Modality For Breast Cancer Detection Research Articles

Comparison of Diagnostic Test Accuracy of Cone-Beam Breast Computed Tomography and Digital Breast Tomosynthesis for Breast Cancer: A Systematic Review and Meta-Analysis Approach.

Background: Cone-beam breast computed tomography (CBBCT) and digital breast tomosynthesis (DBT) remain the main 3D modalities for X-ray breast imaging. This study aimed to systematically evaluate and meta-analyze the comparison of diagnostic accuracy of CBBCT and DBT to characterize breast cancers. Methods: Two independent reviewers identified screening on diagnostic studies from 1 January 2015 to 30 December 2021, with at least reported sensitivity and specificity for both CBBCT and DBT. A univariate pooled meta-analysis was performed using the random-effects model to estimate the sensitivity and specificity while other diagnostic parameters like the area under the ROC curve (AUC), positive likelihood ratio (), and negative likelihood ratio () were estimated using the bivariate model. Results: The pooled sensitivity specificity, and and AUC at 95% confidence interval are 86.7% (80.3–91.2), 87.0% (79.9–91.8), 6.28 (4.40–8.96), 0.17 (0.12–0.25) and 0.925 for the 17 included studies in DBT arm, respectively, while, 83.7% (54.6–95.7), 71.3% (47.5–87.2), 2.71 (1.39–5.29), 0.20 (0.04–1.05), and 0.831 are the pooled sensitivity specificity, and and AUC for the five studies in the CBBCT arm, respectively. Conclusions: Our study demonstrates that DBT shows improved diagnostic performance over CBBCT regarding all estimated diagnostic parameters; with the statistical improvement in the AUC of DBT over CBBCT. The CBBCT might be a useful modality for breast cancer detection, thus we recommend more prospective studies on CBBCT application.

Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review.

Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.

Breast Ultrasound Elastography is the Most Mature and Well-documented Method for the Measurement of Tissue Stiffness: A Review

Breast cancer is the most common malignancy and one of the leading causes of death in females worldwide. North America has one of the highest incidence breast cancer rates in the world, making breast cancer awareness a high priority. Only in the USA, 527 women are expected to be diagnosed with breast cancer while 110 women will die of it per day. Central to the importance of breast cancer diagnosis is the fact that almost one-third of the latter group could survive if their cancer is detected and treated early. In a worldwide context, this translates into nearly 400,000 lives that could be saved every year as a result of early detection. As such; developing technique that can help to detect and diagnose breast cancer at early stage can have a great impact on survival and quality of life of breast cancer patients. Conventional breast cancer screening and detection techniques such as clinical breast examination and X- ray mammography are known to have low sensitivity. Breast magnetic resonance imaging (MRI) is more sensitive modality for breast cancer detection, however, MRI is costly and has been shown to have low specificity for breast cancer diagnosis. Dynamic contrast-enhanced MRI has been demonstrated to provide a good sensitivity and specificity for differentiation of benign versus malignant lesions, due to altered angiogenesis mechanisms in tumors. However, in addition to being costly, requires injection of exogenous contrast agents to provide such contrast. An alternate imaging technique for breast cancer detection employs tissue stiffness as contrast mechanism. The technique is founded on the fact that alterations in breast tissue stiffness are frequently associated with pathology. Ultrasound elastography is the most mature and well-documented method for the measurement of tissue stiffness. Elastographybased imaging technique has received substantial attention in recent years for non-invasive assessment of tissue mechanical properties. These techniques take advantage of changed soft tissue elasticity in various pathologies to yield qualitative and quantitative information that can be used for diagnostic purpose. Measurements are acquired in specialized imaging modes that can detect tissue stiffness in response to an applied mechanical force. Ultrasoundbased methods are of particular interest due to its many inherent advantages, such as wide availability including at the bedside and relatively low cost. While ultrasound elastography has shown promising results for non-invasive assessment of breast stiffness is emerging.
 Faridpur Med. Coll. J. 2021;16(1):42-44

Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering

Multiparametric Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer detection and is increasingly playing a key role in lesion characterization. In this context, accurate and reliable quantification of the shape and extent of breast cancer is crucial in clinical research environments. Since conventional lesion delineation procedures are still mostly manual, automated segmentation approaches can improve this time-consuming and operator-dependent task by annotating the regions of interest in a reproducible manner. In this work, a semi-automated and interactive approach based on the spatial Fuzzy C-Means (sFCM) algorithm is proposed, used to segment masses on dynamic contrast-enhanced (DCE) MRI of the breast. Our method was compared against existing approaches based on classic image processing, namely (i) Otsu’s method for thresholding-based segmentation, and (ii) the traditional FCM algorithm. A further comparison was performed against state-of-the-art Convolutional Neural Networks (CNNs) for medical image segmentation, namely SegNet and U-Net, in a 5-fold cross-validation scheme. The results showed the validity of the proposed approach, by significantly outperforming the competing methods in terms of the Dice similarity coefficient (84.47±4.75). Moreover, a Pearson’s coefficient of ρ=0.993 showed a high correlation between segmented volume and the gold standard provided by clinicians. Overall, the proposed method was confirmed to outperform the competing literature methods. The proposed computer-assisted approach could be deployed into clinical research environments by providing a reliable tool for volumetric and radiomics analyses.

Diagnostic performance of combined use of automated breast volume scanning & hand-held ultrasound for breast lesions

Background & objectives:Breast cancer being one of the most common malignant tumours among women, diagnostic modalities for early detection of the same become of paramount importance. In this context, the hand-held ultrasound (HHUS) and automated breast volume scanner (ABVS) could provide valuable information for clinicians to diagnose breast diseases. This study aimed to compare and evaluate the diagnostic performance of combined use of HHUS and ABVS for the differentiation of benign and malignant breast lesions.Methods:A total of 361 female patients, who underwent both HHUS and ABVS examinations were included in this study. ABVS and HHUS images were interpreted using the American College of Radiology Breast Imaging-Reporting and Data System (BI-RADS). The distributions of the BI-RADS categories and pathology results were shown as specific numbers. Kappa coefficients test (κ) was calculated to compare the diagnostic results amongst the ABVS, HHUS and ABVS combined with HHUS. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of the three diagnostic methods were calculated and their respective diagnostic performance was analyzed by receiver operator characteristic curve.Results:Of a total of 431 lesions, 153 (35.5%) were malignant and 278 (64.5%) were benign. With respect to the pathology results, the value of κ was 0.713 (P<0.001) for HHUS, κ=0.765 (P<0.001) for ABVS and κ=0.815 (P<0.001) for HHUS+ABVS. The sensitivity, specificity, accuracy, PPV and NPV for HHUS combined with ABVS were 96.08 (147/153), 88.49 (246/278), 91.18 (393/431), 82.12 (147/179) and 97.62 per cent (246/252) respectively. For HHUS, these were 90.20 (138/153), 84.17 (234/278), 86.31 (372/431), 75.82 (138/182) and 93.98 per cent (234/249) respectively; and for ABVS these were 92.16 (141/153), 87.05 (242/278), 88.86 (383/431), 79.66 (141/177) and 95.28 per cent (242/254), respectively. There was no significant difference amongst these three methods, but the diagnostic performance of HHUS combined with ABVS was better than, or at least equal to, that of HHUS or ABVS alone.Interpretation & conclusions:The results of this study suggest that ABVS is a promising and advantageous modality for breast cancer detection. Furthermore, the combination of HHUS and ABVS showed a more comparable diagnostic performance than HHUS or ABVS alone for distinguishing between benign and malignant breast lesions.

Ultra-High b-Value Diffusion-Weighted Imaging-Based Abbreviated Protocols for Breast Cancer Detection.

Contrast-enhanced (CE) magnetic resonance imaging (MRI) is the most effective imaging modality for breast cancer detection. A contrast agent-free examination technique would be desirable for breast MRI screening. The purpose of this study was to evaluate the capability to detect and characterize suspicious breast lesions with an abbreviated, non-contrast-enhanced MRI protocol featuring ultra-high b-value diffusion-weighted imaging (DWI) compared with CE images. The institutional review board-approved prospective study included 127 female subjects with different clinical indications for breast MRI. Magnetic resonance imaging examinations included DWI sequences with b-values of 1500 s/mm2 (b1500) and 2500 s/mm2 (b2500), native T1- and T2-weighted images, and CE sequences at 1.5 T and 3 T scanners. Two reading rounds were performed, including either the b1500 or the b2500 DWI in consecutive assessment steps: (A) maximum intensity projections (MIPs) of DWI, (B) DWI and apparent diffusion coefficient maps, (C) as (B) but with additional native T1- and T2-weighted images, and (D) as (C) but with additional CE images (full-length protocol). Two readers independently determined the presence of a suspicious lesion. Histological confirmation was obtained for conspicuous lesions, whereas the full MRI data set was obtained for inconspicuous and clearly benign lesions. Statistical analysis included calculation of diagnostic accuracy and interrater agreement via the intraclass correlation coefficient. The cohort comprised 116 cases with BI-RADS 1 findings and 138 cases with BI-RADS ≥2 findings, including 38 histologically confirmed malignancies. For (A), breasts without pathological findings could be recognized with high diagnostic accuracy (negative predictive value, ≥97.0%; sensitivity, ≥92.1% for both readers), but with a limited specificity (≥58.3%; positive predictive value, ≥28.6%). Within the native readings, approach (C) with b2500 performed best (negative predictive value, 99.5%; sensitivity, 97.4%; specificity, 88.4%). The intraclass correlation coefficient was between 0.683 (MIP b1500) and 0.996 (full protocol). A native abbreviated breast MRI protocol with advanced high b-value DWI might allow nearly equivalent diagnostic accuracy as CE breast MRI and seems to be well suited for lesion detection purposes.

Applications of Artificial Intelligence in Mammography from a Development and Validation Perspective

유방촬영술은 유방암 검진 및 진단을 위한 기본적인 영상 검사이지만, 판독이 어려우며 높은 숙련도를 필요로 한다고 잘 알려져 있다. 이러한 어려움을 극복하기 위해 최근 몇 년 사이에 인공지능을 이용한 유방암 검출 알고리즘들이 활발히 연구되고 있다. 본 종설에서 저자는 고전적인 computer-aided detection 소프트웨어 대비 최근 많이 사용되는 딥러닝의 특징을 알아보고, 딥러닝 알고리즘의 개발 방법과 임상적 검증 방법에 대해서 기술하였다. 또한 딥러닝 기반의 검진 유방촬영술의 판독 방법 분류, 유방 치밀도 평가, 그리고 유방암 위험도 예측 모델 등을 위한 딥러닝 연구들도 소개하였다. 마지막으로 유방촬영술 관련 인공지능 기술들에 대한 영상의학과 전문의의 관심과 의견의 필요성을 기술하였다.

Diffusion-Weighted Imaging as a Stand-Alone Breast Imaging Modality

확산강조영상은 유방암의 진단과 스크리닝에 있어 독립적 검사 방법으로서의 기대되는 결과를 보여주는 빠른 비조영증강 검사 방법이다. 현재까지의 연구 결과 유방암 진단에 있어 독립적 검사 방법으로서 확산강조영상의 민감도는 역동적 조영증강 검사보다는 낮으나 유방촬영술보다는 높으며, 이로써 유방암 스크리닝에 대한 유용한 대안이 될 수 있을 것으로 보인다. 확산강조영상의 표준화된 영상 획득과 판독을 통해 영상 화질이 개선될 수 있고, 판독 결과의 다양성도 감소할 것으로 기대된다. 또한, 최신 기법과 후처리 기법을 사용한 고해상도 확산강조영상을 시행함으로써 1 cm 미만의 작은 암의 발견율을 증가시킬 수 있고, 가음성 및 가양성 결과를 감소시킬 것으로 보인다. 현재 한국에서 진행 중인 고위험군 여성에서의 확산강조영상 스크리닝에 대한 다기관 연구 결과가 나온다면 독립적 검사로서의 확산강조영상의 사용을 촉진시킬 수 있을 것으로 기대된다.

An introduction to the Cyrcadia Breast Monitor: A wearable breast health monitoring device

BackgroundThe most common breast cancer detection modalities are generally limited by radiation exposure, discomfort, high costs, inter-observer variabilities in image interpretation, and low sensitivity in detecting cancer in dense breast tissue. Therefore, there is a clear need for an affordable and effective adjunct modality that can address these limitations. The Cyrcadia Breast Monitor (CBM) is a non-invasive, non-compressive, and non-radiogenic wearable device developed as an adjunct to current modalities to assist in the detection of breast tissue abnormalities in any type of breast tissue. MethodsThe CBM records thermodynamic metabolic data from the breast skin surface over a period of time using two wearable biometric patches consisting of eight sensors each and a data recording device. The acquired multi-dimensional temperature time series data are analyzed to determine the presence of breast tissue abnormalities. The objective of this paper is to present the scientific background of CBM and also to describe the history around the design and development of the technology. ResultsThe results of using the CBM device in the initial clinical studies are also presented. Twenty four-hour long breast skin temperature circadian rhythm data was collected from 93 benign and 108 malignant female study subjects in the initial clinical studies. The predictive model developed using these datasets could differentiate benign and malignant lesions with 78% accuracy, 83.6% sensitivity and 71.5% specificity. A pilot study of 173 female study subjects is underway, in order to validate this predictive model in an independent test population. ConclusionsThe results from the initial studies indicate that the CBM may be valuable for breast health monitoring under physician supervision for confirmation of any abnormal changes, potentially prior to other methods, such as, biopsies. Studies are being conducted and planned to validate the technology and also to evaluate its ability as an adjunct breast health monitoring device for identifying abnormalities in difficult-to-diagnose dense breast tissue.

Thermal Imaging - An Emerging Modality for Breast Cancer Detection: A Comprehensive Review.

Breast cancer is not preventable. To reduce the death rate and improve the survival chances of breast cancer patients, early and accurate detection is the only panacea. Delay in diagnosis of this disease causes 60% of deaths. Thermal imaging is a low-risk modality for early breast cancer decision making without injecting any form of energy into the human body. Thermography as a screening tool was first introduced and well accepted in 1956. However, a study in 1977 found that it lagged behind other screening tools and is subjective. Soon after, its use was discontinued. This review discusses various screening tools used to detect breast cancer with a focus on thermography along with their advantages and shortcomings. With the maturation of thermography equipment and technological advances, this technique is emerging and has become the refocus of many biomedical researchers across the globe in the past decade. This study dispenses an exhaustive review of the work done related to interpretation of breast thermal variations and confers the discipline, frameworks, and methodologies used by different authors to diagnose breast cancer. Different performance metrics like accuracy, specificity, and sensitivity have also been examined. This paper outlines the most pressing research gaps for future work to improvise the accuracy of results for diagnosis of breast abnormalities using image processing tools, mathematical modelling and artificial intelligence. However, supplementary research is needed to affirm the potential of this technology for predicting breast cancer risk effectively. Altogether, our findings inform that it is a promising research problem and a potential solution for early detection of breast cancer in younger women.

Computer-aided tumor detection in automated breast ultrasound using a 3-D convolutional neural network

Background and ObjectivesAutomated breast ultrasound (ABUS) is a widely used screening modality for breast cancer detection and diagnosis. In this study, an effective and fast computer-aided detection (CADe) system based on a 3-D convolutional neural network (CNN) is proposed as the second reader for the physician in order to decrease the reviewing time and misdetection rate. MethodsOur CADe system uses the sliding window method, a CNN-based determining model, and a candidate aggregation algorithm. First, the sliding window method is performed to split the ABUS volume into volumes of interest (VOIs). Afterward, VOIs are selected as tumor candidates by our determining model. To achieve higher performance, focal loss and ensemble learning are used to solve data imbalance and reduce false positive (FP) and false negative (FN) rates. Because several selected candidates may be part of the same tumor and they may overlap each other, a candidate aggregation method is applied to merge the overlapping candidates into the final detection result. ResultsIn the experiments, 165 and 81 cases are utilized for training the system and evaluating system performance, respectively. On evaluation with the 81 cases, our system achieves sensitivities of 100% (81/81), 95.3% (77/81), and 90.9% (74/81) with FPs per pass (per case) of 21.6 (126.2), 6.0 (34.8), and 4.6 (27.1) respectively. According to the results, the number of FPs per pass (per case) can be diminished by 56.8% (57.1%) at a sensitivity of 95.3% based on our tumor detection model. ConclusionsIn conclusion, our CADe system using 3-D CNN with the focal loss and ensemble learning may have the capability of being a tumor detection system in ABUS image.

Space-occupying lesions identification in mammary glands using a mixed computational strategy

The mammary pathology can manifest itself in multiple ways and originates space-occupying lesions. The breast cancer is a space-occupying lesion, which is highly prevalent, especially in women, and worldwide it is one of the leading causes of morbidity and mortality in this population. The main image modality for breast cancer detection is the magnetic resonance but this kind of image modality introduces several imperfections that affect the image quality. Some of these imperfections or problems are: inhomogeneity in the anatomical structures, riccian noise and artifacts. These problems make the analysis of the image information a real challenge. To address these problems, in this paper, we propose a computational technique able to extract a space-occupying lesion linked to breast cancer, present in magnetic resonance images. For this, the original image is processed with statistical-arithmetic filters and segmentation algorithms based on thresholding and multi-seed region growing techniques. The results, based on Dice score, show that the proposed technique is suitable for segmenting the breast cancer due high correlation between semi-automatic and manual segmentations. This technique can be useful in the detection, characterization and monitoring of this type of cancer and it can let to medical doctors to realize their work more efficiently.

Highlights of This Issue

Classical Hodgkin lymphomas have long been treated with a combination of doxorubicin, vinblastine, dacarbazine, and bleomycin (ABVD). Although this combination has worked well overall, roughly one-third of patients with advanced disease will ultimately develop resistance and relapse. Ramchandren and colleagues describe results from the ECHELON-1 phase III trial, comparing the ABVD regimen with A+AVD (doxorubicin, vinblastine, dacarbazine, and brentuximab vedotin (ADCETRIS), an CD30-directed antibody-drug conjugate). In the North American cohort assessed in this article, A+AVD improved modified progression-free survival and reduced the risk of disease progression or death. Although adverse events were common on A+AVD, fewer patients needed to discontinue participation in the study than patients treated with ABVD. Overall, these results support the continuation of the A+AVD regimen for patients with advanced classical Hodgkin lymphoma.Patients with intermediate risk acute myeloid leukemia (int-risk AML) have a high rate of relapse and a poor overall survival. The preferred treatment for int-risk AML has been allogenic hematopoietic stem cell transplant (allo-HSCT); however, haploidentical hematopoietic stem cell transplant (haplo-HCST) has been proposed as a suitable postremission therapy for patients lacking a matching donor. Lv and colleagues describe the results of a prospective trial in which int-risk AML patients received either chemotherapy or underwent halplo-HSCT after the first complete remission. Haplo-HSCT significantly improved 3-year disease-free and overall survival compared with chemotherapy. These results suggest that, in the absence of a matched HSCT donor, haplo-HCST is an effective treatment strategy for int-risk AML postremission.While early detection of breast cancer has been improved by advances in imaging, even MRI, the most sensitive known imaging modality for breast cancer detection, can yield false positive results, leading to unnecessary biopsies. To determine the efficacy of noncontrast diffusion weighted imaging (DWI) in reducing breast MRI false positives, Rahbar and colleagues performed a prospective, multicenter phase II trial. This trial confirmed that DWI is efficacious in discriminating benign from malignant pathologies to potentially reduce the number of unnecessary biopsies. Furthermore, the trial identified quantitative DWI-based apparent diffusion coefficient thresholds that could be validated in future phase III trials to facilitate further clinical translation of this novel early detection strategy.Triple-negative breast cancers (TNBC) carry the worst prognosis of all breast cancer subtypes, in part because no effective therapies have been identified. Foidart and colleagues have identified a combination of markers to serve as predictive biomarkers to targeted therapies. Specifically, MT4-MMP+/EGFR+/RB+ TNBC patient-derived xenografts showed greater sensitivity to a combination of anti-EGFR (erlotinib) and anti-CDK4/6 (palbociclib). The authors estimate that half of TNBC express this combination of markers. Further study of the combination of erlotinib and palbociclib in this population will therefore determine the utility of this biomarker combination in clinical settings.

One-view digital breast tomosynthesis as a stand-alone modality for breast cancer detection: do we need more?

PurposeTo compare the performance of one-view digital breast tomosynthesis (1v-DBT) to that of three other protocols combining DBT and mammography (DM) for breast cancer detection.Materials and methodsSix radiologists, three experienced with 1v-DBT in screening, retrospectively reviewed 181 cases (76 malignant, 50 benign, 55 normal) in two sessions. First, they scored sequentially: 1v-DBT (medio-lateral oblique, MLO), 1v-DBT (MLO) + 1v-DM (cranio-caudal, CC) and two-view DM + DBT (2v-DM+2v-DBT). The second session involved only 2v-DM. Lesions were scored using BI-RADS® and level of suspiciousness (1–10). Sensitivity, specificity, receiver operating characteristic (ROC) and jack-knife alternative free-response ROC (JAFROC) were computed.ResultsOn average, 1v-DBT was non-inferior to any of the other protocols in terms of JAFROC figure-of-merit, area under ROC curve, sensitivity or specificity (p>0.391). While readers inexperienced with 1v-DBT screening improved their sensitivity when adding more images (69–79 %, p=0.019), experienced readers showed similar sensitivity (76 %) and specificity (70 %) between 1v-DBT and 2v-DM+2v-DBT (p=0.482). Subanalysis by lesion type and breast density showed no difference among modalities.ConclusionDetection performance with 1v-DBT is not statistically inferior to 2v-DM or to 2v-DM+2v-DBT; its use as a stand-alone modality might be sufficient for readers experienced with this protocol.Key points• One-view breast tomosynthesis is not inferior to two-view digital mammography.• One-view DBT is not inferior to 2-view DM plus 2-view DBT.• Training may lead to 1v-DBT being sufficient for screening.

Review of breast screening: Toward clinical realization of microwave imaging.

Microwave imaging (MI) technology has come a long way to introduce a noninvasive, inexpensive, fast, convenient, and safe screening tool for clinical breast monitoring. However, there is a niche between the existing understanding of MI by engineers versus clinicians. Our manuscript targets that niche and highlights the state of the art in MI technology compared to the existing breast cancer detection modalities (mammography, ultrasound, molecular imaging, and magnetic resonance). The significance of our review article is in consolidation of up-to-date breast clinician views with the practical needs and engineering challenges of a novel breast screening modality. We summarize breast tissue abnormalities and highlight the benefits as well as potential drawbacks of the MI as a cancer detection methodology. Our goal is to present an article that MI researchers as well as practitioners in the field can use to assess the viability of the MI technology as a competing or complementary modality to the existing means of breast cancer screening.

Breast Cancer Detection by B7-H3-Targeted Ultrasound Molecular Imaging.

Ultrasound complements mammography as an imaging modality for breast cancer detection, especially in patients with dense breast tissue, but its utility is limited by low diagnostic accuracy. One emerging molecular tool to address this limitation involves contrast-enhanced ultrasound using microbubbles targeted to molecular signatures on tumor neovasculature. In this study, we illustrate how tumor vascular expression of B7-H3 (CD276), a member of the B7 family of ligands for T-cell coregulatory receptors, can be incorporated into an ultrasound method that can distinguish normal, benign, precursor, and malignant breast pathologies for diagnostic purposes. Through an IHC analysis of 248 human breast specimens, we found that vascular expression of B7-H3 was selectively and significantly higher in breast cancer tissues. B7-H3 immunostaining on blood vessels distinguished benign/precursors from malignant lesions with high diagnostic accuracy in human specimens. In a transgenic mouse model of cancer, the B7-H3-targeted ultrasound imaging signal was increased significantly in breast cancer tissues and highly correlated with ex vivo expression levels of B7-H3 on quantitative immunofluorescence. Our findings offer a preclinical proof of concept for the use of B7-H3-targeted ultrasound molecular imaging as a tool to improve the diagnostic accuracy of breast cancer detection in patients.

Comprehensive breast MRI: an update

With increasing use of clinical breast MRI and a plethora of novel techniques and sequences for lesion characterisation, a standardised approach to lesion description and reporting is increasingly important. This facilitates meaningful research and audit, particularly in the context of multicentre studies. To this end the American College of Radiology has recently published the 5th edition of the BI-RADS lexicon, with revision of the MRI section including addition of new descriptors, modification of others and deletion of some that were rarely helpful in practice, such as ‘stippled’ enhancement [1]. Categories for fibroglandular volume are now a to d, in line with the descriptors for mammography, thus avoiding confusion with assessment category. The degree of background parenchymal enhancement is now purely descriptive, from none/minimal to marked. A new descriptor is clustered rim enhancement (usually indicating DCIS). Non mass-like enhancement becomes non mass enhancement, whereas ‘ductal’ enhancement becomes linear. Overall the effect is of simplification and consistency with the mammography and ultrasound lexicons. The lexicon highlights the importance of a structured report, including the indication, scan technique, salient findings and critically, an overall assessment with a clear recommendation for further management. This approach has been validated in a number of studies correlating the assessment category with histopathological findings and/or long-term follow-up [2,3]. Use of a standardised lexicon, modified in the light of available evidence, has obviated some of the interpretative challenges of breast MRI. Use of supplemental diffusion weighted imaging may also be helpful [4]. Nonetheless, technical and clinical challenges remain. Artefacts are problematic, especially at 3T [5], and medical physics support is essential for good quality diagnostic scans, especially at higher field strength. Most importantly, it should be remembered that to date, despite the recognised superiority of breast MRI over any other modality for breast cancer detection and local staging, there is no hard evidence for improved patient outcomes. Two randomised controlled trials and seven comparative cohort studies have not shown any benefit for preoperative MRI in terms of re-excision rates; rather, there is a trend to higher mastectomy rates [6]. No convincing beneficial effects have been demonstrated in terms of in-breast tumour recurrence rates, disease-free survival or overall survival. Regarding assessment of response to neoadjuvant chemotherapy, MRI correlates better than mammography or ultrasound with final pathology, but false positive and negative studies are frequent, with underestimation of the amount of residual disease in up to 30% and overestimation in around 20% of cases [7,8]. Though evidence is accruing that MRI may be helpful in early response assessment, this is by no means standardised and is heavily dependent on many factors, not least tumour immunophenotype. Finally, though screening breast MRI for women at high risk has approximately double the sensitivity of mammography with encouraging stage shift and high rates of node negativity, there is as yet no evidence of a reduction in breast cancer mortality [9,10]. This workshop will provide an update on the BI-RADS lexicon and indications for breast MRI, with expert tuition in hands-on analysis of case studies.

The PSTD Method with the 4th-Order Time Integration for 3D TAT Reconstruction of a Breast Model

The thermoacoustic tomography (TAT) is a novel noninvasive and nonionizing medical imaging modality for breast cancer detection. In the TAT, a short pulse of microwave is irradiated to the breast tissue. The tissue absorbs the microwave energy and is heated up momentarily, thus it generates acoustic waves due to the thermoelastic expansion. If the pulse width of the microwave radiation is around one microsecond, the generated acoustic waves are ultrasonic and are in the MHz range. Wide-band ultrasonic transducers are employed to acquire the time-resolved ultrasound signals, which carry information about the microwave absorption properties (mainly related to conductivities) of different tissues. An image showing such properties can then be reconstructed from the time-resolved ultrasound signals. Most existing TAT reconstruction methods are based on the assumption that the tissue under study is acoustically homogeneous. In practice, however, most biological tissues are inhomogeneous. For example, the speed of sound has about 10% variation in breast tissue. The acoustic heterogeneity will cause phase distortion of the pressure field, which will in turn cause blurring in the reconstructed image, thus limiting the ability to resolve small objects. In this work, a 3D inhomogeneous reconstruction method based on pseudo-spectral time-domain (PSTD) is presented to overcome this problem. The method includes two steps. The first step is a homogeneous reconstruction process, from which an initial image is obtained. Since the inhomogeneity itself is usually an acoustic source, the shape and location of the inhomogeneity can be estimated. Then, the acoustic properties of the inhomogeneities (available from the literatures for known tissue types) are assigned to the classified regions, and the other reconstruction based on the updated acoustic property map is conducted. With this process, the phase distortion can be effectively corrected. So it can improve the ability to image small objects. A 3D breast phantom is used to study the proposed method. The breast phantom was generated based on the data set of the Visible Human Project. Regions of different tissue types have been classified and acoustic and electric properties are assigned to such regions. Small phantom tumors placed in the breast phantom have been reconstructed successfully with the inhomogeneous reconstruction method. Improved resolution has been achieved compared to that obtained by homogeneous method.

Photoacoustic mammography: prospects and promises

. The strategy to reduce breast cancer deaths is by early detection, reliable diagnosis and effective treatments. The standard imag-ing modality for breast cancer detection by screening asymptomatic women is x-ray imaging. Diagnosis is performed after the triple assessment of x-ray mammogra-phy, ultrasonography and biopsy. MRI is used in specific problem-solving situa-tions, such as when contradictory results are obtained from x-ray and ultrasound images. x-ray mammography is capable of producing 2D projection images with a high spatial resolution. However, x-ray mammo graphy, besides possessing ionizing hazards and requiring painful breast com -pression, is less sensitive in dense breasts

Impact of Microwave Pulses on Thermoacoustic Imaging Applications

Thermoacoustic imaging is a promising modality for breast cancer detection. Acoustic signals generated by tumor targets with different sizes subject to microwave pulses with various widths and waveforms are analytically studied in this letter. Time- and frequency-domain profiles of the acoustic signals are achieved. Some key parameters of the acoustic signals, including peak value and peak-to-peak time interval of the time-domain acoustic pressure and frequency of the global peak and magnitude ratio of the second highest peak to the global peak of the frequency-domain pressure, are discussed. Impact of pulse width on acoustic signal peak-to-peak interval and image spatial resolution is developed. Square pulse is demonstrated to be superior in acquiring higher image resolution and signal-to-noise ratio. This study is helpful to optimizing the microwave pulse, selecting acoustic transducer, and evaluating image resolution.