Invasive carcinomas and fibroadenomas of the breast: comparison of microvessel distributions--implications for imaging modalities

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Utility of ultrasound Angio-PLUS imaging for detecting blood flow in breast masses and comparison with color Doppler for differentiating benign from malignant masses

BACKGROUND

Tumor neo-angiogenesis plays an important role in the development and growth of breast cancers, but its detection by imaging is challenging. A novel microvascular imaging (MVI) technique, Angio-PLUS, promises to overcome the limitations of color Doppler (CD) in detecting low-velocity flow and small diameter vessels.

PURPOSE

To determine the utility of the Angio-PLUS technique for detecting blood flow in breast masses and compare it with CD for differentiating benign from malignant masses.

MATERIAL AND METHODS

A total of 79 consecutive women with breast masses were prospectively evaluated using CD and Angio-PLUS techniques, and biopsied as per BI-RADS recommendations. Vascular imaging scores were assigned using three factors (number, morphology, and distribution) and vascular patterns were divided into five groups: internal-dot-spot, external-dot-spot, marginal, radial, and mesh patterns. The independent samples t-test, Mann-Whitney U test, Wilcoxon signed rank test, or Fisher's exact test were used to compare the two groups as appropriate. Area under the receiver operating characteristic (ROC) curve (AUC) methods were used to assess diagnostic accuracy.

RESULTS

Vascular scores were significantly higher on Angio-PLUS than CD (median=11, [IQR=9-13] vs. 5 [IQR=3-9], P < 0.001). Malignant masses had higher vascular scores than benign masses on Angio-PLUS (P < 0.001). AUC was 80% (95% CI=70.3-89.7; P < 0.001) for Angio-PLUS and 51.9% for CD. Using Angio-PLUS at a cutoff value of ≥9.5, sensitivity was 80% and specificity was 66.7%. Vascular pattern descriptors on AP showed good correlation with histopathological results (PPV mesh 95.5%, radial 96.9%, and NPV of marginal orientation 90.5%).

CONCLUSION

Angio-PLUS was more sensitive in detecting vascularity and superior in differentiating benign from malignant masses compared to CD. Vascular pattern descriptors on Angio-PLUS were useful.

Preoperative Prediction Power of Radiomics for Breast Cancer: A Systemic Review and Meta-Analysis

Background

To evaluate the preoperative predictive value of radiomics in the diagnosis of breast cancer (BC).

Methods

By searching PubMed and Embase libraries, our study identified 19 eligible studies. We conducted a meta-analysis to assess the differential value in the preoperative assessment of BC using radiomics methods.

Results

Nineteen radiomics studies focusing on the diagnostic efficacy of BC and involving 5865 patients were enrolled. The integrated sensitivity and specificity were 0.84 (95% CI: 0.80–0.87, I^{2 =} 76.44%) and 0.83 (95% CI: 0.78–0.87, I^{2 =} 81.79%), respectively. The AUC based on the SROC curve was 0.91, indicating a high diagnostic value.

Conclusion

Radiomics has shown excellent diagnostic performance in the preoperative prediction of BC and is expected to be a promising method in clinical practice.

Multi-Channel Local Binary Pattern Guided Convolutional Neural Network for Breast Cancer Classification

Background:

The advancement in convolutional neural network (CNN) has reduced the burden of experts using the computer-aided diagnosis of human breast cancer. However, most CNN networks use spatial features only. The inherent texture structure present in histopathological images plays an important role in distinguishing malignant tissues. This paper proposes an alternate CNN network that integrates Local Binary Pattern (LBP) based texture information with CNN features.

Methods:

The study propagates that LBP provides the most robust rotation, and translation-invariant features in comparison with other texture feature extractors. Therefore, a formulation of LBP in context of convolution operation is presented and used in the proposed CNN network. A non-trainable fixed set binary convolutional filters representing LBP features are combined with trainable convolution filters to approximate the response of the convolution layer. A CNN architecture guided by LBP features is used to classify the histopathological images.

Result:

The network is trained using BreKHis datasets. The use of a fixed set of LBP filters reduces the burden of CNN by minimizing training parameters by a factor of 9. This makes it suitable for the environment with fewer resources. The proposed network obtained 96.46% of maximum accuracy with 98.51% AUC and 97% F1-score.

Conclusion:

LBP based texture information plays a vital role in cancer image classification. A multi-channel LBP futures fusion is used in the CNN network. The experiment results propagate that the new structure of LBP-guided CNN requires fewer training parameters preserving the capability of the CNN network’s classification accuracy.

Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions

Background

While breast imaging such as full-field digital mammography and digital breast tomosynthesis have helped to reduced breast cancer mortality, issues with low specificity exist resulting in unnecessary biopsies. The fundamental information used in diagnostic decisions are primarily based in lesion morphology. We explore a dual-energy compositional breast imaging technique known as three-compartment breast (3CB) to show how the addition of compositional information improves malignancy detection.

Methods

Women who presented with Breast Imaging-Reporting and Data System (BI-RADS) diagnostic categories 4 or 5 and who were scheduled for breast biopsies were consecutively recruited for both standard mammography and 3CB imaging. Computer-aided detection (CAD) software was used to assign a morphology-based prediction of malignancy for all biopsied lesions. Compositional signatures for all lesions were calculated using 3CB imaging and a neural network evaluated CAD predictions with composition to predict a new probability of malignancy. CAD and neural network predictions were compared to the biopsy pathology.

Results

The addition of 3CB compositional information to CAD improves malignancy predictions resulting in an area under the receiver operating characteristic curve (AUC) of 0.81 (confidence interval (CI) of 0.74–0.88) on a held-out test set, while CAD software alone achieves an AUC of 0.69 (CI 0.60–0.78). We also identify that invasive breast cancers have a unique compositional signature characterized by reduced lipid content and increased water and protein content when compared to surrounding tissues.

Conclusion

Clinically, 3CB may potentially provide increased accuracy in predicting malignancy and a feasible avenue to explore compositional breast imaging biomarkers.

Leong et al. use a dual-energy mammography technique termed three-compartment breast imaging to study breast composition and detect malignant lesions. Combining compositional information with morphology-based computer-aided diagnosis improves breast cancer detection.

Breast cancers are detected by mammography. This study explored the use of a particular kind of mammography technique to obtain information about the composition of cancerous and non-cancerous breast tissue. This technique provided measures of lipid (fat), water, and protein content in addition to shape characteristics provided from standard mammography. Adding information about the composition of the tissue to its shape characteristics resulted in an increased ability to distinguish invasive cancerous tissue from unaffected surroundings. Invasive breast cancer tissues were also found to exhibit lower lipid, higher protein and higher water content when compared to other non-invasive, non-cancerous breast tissues in which cancer was suspected. Our findings highlight the added value of including the composition of breast tissue when deciding if biopsy of the suspicious tissue is warranted.

Quantitative Inflammation Assessment for Crohn Disease Using Ultrasensitive Ultrasound Microvessel Imaging: A Pilot Study

OBJECTIVES

Crohn disease (CD) is a chronic inflammation in the digestive tract that affects millions of Americans. Bowel vascularity has important diagnostic information because inflammation is associated with blood flow changes. We recently developed an ultrasensitive ultrasound microvessel imaging (UMI) technique with high vessel sensitivity. This study aimed to evaluate the feasibility of UMI to assist CD detection and staging.

METHODS

Ultrasound microvessel imaging was performed on 76 bowel wall segments from 48 symptomatic patients with CD. Clinically indicated computed tomographic/magnetic resonance enterography was used as the reference standard. The vessel-length ratio (VLR, the number of vessel pixels in the bowel wall segment normalized to the segment length) was derived in both conventional color flow imaging (CFI) and UMI to quantitatively stage disease activity. Receiver operating characteristic curves were then analyzed between different disease groups.

RESULTS

The VLR-CFI and VLR-UMI detected similar correlations between vascularization and disease activity: severe inflammation had a higher VLR than normal/mildly inflamed bowels (P < .05). No significant difference was found between quiescent and mild CD due to the small sample size. The VLR-CFI had more difficulties in distinguishing quiescent versus mild CD compared to the VLR-UMI. After combining the VLR-UMI with thickness, in the receiver operating characteristic curve analysis, the areas under the curves (AUCs) improved to AUC₁ = 0.996 for active versus quiescent CD, AUC₂ = 0.978 for quiescent versus mild CD, and AUC₃ = 0.931 for mild versus severe CD, respectively, compared to those using thickness alone (AUC₁ = 0.968; P = .04; AUC₂ = 0.919; P = .16; AUC₃ = 0.857; P = .01).

CONCLUSIONS

Ultrasound microvessel imaging offers a safe and cost-effective tool for CD diagnosis and staging, which may potentially assist disease activity classification and therapy efficacy evaluation.

Ultrasensitive Ultrasound Microvessel Imaging for Characterizing Benign and Malignant Breast Tumors

Tumor angiogenesis plays an important role during breast tumor growth. However, conventional Doppler has limited sensitivity to detect small blood vessels, resulting in a large overlap of Doppler features between benign and malignant tumors. An ultrasensitive ultrasound microvessel imaging (UMI) technique was recently developed. To evaluate the performance of UMI, we studied 44 patients with 51 breast masses. Tumor pathology served as the gold standard: 28 malignancies and 23 benignities. UMI provided a significant improvement in depicting smaller vessels compared with conventional Doppler. The microvessel morphologies observed on UMI were associated with tumor benign/malignant classification. The diagnostic accuracy of correct Breast Imaging Reporting and Data System (BI-RADS) classification rate (BI-RADS ≥4a: test positive; BI-RADS ≤3: test negative) as a fraction of total mass population was improved by 16% after combining conventional ultrasound with UMI compared with using conventional ultrasound alone. This improvement indicates the potential of UMI in reducing unnecessary benign biopsies and avoiding missed malignant biopsies.

Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set

Purpose To investigate the combination of mammography radiomics and quantitative three-compartment breast (3CB) image analysis of dual-energy mammography to limit unnecessary benign breast biopsies. Materials and Methods For this prospective study, dual-energy craniocaudal and mediolateral oblique mammograms were obtained immediately before biopsy in 109 women (mean age, 51 years; range, 31-85 years) with Breast Imaging Reporting and Data System category 4 or 5 breast masses (35 invasive cancers, 74 benign) from 2013 through 2017. The three quantitative compartments of water, lipid, and protein thickness at each pixel were calculated from the attenuation at high and low energy by using a within-image phantom. Masses were automatically segmented and features were extracted from the low-energy mammograms and the quantitative compartment images. Tenfold cross-validations using a linear discriminant classifier with predefined feature signatures helped differentiate between malignant and benign masses by means of (a) water-lipid-protein composition images alone, (b) mammography radiomics alone, and (c) a combined image analysis of both. Positive predictive value of biopsy performed (PPV₃) at maximum sensitivity was the primary performance metric, and results were compared with those for conventional diagnostic digital mammography. Results The PPV₃ for conventional diagnostic digital mammography in our data set was 32.1% (35 of 109; 95% confidence interval [CI]: 23.9%, 41.3%), with a sensitivity of 100%. In comparison, combined mammography radiomics plus quantitative 3CB image analysis had PPV₃ of 49% (34 of 70; 95% CI: 36.5%, 58.9%; P < .001), with a sensitivity of 97% (34 of 35; 95% CI: 90.3%, 100%; P < .001) and 35.8% (39 of 109) fewer total biopsies (P < .001). Conclusion Quantitative three-compartment breast image analysis of breast masses combined with mammography radiomics has the potential to reduce unnecessary breast biopsies. © RSNA, 2018 Online supplemental material is available for this article.

Comparison of the Diagnostic Performance of Power Doppler Ultrasound and a New Microvascular Doppler Ultrasound Technique (AngioPLUS) for Differentiating Benign and Malignant Breast Masses

This study was performed to compare the diagnostic performance of power Doppler ultrasound (US) and a new microvascular Doppler US technique (AngioPLUS; SuperSonic Imagine, Aix-en-Provence, France) for differentiating benign and malignant breast masses. Power Doppler US and AngioPLUS findings were available in 124 breast masses with confirmed pathologic results (benign, 80 [64.5%]; malignant, 44 [35.5%]). The diagnostic performance of each tool was calculated to distinguish benign from malignant masses using a receiver operating characteristic curve analysis and compared. The area under the curve showed that AngioPLUS was superior to power Doppler US in differentiating benign from malignant breast masses, but the difference was not statistically significant.

Angiogenesis Research in Mouse Mammary Cancer Based on Contrast-enhanced Ultrasonography: Exploratory Study

RATIONALE AND OBJECTIVES

The objective of this study was to investigate the contrast-enhanced ultrasound (CEUS) characteristics of tumor angiogenesis in mouse mammary cancer.

MATERIALS AND METHODS

Twenty-four mice were examined with ultrasound and CEUS at 2-12 days after implantation. Four to five mice were assessed daily, and one to three mice were then sacrificed for histology. All of the histologic slides were reviewed and correlated with CEUS findings.

RESULTS

A total of 46 cases of ultrasound examination had been performed in 24 mice. The mice were classified into three groups according to the tumor growth: group 1 (2~6 days after implantation, n = 20 cases), group 2 (7~9 days after implantation, n = 15 cases), and group 3 (10~12 days after implantation, n = 11 cases). In group 1, all tumors presented as a homogeneous hypoechoic mass with no color Doppler signals. However, three CEUS patterns were observed: 14 tumors presented as type I (peripheral ring enhancement with no enhancement within the tumor), 4 tumors presented as type II (peripheral ring enhancement with deep penetration), and 2 tumors presented as type III (homogeneous or heterogeneous enhancement in the entire tumor). In group 2, there was only difference in the echo (heterogeneous or not) and color Doppler signals (with or without) among the tumors in conventional ultrasound, but four CEUS patterns were observed and most presented as type III (53.3%, 8/15). In group 3, most tumors presented as a heterogeneous solid mass (81.8%, 9/11) with color signals (100%, 11/11), and almost all tumors presented as enhancement of type IV (peripheral ring enhancement with focal nodular enhancement) (90.9%, 10/11).The histologic results showed that the enhanced areas mainly corresponded to tumor cells, large tortuous vessels, and an inflammatory cell infiltrate. Nonenhanced areas corresponded to large areas of necrotic tissue or tumor cells, which arranged loosely with the small zone of necrosis.

CONCLUSIONS

CEUS could image the progression of vessel formation. Moreover, most importantly, CEUS is able to identify angiogenesis before the change of tumor color Doppler, and presents different enhanced patterns at different tumor growth times, which corresponded to tumor histologic features.

Differentiation between Pure Mucinous Breast Carcinomas and Fibroadenomas with Strong High-Signal Intensity on T2-Weighted Images from Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Objective

This study aimed to identify characteristics that can differentiate between pure mucinous breast carcinomas (PMBCs) and fibroadenomas (FAs) with strong high-signal intensity on T2-weighted images (T2-SHi) from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Methods

The DCE-MRI tumor characteristics were compared and analyzed between 35 PMBCs and 70 FAs with T2-SHi.

Results

Multivariate analysis revealed that delayed enhancement pattern was the only significant independent predictor (p = 0.007).

Conclusion

A delayed enhancement pattern is the most reliable characteristic for differentiating PMBCs from FAs with T2-SHi from DCE-MRI.

DCAN: Deep contour-aware networks for object instance segmentation from histology images

In histopathological image analysis, the morphology of histological structures, such as glands and nuclei, has been routinely adopted by pathologists to assess the malignancy degree of adenocarcinomas. Accurate detection and segmentation of these objects of interest from histology images is an essential prerequisite to obtain reliable morphological statistics for quantitative diagnosis. While manual annotation is error-prone, time-consuming and operator-dependant, automated detection and segmentation of objects of interest from histology images can be very challenging due to the large appearance variation, existence of strong mimics, and serious degeneration of histological structures. In order to meet these challenges, we propose a novel deep contour-aware network (DCAN) under a unified multi-task learning framework for more accurate detection and segmentation. In the proposed network, multi-level contextual features are explored based on an end-to-end fully convolutional network (FCN) to deal with the large appearance variation. We further propose to employ an auxiliary supervision mechanism to overcome the problem of vanishing gradients when training such a deep network. More importantly, our network can not only output accurate probability maps of histological objects, but also depict clear contours simultaneously for separating clustered object instances, which further boosts the segmentation performance. Our method ranked the first in two histological object segmentation challenges, including 2015 MICCAI Gland Segmentation Challenge and 2015 MICCAI Nuclei Segmentation Challenge. Extensive experiments on these two challenging datasets demonstrate the superior performance of our method, surpassing all the other methods by a significant margin.

Contrast-enhanced ultrasound improved performance of breast imaging reporting and data system evaluation of critical breast lesions

AIM: To determine whether contrast-enhanced ultrasound (CEUS) can improve the precision of breast imaging reporting and data system (BI-RADS) categorization.

METHODS: A total of 230 patients with 235 solid breast lesions classified as BI-RADS 4 on conventional ultrasound were evaluated. CEUS was performed within one week before core needle biopsy or surgical resection and a revised BI-RADS classification was assigned based on 10 CEUS imaging characteristics. Receiver operating characteristic curve analysis was then conducted to evaluate the diagnostic performance of CEUS-based BI-RADS assignment with pathological examination as reference criteria.

RESULTS: The CEUS-based BI-RADS evaluation classified 116/235 (49.36%) lesions into category 3, 20 (8.51%), 13 (5.53%) and 12 (5.11%) lesions into categories 4A, 4B and 4C, respectively, and 74 (31.49%) into category 5. Selecting CEUS-based BI-RADS category 4A as an appropriate cut-off gave sensitivity and specificity values of 85.4% and 87.8%, respectively, for the diagnosis of malignant disease. The cancer-to-biopsy yield was 73.11% with CEUS-based BI-RADS 4A selected as the biopsy threshold compared with 40.85% otherwise, while the biopsy rate was only 42.13% compared with 100% otherwise. Overall, only 4.68% of invasive cancers were misdiagnosed.

CONCLUSION: This pilot study suggests that evaluation of BI-RADS 4 breast lesions with CEUS results in reduced biopsy rates and increased cancer-to-biopsy yields.

Predictive model for contrast-enhanced ultrasound of the breast: Is it feasible in malignant risk assessment of breast imaging reporting and data system 4 lesions?

AIM: To build and evaluate predictive models for contrast-enhanced ultrasound (CEUS) of the breast to distinguish between benign and malignant lesions.

METHODS: A total of 235 breast imaging reporting and data system (BI-RADS) 4 solid breast lesions were imaged via CEUS before core needle biopsy or surgical resection. CEUS results were analyzed on 10 enhancing patterns to evaluate diagnostic performance of three benign and three malignant CEUS models, with pathological results used as the gold standard. A logistic regression model was developed basing on the CEUS results, and then evaluated with receiver operating curve (ROC).

RESULTS: Except in cases of enhanced homogeneity, the rest of the 9 enhancement appearances were statistically significant (P < 0.05). These 9 enhancement patterns were selected in the final step of the logistic regression analysis, with diagnostic sensitivity and specificity of 84.4% and 82.7%, respectively, and the area under the ROC curve of 0.911. Diagnostic sensitivity, specificity, and accuracy of the malignant vs benign CEUS models were 84.38%, 87.77%, 86.38% and 86.46%, 81.29% and 83.40%, respectively.

CONCLUSION: The breast CEUS models can predict risk of malignant breast lesions more accurately, decrease false-positive biopsy, and provide accurate BI-RADS classification.

A Heuristic Framework for Image Filtering and Segmentation: Application to Blood Vessel Immunohistochemistry

The blood vessel density in a cancerous tissue sample may represent increased levels of tumor growth. However, identifying blood vessels in the histological (tissue) image is difficult and time-consuming and depends heavily on the observer's experience. To overcome this drawback, computer-aided image analysis frameworks have been investigated in order to boost object identification in histological images. We present a novel algorithm to automatically abstract the salient regions in blood vessel images. Experimental results show that the proposed framework is capable of deriving vessel boundaries that are comparable to those demarcated manually, even for vessel regions with weak contrast between the object boundaries and background clutter.

Ultrasound imaging of breast tumor perfusion and neovascular morphology

A novel image processing strategy is detailed for simultaneous measurement of tumor perfusion and neovascular morphology parameters from a sequence of dynamic contrast-enhanced ultrasound (DCE-US) images. After normalization and tumor segmentation, a global time-intensity curve describing contrast agent flow was analyzed to derive surrogate measures of tumor perfusion (i.e., peak intensity, time-to-peak intensity, area under the curve, wash-in rate, wash-out rate). A maximum intensity image was generated from these same segmented image sequences, and each vascular component was skeletonized via a thinning algorithm. This skeletonized data set and collection of vessel segments were then investigated to extract parameters related to the neovascular network and physical architecture (i.e., vessel-to-tissue ratio, number of bifurcations, vessel count, average vessel length and tortuosity). An efficient computation of local perfusion parameters was also introduced and operated by averaging time-intensity curve data over each individual neovascular segment. Each skeletonized neovascular segment was then color-coded by these local measures to produce a parametric map detailing spatial properties of tumor perfusion. Longitudinal DCE-US image data sets were collected in six patients diagnosed with invasive breast cancer using a Philips iU22 ultrasound system equipped with a L9-3 transducer and Definity contrast agent. Patients were imaged using US before and after contrast agent dosing at baseline and again at weeks 6, 12, 18 and 24 after treatment started. Preliminary clinical results suggested that breast tumor response to neoadjuvant chemotherapy may be associated with temporal and spatial changes in DCE-US-derived parametric measures of tumor perfusion. Moreover, changes in neovascular morphology parametric measures may also help identify any breast tumor response (or lack thereof) to systemic treatment. Breast cancer management from early detection to therapeutic monitoring is currently undergoing profound changes. Novel imaging techniques that are sensitive to the unique biological conditions of each individual tumor represent valuable tools in the pursuit of personalized medicine.

Automated discrimination of lower and higher grade gliomas based on histopathological image analysis

INTRODUCTION

Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides for diagnosis remain an area of significant research activity. In this work, we focus on automated brain cancer grading, specifically glioma grading. Grading of a glioma is a highly important problem in pathology and is largely done manually by medical experts based on an examination of pathology slides (images). To complement the efforts of clinicians engaged in brain cancer diagnosis, we develop novel image processing algorithms and systems to automatically grade glioma tumor into two categories: Low-grade glioma (LGG) and high-grade glioma (HGG) which represent a more advanced stage of the disease.

RESULTS

We propose novel image processing algorithms based on spatial domain analysis for glioma tumor grading that will complement the clinical interpretation of the tissue. The image processing techniques are developed in close collaboration with medical experts to mimic the visual cues that a clinician looks for in judging of the grade of the disease. Specifically, two algorithmic techniques are developed: (1) A cell segmentation and cell-count profile creation for identification of Pseudopalisading Necrosis, and (2) a customized operation of spatial and morphological filters to accurately identify microvascular proliferation (MVP). In both techniques, a hierarchical decision is made via a decision tree mechanism. If either Pseudopalisading Necrosis or MVP is found present in any part of the histopathology slide, the whole slide is identified as HGG, which is consistent with World Health Organization guidelines. Experimental results on the Cancer Genome Atlas database are presented in the form of: (1) Successful detection rates of pseudopalisading necrosis and MVP regions, (2) overall classification accuracy into LGG and HGG categories, and (3) receiver operating characteristic curves which can facilitate a desirable trade-off between HGG detection and false-alarm rates.

CONCLUSION

The proposed method demonstrates fairly high accuracy and compares favorably against best-known alternatives such as the state-of-the-art WND-CHARM feature set provided by NIH combined with powerful support vector machine classifier. Our results reveal that the proposed method can be beneficial to a clinician in effectively separating histopathology slides into LGG and HGG categories, particularly where the analysis of a large number of slides is needed. Our work also reveals that MVP regions are much harder to detect than Pseudopalisading Necrosis and increasing accuracy of automated image processing for MVP detection emerges as a significant future research direction.

Histopathological image analysis for centroblasts classification through dimensionality reduction approaches

We present two novel automated image analysis methods to differentiate centroblast (CB) cells from noncentroblast (non-CB) cells in digital images of H&E-stained tissues of follicular lymphoma. CB cells are often confused by similar looking cells within the tissue, therefore a system to help their classification is necessary. Our methods extract the discriminatory features of cells by approximating the intrinsic dimensionality from the subspace spanned by CB and non-CB cells. In the first method, discriminatory features are approximated with the help of singular value decomposition (SVD), whereas in the second method they are extracted using Laplacian Eigenmaps. Five hundred high-power field images were extracted from 17 slides, which are then used to compose a database of 213 CB and 234 non-CB region of interest images. The recall, precision, and overall accuracy rates of the developed methods were measured and compared with existing classification methods. Moreover, the reproducibility of both classification methods was also examined. The average values of the overall accuracy were 99.22% ± 0.75% and 99.07% ± 1.53% for COB and CLEM, respectively. The experimental results demonstrate that both proposed methods provide better classification accuracy of CB/non-CB in comparison with the state of the art methods.

Diagnostic efficacy of contrast-enhanced sonography by combined qualitative and quantitative analysis in breast lesions: a comparative study with magnetic resonance imaging

OBJECTIVES

The purpose of this study was to evaluate the diagnostic efficacy of contrast-enhanced sonography for differentiation of breast lesions by combined qualitative and quantitative analyses in comparison to magnetic resonance imaging (MRI).

METHODS

Fifty-six patients with American College of Radiology Breast Imaging Reporting and Data System category 3 to 5 breast lesions on conventional sonography were evaluated by contrast-enhanced sonography and MRI. A comparative analysis of diagnostic results between contrast-enhanced sonography and MRI was conducted in light of the pathologic findings.

RESULTS

Pathologic analysis showed 26 benign and 30 malignant lesions. The predominant enhancement patterns of the benign lesions on contrast-enhanced sonography were homogeneous, centrifugal, and isoenhancement or hypoenhancement, whereas the patterns of the malignant lesions were mainly heterogeneous, centripetal, and hyperenhancement. The detection rates for perfusion defects and peripheral radial vessels in the malignant group were much higher than those in the benign group (P < .05). As to quantitative analysis, statistically significant differences were found in peak and time-to-peak values between the groups (P < .05). With pathologic findings as the reference standard, the sensitivity, specificity, and accuracy of contrast-enhanced sonography and MRI were 90.0%, 92.3%, 91.1% and 96.7%, 88.5%, and 92.9%, respectively. The two methods had a concordant rate of 87.5% (49 of 56), and the concordance test gave a value of κ = 0.75, indicating that there was high concordance in breast lesion assessment between the two diagnostic modalities.

CONCLUSIONS

Contrast-enhanced sonography provided typical enhancement patterns and valuable quantitative parameters, which showed good agreement with MRI in diagnostic efficacy and may potentially improve characterization of breast lesions.

Quantitative mapping of tumor vascularity using volumetric contrast-enhanced ultrasound

OBJECTIVE

The goal of this research project was to develop a volumetric strategy for real-time monitoring and characterization of tumor blood flow using microbubble contrast agents and ultrasound (US) imaging.

MATERIALS AND METHODS

Volumetric contrast-enhanced US (VCEUS) imaging was implemented on a SONIX RP US system (Ultrasonix Medical Corp, Richmond, BC) equipped with a broadband 4DL14-5/38 probe. Using a microbubble-sensitive harmonic imaging mode (transducer transmits at 5 MHz and receives at 10 MHz), acquisition of postscan-converted VCEUS data was achieved at a volume rate of 1 Hz. After microbubble infusion, custom data processing software was used to derive microbubble time-intensity curve-specific parameters, namely, blood volume (IPK), transit time (T1/2PK), flow rate (SPK), and tumor perfusion (AUC).

RESULTS

Using a preclinical breast cancer animal model, it is shown that millimeter-sized deviations in transducer positioning can have profound implications on US-based blood flow estimators, with errors ranging from 6.4% to 40.3% and dependent on both degree of misalignment (offset) and particular blood flow estimator. These errors indicate that VCEUS imaging should be considered in tumor analyses, because they incorporate the entire mass and not just a representative planar cross-section. After administration of an antiangiogenic therapeutic drug (bevacizumab), tumor growth was significantly retarded compared with control tumors (P > 0.03) and reflects observed changes in VCEUS-based blood flow measurements. Analysis of immunohistologic data revealed no differences in intratumoral necrosis levels (P = 0.70), but a significant difference was found when comparing microvessel density counts in control with therapy group tumors (P = 0.05).

CONCLUSIONS

VCEUS imaging was shown to be a promising modality for monitoring changes in tumor blood flow. Preliminary experimental results are encouraging, and this imaging modality may prove clinically feasible for detecting and monitoring the early antitumor effects in response to cancer drug therapy.

Detecting angiogenesis in breast tumors: comparison of color Doppler flow imaging with ultrasound-guided diffuse optical tomography

We investigated the correlation between color Doppler flow imaging (CDFI) and ultrasound (US)-guided diffuse optical tomography (DOT) for detection of breast tumor angiogenesis. Both CDFI and DOT were performed in 214 breast lesions scheduled for biopsy. The lesions were classified as vascular or nonvascular on CDFI and total hemoglobin concentration (THC) was measured by DOT. Sonographic results were correlated with the THC measurements. Pathologic examination showed 118 breast cancers and 96 benign breast masses. When vascularization on CDFI as a sign of malignancy and a cutoff of 140 μmol/L was used, the sensitivity, specificity and accuracy were 83.9, 50.0 and 68.7% for CDFI and 83.9, 66.7 and 76.2% for DOT, respectively. Thirteen (11.0%) nonvascular breast cancers presented high THC levels. Twenty-five (52.1%) vascular benign tumors demonstrated low THC levels. Mean THC did not differ significantly in malignancies with vascular or without vascular (228.14 ± 85.37 μmol/L vs. 191.42 ± 92.59 μmol/L; p > 0.05). Likewise, for benign lesions, the difference between THC values in vascular lesions and nonvascular lesions was not statistically significant (140.86 ± 79.63 μmol/L vs. 110.13 ± 85.05 μmol/L; p > 0.05). Our results suggest that the addition of DOT to CDFI could be helpful for characterizing CDFI nonvascular lesions that are suspicious for malignancy or vascular lesions that are probably benign.

Histopathological image analysis: a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.

Histopathological image analysis: a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.

A wide variety of dynamic contrast-enhanced MR appearances of breast cancer: pathologic correlation study

PURPOSE

The aim of this study was to elucidate the characteristic magnetic resonance (MR) appearance of breast cancers, as well as, its variations and to investigate the pathology providing different patterns of dynamic-MR appearances.

MATERIALS AND METHODS

Fifty-two women with cancer underwent mastectomy (52 tumors resected) and had MR imaging at our institution between April 2001 and March 2004. MR images of T1WI, T2WI, dynamic-MRI and contrast-enhanced T1WI were obtained and evaluated. Dynamic-MR images were correlated with pathological findings.

RESULTS

Common MR appearance of breast cancer was a focal mass either with irregular or spiculated margins with similar signal intensity on T1WI as and similar to higher signal intensity on T2WI compared to the normal mammary gland. On static contrast-enhanced T1WI, apparent enhancement was typically observed. On dynamic MRI, tumor-rim-enhancement on an early phase image and washout enhancement pattern on dynamic images, both characteristic for breast cancer, were observed, however, the prevalence of them was relatively low, which could be explained by the variation of histopathology among breast cancer nodules.

CONCLUSION

In diagnosing breast masses on MRI, as well as the common and characteristic findings of breast cancer, the variations of MR findings and their underlying histopathology should also be considered.

Breast carcinomas with strong high-signal intensity on T2-weighted MR images: Pathological characteristics and differential diagnosis

PURPOSE

To investigate the histopathological characteristics of breast carcinomas with strong high-signal intensity (SHi) on T2-weighted (T2W) MR images (T2-SHi), and discuss the differential diagnosis between T2-SHi breast carcinomas and T2-SHi fibroadenomas.

MATERIALS AND METHODS

Thirty of 480 breast carcinomas examined by MRI were defined as tumors with T2-SHi (defined as homogeneous higher signal intensity (SI) compared to surrounding normal breast tissue on fat-suppressed T2W imaging (T2WI). They included eight mucinous and 22 nonmucinous carcinomas. The histopathological characteristics of T2-SHi breast carcinomas, their signal-to-noise ratios (SNRs) on T2WI, contrast-enhancement patterns, and morphology were compared with those of 22 non-T2-SHi breast carcinomas and 19 T2-SHi fibroadenomas.

RESULTS

In nonmucinous carcinomas T2-SHi was attributable to a mixture of background matrix, a higher proportion of cells than stroma, abundant cytoplasm, edematous stroma, and hemorrhage. The significantly high SNR (mean = 75) and enhancing internal septations seen in mucinous carcinomas, and the washout phenomenon, irregular border, absence of internal septation, and rim enhancement seen in nonmucinous carcinomas provide useful information for differentiating these tumors from T2-SHi fibroadenomas.

CONCLUSION

A mixture of several histopathological characteristics was associated with T2-SHi breast carcinomas. The combined information from T2WI and contrast-enhanced (CE) imaging may help distinguish T2-SHi breast carcinomas from T2-SHi fibroadenomas.

Diagnosis of suspicious breast lesions using an empirical mathematical model for dynamic contrast-enhanced MRI

The purpose of this study was to test whether an empirical mathematical model (EMM) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can distinguish between benign and malignant breast lesions. A modified clinical protocol was used to improve the sampling of contrast medium uptake and washout. T(1)-weighted DCE magnetic resonance images were acquired at 1.5 T for 22 patients before and after injection of Gd-DTPA. Contrast medium concentration as a function of time was calculated over a small region of interest containing the most rapidly enhancing pixels. Then the curves were fitted with the EMM, which accurately described contrast agent uptake and washout. Results demonstrate that benign lesions had uptake (P<2.0 x 10(-5)) and washout (P<.01) rates of contrast agent significantly slower than those of malignant lesions. In addition, secondary diagnostic parameters, such as time to peak of enhancement, enhancement slope at the peak and curvature at the peak of enhancement, were derived mathematically from the EMM and expressed in terms of primary parameters. These diagnostic parameters also effectively differentiated benign from malignant lesions (P<.03). Conventional analysis of contrast medium dynamics, using a subjective classification of contrast medium kinetics in lesions as "washout," "plateau" or "persistent" (sensitivity=83%, specificity=50% and diagnostic accuracy=72%), was less effective than the EMM (sensitivity=100%, specificity=83% and diagnostic accuracy=94%) for the separation of benign and malignant lesions. In summary, the present research suggests that the EMM is a promising alternative method for evaluating DCE-MRI data with improved diagnostic accuracy.

Dynamic MR imaging of breast lesions: correlation with microvessel distribution pattern and histologic characteristics of prognosis

PURPOSE

To evaluate the association of dynamic enhancement parameters of benign and malignant breast lesions at magnetic resonance (MR) imaging with microvessel distribution and histologic prognostic tumor characteristics.

MATERIALS AND METHODS

Regional review board approval and informed consent were obtained. Surgical resection specimens of breast lesions (32 benign, 86 malignant) in 118 patients (age range, 28-86 years; mean, 58 years) who had undergone dynamic T1-weighted MR imaging of both breasts were included in the study. Different MR enhancement parameters and microvessel density (MVD) distribution were determined. In malignant lesions, TNM stage, tumor grade, proliferative activity, and hormone receptor expression were determined. Spearman correlation coefficients; Wilcoxon, Fisher exact, Kruskal Wallis, and chi(2) tests; and logistic regression analysis were used for evaluation.

RESULTS

Malignant lesions exhibited a higher ratio of microvessels in tumor periphery versus tumor center than did benign lesions (P < .0005). High vessel ratios (P = .001) and low central vessel numbers (P = .007) were associated with high tumor grade. In malignant lesions, initial enhancement ratios of periphery to center of lesion correlated with the corresponding microvessel ratios (r = 0.61). Yet, a high peripheral MVD was not associated with strong peripheral enhancement (r = -0.09). High enhancement ratios, washout rates, and early enhancement peaks were associated with unfavorable, albeit not significant, prognostic indicators. Visible rim enhancement was the most accurate prognostic enhancement criterion for estrogen receptor status (P = .007), tumor grade (P = .06), and lymph node status (P = .046). Washout was the best discriminating criterion for proliferative activity.

CONCLUSION

The different enhancement behaviors of malignant and benign breast lesions cannot be explained by MVD alone; however, a low MVD in the center of carcinoma is reflected quantitatively by a high enhancement ratio and qualitatively by rim enhancement, with an implication of adverse prognosis.

Magnetic resonance imaging reveals functional diversity of the vasculature in benign and malignant breast lesions

BACKGROUND

Tumor perfusion through the microvascular network can be imaged noninvasively by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The objective of the current study was to quantify the microvascular perfusion parameters in various human breast lesions and to determine whether they varied between benign lesions and malignancy and whether they were altered with increased invasiveness.

METHODS

Perfusion parameters in 22 benign fibrocystic changes, 15 ductal carcinomas in situ (DCIS), 30 infiltrating ductal carcinomas (IDC), and 22 fibroadenomas were measured using high-resolution DCE-MRI. Pixel-by-pixel image analysis yielded parametric images of two perfusion indicators: the influx transcapillary transfer constant (k(trans)) and the efflux transcapillary rate constant (k(ep)). Correlations of lesion type and perfusion parameters were calculated using Spearman correlation. Logistic regression analysis evaluated the best predictors of the kinetic parameters that differentiate between IDC and benign lesions.

RESULTS

The perfusion parameters exhibited a progressive increase from benign fibrocystic changes to DCIS and IDC, with a significant correlation between lesion type and the parameters' values (range of correlation coefficients, 0.56-0.76; P < 0.0001). In addition, k(trans) increased from low-grade DCIS to high-grade DCIS. Fibroadenomas were characterized uniquely by high k(trans) but low k(ep). Stepwise logistic regression selected k(trans) as the best predictor for distinguishing benign fibrocystic changes from IDC, yielding 93% sensitivity and 96% specificity.

CONCLUSIONS

The microvascular perfusion parameters in breast lesions were elevated with invasiveness. Quantification of these parameters using high-resolution DCE-MRI was helpful for differentiating between breast lesions and should improve breast carcinoma diagnosis.

Antivascular cancer treatments: functional assessments by dynamic contrast-enhanced magnetic resonance imaging

New anticancer therapeutics that target tumor blood vessels promise improved efficacy and tolerability in humans. Early phase 1 drug trials have shown that the maximum tolerated dose may be inappropriate for more advanced clinical studies with efficacy endpoints. More advanced clinical trials have demonstrated that morphologic assessments of tumor response are of limited value for gauging the efficacy of treatment. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can serve as pharmacodynamic indicator of biological activity for antivascular cancer drugs by helping to define the biologically active dose. DCE-MRI studies may also predict the efficacy of treatment on the basis of changes observed. If DCE-MRI is to be used for the selection of antivascular drugs that advance into efficacy trials, then it will be necessary to develop standardized approaches to measurement and robust analytic approaches with clear accepted endpoints specified prospectively that have biological validity. Such developments will be essential for multicenter trials in which it will be necessary to establish effective cross-site standardization of measurements and evaluation.

Expression of hypoxia-inducible factor 1 alpha and its downstream targets in fibroepithelial tumors of the breast

Introduction

Hypoxia-inducible factor 1 (HIF-1) alpha and its downstream targets carbonic anhydrase IX (CAIX) and vascular endothelial growth factor (VEGF) are key factors in the survival of proliferating tumor cells in a hypoxic microenvironment. We studied the expression and prognostic relevance of HIF-1α and its downstream targets in phyllodes tumors and fibroadenomas of the breast.

Methods

The expression of HIF-1α, CAIX, VEGF and p53 was investigated by immunohistochemistry in a group of 37 primary phyllodes tumors and 30 fibroadenomas with known clinical follow-up. The tumor microvasculature was visualized by immunohistochemistry for CD31. Proliferation was assessed by Ki67 immunostaining and mitotic counts. Being biphasic tumors, immunoquantification was performed in the stroma and epithelium.

Results

Only two fibroadenomas displayed low-level stromal HIF-1α reactivity in the absence of CAIX expression. Stromal HIF-1α expression was positively correlated with phyllodes tumor grade (P = 0.001), with proliferation as measured by Ki67 expression (P < 0.001) and number of mitoses (P < 0.001), with p53 accumulation (P = 0.003), and with global (P = 0.015) and hot-spot (P = 0.031) microvessel counts, but not with CAIX expression. Interestingly, concerted CAIX and HIF-1α expression was frequently found in morphologically normal epithelium of phyllodes tumors. The distance from the epithelium to the nearest microvessels was higher in phyllodes tumors as compared with in fibroadenomas. Microvessel counts as such did not differ between fibroadenomas and phyllodes tumors, however. High expression of VEGF was regularly found in both tumors, with only a positive relation between stromal VEGF and grade in phyllodes tumors (P = 0.016). Stromal HIF-1α overexpression in phyllodes tumors was predictive of disease-free survival (P = 0.032).

Conclusion

These results indicate that HIF-1α expression is associated with diminished disease-free survival and may play an important role in stromal progression of breast phyllodes tumors. In view of the absence of stromal CAIX expression in phyllodes tumors, stromal upregulation of HIF-1α most probably arises from hypoxia-independent pathways, with p53 inactivation as one possible cause. In contrast, coexpression of HIF-1α and CAIX in the epithelium in phyllodes tumors points to epithelial hypoxia, most probably caused by relatively distant blood vessels. On the other hand, HIF-1α and CAIX seem to be of minor relevance in breast fibroadenomas.

The use of unenhanced Doppler sonography in the evaluation of solid breast lesions

OBJECTIVE

The objectives of our study were to investigate differences in Doppler sonography features between benign and malignant breast lesions and between malignant lesions with different prognostic factors and to propose diagnostic criteria for Doppler sonography of breast lesions.

SUBJECTS AND METHODS

We performed power and duplex Doppler sonography examinations in 826 breast lesions scheduled for sonographically guided core needle biopsy. Lesion vascularity, pulsatility index (PI), and resistive index (RI) of the vessels detected were analyzed and correlated with histologic results.

RESULTS

Color flow was more frequently seen in malignant (237/348 lesions, 68%) than in benign (171/478, 36%) lesions (p < 0.001). However, sensitivity, specificity, and positive and negative predictive values for this sign were low (68%, 64%, 58%, and 73%, respectively). The RI and PI values were significantly higher (p < 0.001) in cancers. Although an overlap in these values between benign and malignant lesions was observed, all but one nodule with an RI of greater than 0.99 (those with null or inverted diastolic flow) or a PI of greater than 4 were malignant. No significant relationship was found between PI, RI, or flow visualization on power Doppler sonography and tumor grade or lymph node involvement in cancers.

CONCLUSION

Flow visualization on power Doppler sonography indicates a higher possibility of malignancy but is not useful as the main sign for malignancy. However, any lesion with a vessel that has an RI value greater than 0.99 or a PI value greater than 4 within it must be considered as probably malignant regardless of any other sonography sign present. Doppler findings are not useful to predict tumor grade or lymph node involvement.

MRI for assessing antivascular cancer treatments

Selective antiangiogenesis and vascular targeting drugs hold out the promise of improved efficacy and tolerability for anticancer treatments. Early phase 1 drug trials have shown good tolerability for antiangiogenesis agents with biological activity below the maximum tolerated dose. Advanced clinical trials have demonstrated that morphological assessments of tumour response are of limited value in gauging the efficacy of treatment. MRI is a versatile technique which is sensitive to contrast mechanisms that can be affected by antivascular treatments; this use for MRI has been validated in xenografts and humans. Dynamic contrast-enhanced MRI (DCE-MRI), which demonstrates tissue perfusion and permeability, is being used clinically as a pharmacodynamic indicator of biological activity for antivascular cancer drugs. Early data show that DCE-MRI studies can define the biologically active dose and predict the efficacy of treatment on the basis of changes observed. MRI with macromolecular contrast media (MMCM) depicts microvessel permeability and fractional plasma volume. Xenograft studies with MMCM have shown great promise for evaluating antivascular treatments but this has not been used clinically. Intrinsic susceptibility-weighted MRI, which is sensitive to blood oxygenation and flow, is emerging as a technique that may be able to monitor vascular targeting therapies.

Color Doppler imaging of fibroadenomas of the breast with histopathologic correlation

PURPOSE

This study was conducted to characterize the spatial distribution of blood vessels in breast fibroadenomas.

METHODS

We performed a prospective study to map the anatomic distribution of the vessels in 29 fibroadenomas of the breast using color Doppler sonography. We categorized the detected vessels according to their location in or on the fibroadenoma, counted the different types of vessels, and tested for correlations between vessel distributions or numbers and histopathologic findings.

RESULTS

Blood flow was demonstrated in 24/29 (83%) of fibroadenomas. We found 3 vessel types: feeding vessels, which are prominent vessels leading from the surrounding breast tissue into the fibroadenoma; capsular vessels, which are located within the tissue capsule; and segmental vessels, which are located within the fibrous septa of the fibroadenoma. Capsular and segmental vessels were demonstrated in 23/24 (96%) and 24/24 (100%) of the cases, respectively. Feeding vessels were seen in 12/24 (50%) of the cases. Histopathologic analysis revealed the same location and distribution of the vessels as color Doppler imaging. However, there was no correlation between numbers of vessels counted on sonograms and on histopathologic specimens.

CONCLUSIONS

Examination of the vascularity demonstrated on color Doppler imaging helps in the diagnosis of benign breast neoplasms such as fibroadenomas.

Perfusion MR imaging of extracranial tumor angiogenesis

Dynamic contrast-enhanced MRI (DCE-MRI) using small molecular weight gadolinium chelates enables noninvasive imaging characterization of tissue vascularity. Depending on the technique used, data reflecting tissue perfusion (blood flow, blood volume, mean transit time), microvessel permeability surface area product, and extracellular leakage space can be obtained. Insights into these physiological processes can be obtained from inspection of kinetic enhancement curves or by the application of complex compartmental modeling techniques. Combining morphologic and kinetic features can increase the accuracy of clinical diagnoses. Potential clinical applications include screening for malignant disease, lesion characterization, monitoring lesion response to treatment, and assessment of residual disease. Newer applications include prognostication, pharmacodynamic assessments of antivascular anticancer drugs, and predicting efficacy of treatment. For dynamic MRI to enter into widespread clinical practice, it will be necessary to develop standardized approaches to measurement and robust analysis approaches.

Role of color and power doppler imaging in differentiating between malignant and benign solid breast masses

PURPOSE

This study analyzed the color and power Doppler signals in solid breast masses and assessed their value in differentiating malignant from benign lesions.

METHODS

One hundred twenty-nine biopsy-proven solid breast masses (54 malignant and 75 benign) were evaluated with color and/or power Doppler sonography using a 7-MHz linear-array transducer. We retrospectively analyzed the location, shape, and penetration of the Doppler vascular signals in the breast masses. The location of the vascular signals was categorized as central, peripheral, or both. The shape of the signals was categorized as linear, irregular, branching, or a single dot. A penetrating vessel was defined as a continuous vascular signal extending from outside the lesion to inside it. In 43 cases, power and color Doppler sonograms were compared.

RESULTS

Doppler features suggestive of malignant lesions were the presence of both peripheral and central vascularity (odds ratio, 6.0), presence of penetrating vessels (odds ratio, 5.4), and presence of branching vessels (odds ratio, 13.7). Power Doppler sonography was more sensitive than color Doppler sonography in detecting vascular signals in 49% of cases.

CONCLUSIONS

Color (power) Doppler imaging is a valuable adjunct to conventional sonography in differentiating between malignant and benign breast lesions.

Dynamic contrast-enhanced MRI in clinical oncology: current status and future directions

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is performed after the administration of intravenous contrast medium to noninvasively access tumor vascular characteristics. DCE-MRI techniques utilizing low-molecular-weight contrast media have successfully made the transition from methodological development to preclinical and clinical validation and are now rapidly becoming mainstream clinical tools. DCE-MRI using macromolecular contrast medium (MMCM) can also assay microvascular characteristics of human tumor xenografts. MMCM approval for human use will occur soon. The success of both techniques depends on their ability to demonstrate quantitative differences of contrast medium behavior in a variety of tissues. Evidence is mounting that kinetic parameters correlate with immunohistochemical surrogates of tumor angiogenesis, including microvessel density, and with pathologic tumor grade. DCE-MRI is being applied to monitor the clinical effectiveness of a variety of treatments, including antiangiogenic drugs. Kinetic parameter changes following treatment have correlated with histopathological outcome and patient survival. This article reviews the current clinical status of low-molecular-weight DCE-MRI and reviews the potential of MMCM techniques for evaluating human tumors. Ongoing challenges faced by DCE-MRI as clinical and research tools will be explored.

Differentiation of breast masses using 3-D sonographic and echo-enhancer-based evaluation of the vascular pattern: initial experiences

To evaluate the potential of combined 3-D B-mode and color Doppler (CD) data sets in the differentiation of breast masses, in 50 patients with histologically proven solid breast lesions, 3-D datasets were acquired. A 3-D display was created and volume calculation of tumors, their periphery and vasculature was performed. Time-intensity curves of enhancement after administration of a contrast agent were analyzed. Volumetry of tumor vasculature yielded no significant differences between malignant and benign tumors regarding vascularization of the center (2.60 vs. 2.88%) and periphery (6.66 vs. 3.78%). Only the mean values for the rise time in the center of the tumor, fibroadenoma (FA): 5.7 s and ductal invasive carcinoma (DIC): 15.8s; p = 0.05, and the time to peak in the periphery, FA: 21.0 s and DIC: 31.6 s; p = 0.03, differed significantly. The 3-D ultrasound (US) technique was of no additional value in differentiating breast masses. The calculation of time-intensity curves after administration of a contrast agent may be helpful in differentiating FA and DIC.

Comparing contrast-enhanced ultrasound to immunohistochemical markers of angiogenesis in a human melanoma xenograft model: preliminary results

This study compared contrast-enhanced ultrasound (US) measures of tumor neovascularity with molecular markers of angiogenesis in a human melanoma xenograft model. A total of 14 mice were implanted with a human melanoma cell line (WM-9) in the thigh. After 2 to 3 weeks, a tumor, approximately 12 mm in diameter, developed. The US contrast agent Optison (Mallinckrodt, St. Louis, MO) was injected in a tail vein (dose: 0.4 to 0.6 mL/kg). Power Doppler and pulse-inversion harmonic imaging (HI) were performed with an Elegra scanner (Siemens Medical Systems, Issaquah, WA) and a 7.5 MHz linear array. Frame-rates of 30 Hz and 0.5 Hz (intermittent imaging) were used for pulse-inversion HI. After surgical removal, specimens were sectioned in the same planes as the US images. Immunohistochemical stains for endothelial cells (CD31), vascular endothelial growth factor (VEGF), basic fibroblast growth factor (bFGF) and cyclooxygenase-2 (COX-2) were performed. Two observers graded the stains (for intensity and percent area), and two other observers graded the US imaging modes (for fractional tumor neovascularity) on the same scale from 0 to 3. Of the 14 mice, 4 failed for technical reasons (i.e., n = 10). Linear regressions indicated statistically significant correlations between percent area stained with COX-2 and power Doppler (r = -0.789; p < 0.01), as well as intermittent pulse-inversion HI (r = -0.795; p < 0.05). There was a trend toward significance between percent area stained with VEGF and intermittent pulse-inversion HI (r = -0.720; 0.05 < p < 0.10). No other comparisons were significant. In conclusion, contrast-enhanced US measures of tumor neovascularity in a human melanoma xenograft model appear to provide a noninvasive marker of angiogenesis corresponding to expression of COX-2. However, the sample size of this study is small and, until further studies have been conducted, these conclusions are preliminary.

Changes of tumor vascularity during gene therapy monitored with color Doppler US

Using contrast-enhanced color Doppler (CD) sonography, we assessed alterations of tumor blood flow induced in 25 murine Morris hepatomas transfected with a gancyclovir- (GCV-)sensitizing Herpes simplex virus thymidine kinase (HSVtk) suicide gene in combination with systemic GCV treatment and compared findings with a control tumor. CD measurements were quantified by the color pixel density (CPD) and the mean encoded flow velocity (mean color value, MCV), using computer-assisted image analysis, and compared with histologic arteriole counts. During 5 days, the tumor volume remained constant. In HSVtk-expressing tumors, the median CPD dropped from 16% at baseline to 5% on day 5 (p = 0.001), remaining constant in controls. The MCV decreased from 1.9 cm/s to 1.6 cm/s in the HSVtk-expressing tumors (p = 0.001) and rose from 1.8 cm/s to 2.0 cm/s in the controls (p = 0.002). In an accompanying histologic arteriole assay, we found no alterations attributable to treatment.

Dynamic contrast-enhanced MRI studies in oncology with an emphasis on quantification, validation and human studies

Magnetic resonance imaging (MRI), after the administration of an extracellular, gadolinium-based contrast medium, can be used to detect and characterize human tumours. The success of dynamic contrast-enhanced MRI (DCE-MRI) is dependent on its ability to demonstrate intrinsic differences between a variety of tissues that affect contrast medium behaviour. Evidence is mounting that DCE-MRI measurements correlate with immunohistochemical surrogates of tumour angiogenesis. DCE-MRI can monitor the effectiveness of a variety of treatments including chemotherapy, hormonal manipulation, radiotherapy and novel therapeutic approaches including antiangiogenic drugs. Kinetic parameters in the treatment setting have been correlated with histopathological outcome and patient survival. This article reviews quantification analysis of these studies together with current and future clinical applications.

Fibroadenomas: computer-assisted quantitative evaluation of contrast-enhanced power Doppler features and correlation with histopathology

We aimed to evaluate whether the histopathologic variability of fibroadenomas accounts for their varied appearance in contrast enhanced power Doppler (PD). Forty patients with fibroadenomas (aged 19 to 61 years) underwent power Doppler ultrasound (US) prior to and following IV bolus injection of a microbubble contrast agent. A 3-min computer-assisted assessment of the color pixel density (CPD) was used for objective evaluation of the increase in color Doppler signals. Enhancement characteristics were correlated to histopathologic features of microvessel density and epithelial hyperplasia, patient's age, tumor size, use of exogenous hormones and menopausal status. Epithelial hyperplasia was diagnosed in 19 patients. Compared to baseline values, patients with epithelial hyperplasia showed a significant increase in mean CPD following contrast media administration (p < 0.01). There was a significant correlation to patient's age (p < 0.0001) and tumor size (p < 0.0001), but not to the use of exogenous hormones and menopausal status. Microvessel counts did not show a significant correlation to CPD at baseline (p = 0.07) or with CPD on contrast enhanced PD (p = 0.13), or with patient age (p = 0.43) or tumor size (p = 0.34). Intratumoral epithelial hyperplasia, primarily occurring in young patients, may contribute to the differential diagnostic overlap in some fibroadenomas and thus limit the ability of PD to distinguish between benign and malignant masses on the basis of enhancement characteristics.

New diagnostic techniques and treatments for early breast cancer

OBJECTIVES

To review diagnostic techniques and treatments for early breast cancer, including radiologic techniques, biomarkers, surgical techniques, radiation therapy, adjuvant chemotherapy regimens, and hormonal therapy.

DATA SOURCES

Scientific and review articles, book chapters, and clinical practice.

CONCLUSIONS

Breast cancer is a major health problem. The advances in diagnosis and treatment of early breast cancer and the amount of information available make it difficult for patients to make diagnostic and treatment decisions.

IMPLICATIONS FOR NURSING PRACTICE

Oncology nurses need to be knowledgeable in the many advances in diagnostic and treatment techniques for early breast cancer to assist patients with difficult decisions.

MRI of the breast

MRI of the breast is rapidly evolving as a sensitive technique for the detection and staging of breast cancer. It has considerable benefits but is time consuming and expensive. The benefits and limitations of this technique are presented.

The role of enhanced Doppler ultrasound in differentiation of benign vs. malignant scar lesion after breast surgery for malignancy

AIM

To evaluate the benefit of echo-contrast-enhanced Doppler sonography in the differentiation of benign vs. malignant breast lesions after surgical removal of a malignant breast mass.

METHODS

Thirty-eight patients referred for biopsy of a palpable, suspicious scar lesion 1-15 years (mean 3.3 years) after surgery for breast cancer were examined. During baseline ultrasound examination a subjective scoring system of the vascularity, the number, the regularity of vessels' course and their Doppler parameters were assessed. After injection of an ultrasound contrast agent (Levovist) the same scoring system was applied to the parameters together with enhancement kinetics, enhancement intensity and enhancement pattern. Any increase in the scoring level of two or more characteristics (vascularity, number of vessels, intensity of enhancement in the tumor or regularity score of vessels in the lesion) was defined as suspicious for malignancy. A marked increase of enhancement in the immediate tumor periphery was also regarded as suspicious for malignancy. The sonographic results were assessed prospectively and correlated with the histology of the lesion.

RESULTS

Of the 38 patients with a clinically-suspicious scar lesion, there were 28 true scars and 10 malignant scar lesions. All scar lesions showed no or slight vascularity on baseline sonography. After Echocontrast-enhancement a significant increase in tumor vascularity and the number of tumor vessels could be demonstrated in all 10 malignant lesions but in only one of the 28 benign scars.

CONCLUSION

Scars pose inherent technical problems for optimal mammography. Sonographic evaluation of the vascularity of the lesion with contrast enhancing agents showed improved diagnostic accuracy in the hands of an experienced examiner.