological description of these different modalities draws on the BI-RADS lexicon and categorisation.
On mammography, triple-negative cancers often presented as a round mass (59.3%) or an oval or lobulated mass (65%), with circumscribed (15%), microlobulated (12.5%), indistinct (55%) or occasionally spiculated margins (15%). On sonography, the vast majority of these cancers appeared as masses (92.8%) with occasional posterior acoustic attenuation (22.6%). MRI showed more suspicious images than the standard examinations, notably rim-enhancement (eight out of 12 masses).
. Radiological images appear as lobulated masses more readily, while on sonography posterior enhancement is shown more often than attenuation, and MRI finds rim-enhancement.
The emerging molecular breast cancer (BC) classification based on key molecules, including hormone receptors (HRs), and human epidermal growth factor receptor 2 (HER2) has been playing an important part of clinical practice guideline. The current molecular classification mainly based on their fingerprints, however, could not provide enough essential information for treatment decision making. The molecular information on both patterns and quantities could be more helpful to heterogeneities understanding for BC personalized medicine. Here we conduct quantitative determination of HRs and HER2 by quantum dots (QDs)-based quantitative spectral analysis, which had excellent consistence with traditional method. Moreover, we establish a new molecular classification system of BC by integrating the quantitative information of HER2 and HRs, which could better reveal BC heterogeneity and identify 5 molecular subtypes with different 5-year prognosis. Furthermore, the emerging 5 molecular subtypes based on simple quantitative molecules information could be as informative as multi-genes analysis in routine practice, and might help formulate a more personalized comprehensive therapy strategy and prognosis prediction.
Shear wave elastography (SWE) is an emerging technique which can obtain quantitative elasticity values in breast disease. We therefore evaluated the diagnostic performance of SWE for the differentiation of breast masses compared with conventional ultrasound (US). Conventional US and SWE were performed by three experienced radiologists for 158 consecutive women who had been scheduled for US-guided core biopsy or surgical excision in 182 breast masses (89 malignancies and 93 benign; mean size, 1.76 cm). For each lesion, quantitative elasticity was measured in terms of the Young's modulus (in kilopascals, kPa) with SWE, and BI-RADS final categories were assessed with conventional US. The mean elasticity values were significantly higher in malignant masses (153.3 kPa ± 58.1) than in benign masses (46.1 kPa ± 42.9), (P < 0.0001). The average mean elasticity values of invasive ductal (157.5 ± 57.07) or invasive lobular (169.5 ± 61.06) carcinomas were higher than those of ductal carcinoma in situ (117.8 kPa ± 54.72). The average mean value was 49.58 ± 43.51 for fibroadenoma, 35.3 ± 31.2 for fibrocystic changes, 69.5 ± 63.2 for intraductal papilloma, and 149.5 ± 132.4 for adenosis or stromal fibrosis. The optimal cut-off value, yielding the maximal sum of sensitivity and specificity, was 80.17 kPa, and the sensitivity and specificity of SWE were 88.8% (79 of 89) and 84.9% (79 of 93). The area under the ROC curve (Az value) was 0.898 for conventional US, 0.932 for SWE, and 0.982 for combined data. In conclusion, there were significant differences in the elasticity values of benign and malignant masses as well as invasive and intraductal cancers with SWE. Our results suggest that SWE has the potential to aid in the differentiation of benign and malignant breast lesions.
The management of breast cancer patients is still guided based on a constellation of clinicopathological features, including prognostic markers derived from careful histo-pathological analysis of tumours, namely tumour size, histological grade, presence of lymph node metastasis and vascular invasion [1-3]. Despite the huge amount of resources allocated to translational research endeavours, only three predictive markers are utilised to define the therapy of breast cancer patients: oestrogen receptor (ER) and progesterone receptor (PR), the predictive markers of response to endocrine therapy, and human epidermal growth factor receptor 2 (HER2), the molecular target of trastuzumab and lapatinib. These parameters are then used in conjunction either in the form of guidelines (for example, St Gallen's consensus criteria) or included in multivariable algorithms (for example, Adjuvant!Online) for clinical decision making [1-3]. Albeit seemingly simplistic, this approach has been shown to be clinically relevant, given that predictions made with Adjuvant!Online do correlate with the actual outcome of breast cancer patients [4], and, most importantly, the use of this framework to define the systemic therapy of breast cancer patients has contributed to the steady decline in the mortality of breast cancer patients [5]. Although eective, this approach is not sucient for the potential of individualised therapy to be realised.
The promise of high throughput technologies, and in particular of gene expression profiling with microarrays, has been of apocalyptic dimensions [6-9]. The objectivity of the methodology coupled with the elaborate, if not mind boggling [10], bioinformatic approaches to answer clinically relevant questions have led some of the proponents of this technology to compare histopathology with some rituals practiced by ancient tribes [7], and some experts in the field predicted back in 2000 that microarrays would make conventional diagnostic techniques obsolete [6].
Microarrays and their derivatives have undoubtedly contributed to our understanding of breast cancer (for reviews, see [1,2]). They have provided direct evidence to demonstrate that breast cancer is a heterogeneous disease at the molecular level [11], that ER-positive and -negative diseases are fundamentally different [11-14], that molecular subtypes of breast cancer do exist [11,15-18], and that some special histological types of breast cancer are distinct entities at the molecular level [19-22]. Furthermore, they have led to the development of a molecular taxonomy that is currently being tested in clinical trials [16], and of prognostic 'gene signatures', some of which have already been approved by the US Food and Drug Administration [1,2,13,23].
While significant advances have been made toward revealing the molecular mechanisms that influence breast cancer progression, much less is known about the associated cellular mechanical properties. To this end, we use particle-tracking microrheology to investigate the interplay among intracellular mechanics, three-dimensional matrix stiffness, and transforming potential in a mammary epithelial cell (MEC) cancer progression series. We use a well-characterized model system where human-derived MCF10A MECs overexpress either ErbB2, 14-3-3ζ, or both ErbB2 and 14-3-3ζ, with empty vector as a control. Our results show that MECs possessing ErbB2 transforming potential stiffen in response to elevated matrix stiffness, whereas non-transformed MECs or those overexpressing only 14-3-3ζ do no exhibit this response. We further observe that overexpression of ErbB2 alone is associated with the highest degree of intracellular sensitivity to matrix stiffness, and that the effect of transforming potential on intracellular stiffness is matrix-stiffness-dependent. Moreover, our intracellular stiffness measurements parallel cell migration behavior that has been previously reported for these MEC sublines. Given the current knowledge base of breast cancer mechanobiology, these findings suggest that there may be a positive relationship among intracellular stiffness sensitivity, cell motility, and perturbed mechanotransduction in breast cancer.
The classification of breast cancer into molecular subtypes with distinctive gene expression signatures that predict treatment response and prognosis has ushered in a new era of personalized medicine for this remarkably heterogeneous and deadly disease. Basal-like breast cancer (BLBC) is a particularly aggressive molecular subtype defined by a robust cluster of genes expressed by epithelial cells in the basal or outer layer of the adult mammary gland. BLBC is a major clinical challenge because these tumors are prevalent in young woman, often relapsing rapidly. Additionally, most (but not all) basal-like tumors lack expression of steroid hormone receptors (estrogen receptor and progesterone receptor) and human epidermal growth factor receptor 2, limiting targeted therapeutic options for these predominantly triple-negative breast cancers. This minireview will focus on new insights into the molecular etiology of these poor-prognosis tumors that underlie their intrinsic genomic instability, deregulated cell proliferation and apoptosis, and invasive tumor biology. We will also review ongoing efforts to translate these fundamental insights into improved therapies for women with BLBC.
To determine the appearance of breast lesions at quantitative ultrasonographic (US) elastography by using supersonic shear imaging (SSI) and to assess the correlation between quantitative values of lesion stiffness and pathologic results, which were used as the reference standard.
This study was approved by the French National Committee for the Protection of Patients Participating in Biomedical Research Programs. All patients provided written informed consent. Conventional US and SSI quantitative elastography were performed in 46 women (mean age, 57.6 years; age range, 38-71 years) with 48 breast lesions (28 benign, 20 malignant; mean size, 14.7 mm); pathologic results were available in all cases. Quantitative lesion elasticity was measured in terms of the Young modulus (in kilopascals). Sensitivity, specificity, and area under the curve were obtained by using a receiver operating characteristic curve analysis to assess diagnostic performance.
All breast lesions were detected at SSI. Malignant lesions exhibited a mean elasticity value of 146.6 kPa +/- 40.05 (standard deviation), whereas benign ones had an elasticity value of 45.3 kPa +/- 41.1 (P < .001). Complicated cysts were differentiated from solid lesions because they had elasticity values of 0 kPa (no signal was retrieved from liquid areas).
SSI provides quantitative elasticity measurements, thus adding complementary information that potentially could help in breast lesion characterization with B-mode US.
Prognostic and predictive factors in breast cancer by immunohistochemical analysis.
- Allred D.C.
- Harvey J.M.
- Berardo M.
- Clark G.M.
Personalizing the treatment of women with early breast cancer: Highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013
- Goldhirsch
Prognostic and predictive factors in breast cancer by immunohistochemical analysis
- Allred