Radiation exposure and screening yield by digital breast tomosynthesis compared to mammography: results of the TOSYMA Trial breast density related.

OBJECTIVES: The randomized TOmosynthesis plus SYnthesized MAmmography (TOSYMA) screening trial has shown that digital breast tomosynthesis plus synthesized mammography (DBT + SM) is superior to digital mammography (DM) in invasive breast cancer detection varying with breast density. On the other hand, the overall average glandular dose (AGD) of DBT is higher than that of DM. Comparing the DBT + SM and DM trial arm, we analyzed here the mean AGD and their determinants per breast density category and related them to the respective invasive cancer detection rates (iCDR). METHODS: TOSYMA screened 99,689 women aged 50 to 69 years. Compression force, resulting breast thickness, the calculated AGD obtained from each mammography device, and previously published iCDR were used for comparisons across breast density categories in the two trial arms. RESULTS: There were 196,622 exposures of 49,227 women (DBT + SM) and 197,037 exposures of 49,132 women (DM) available for analyses. Mean breast thicknesses declined from breast density category A (fatty) to D (extremely dense) in both trial arms. However, while the mean AGD in the DBT + SM arm declined concomitantly from category A (2.41 mGy) to D (1.89 mGy), it remained almost unchanged in the DM arm (1.46 and 1.51 mGy, respectively). In relative terms, the AGD elevation in the DBT + SM arm (64.4% (A), by 44.5% (B), 27.8% (C), and 26.0% (D)) was lowest in dense breasts where, however, the highest iCDR were observed. CONCLUSION: Women with dense breasts may specifically benefit from DBT + SM screening as high cancer detection is achieved with only moderate AGD elevations. CLINICAL RELEVANCE STATEMENT: TOSYMA suggests a favorable constellation for screening with digital breast tomosynthesis plus synthesized mammography (DBT + SM) in dense breasts when weighing average glandular dose elevation against raised invasive breast cancer detection rates. There is potential for density-, i.e., risk-adapted population-wide breast cancer screening with DBT + SM. KEY POINTS: Breast thickness declines with visually increasing density in digital mammography (DM) and digital breast tomosynthesis (DBT). Average glandular doses of DBT decrease with increasing density; digital mammography shows lower and more constant values. With the smallest average glandular dose difference in dense breasts, DBT plus SM had the highest difference in invasive breast cancer detection rates.

CCDC12 gene methylation in peripheral blood as a potential biomarker for breast cancer detection.

BACKGROUND: Aberrant DNA methylation has been identified as biomarkers for breast cancer detection. Coiled-coil domain containing 12 gene (CCDC12) implicated in tumorigenesis. This study aims to investigate the potential of blood-based CCDC12 methylation for breast cancer detection. METHODS: DNA methylation level of CpG sites (Cytosine-phosphate Guanine dinucleotides) in CCDC12 gene was measured by mass spectrometry in 255 breast cancer patients, 155 patients with benign breast nodules and 302 healthy controls. The association between CCDC12 methylation and breast cancer risk was evaluated by logistic regression and receiver operating characteristic curve analysis. RESULTS: A total of eleven CpG sites were analyzed. The CCDC12 methylation levels were higher in breast cancer patients. Compared to the lowest tertile of methylation level in CpG_6,7, CpG_10 and CpG_11, the highest quartile was associated with 82, 91 and 95% increased breast cancer risk, respectively. The CCDC12 methylation levels were associated with estrogen receptor (ER) and human epidermal growth factor 2 (HER2) status. In ER-negative and HER2-positive (ER-/HER2+) breast cancer subtype, the combination of four sites CpG_2, CpG_5, CpG_6,7 and CpG_11 methylation levels could distinguish ER-/HER2+ breast cancer from the controls (AUC = 0.727). CONCLUSION: The hypermethylation levels of CCDC12 in peripheral blood could be used for breast cancer detection.

Breast cancer detection could be facilitated by novel blood-based DNA methylation biomarkers.The methylation levels of CpG sites in CCDC12 were higher in breast cancer than those in controls.The combination of four sites CpG_2, CpG_5, CpG_6,7 and CpG_11 methylation levels could distinguish ER-/HER2+ breast cancer subtype from the controls.

An innovative breast cancer detection framework using multiscale dilated densenet with attention mechanism.

Cancer-related deadly diseases affect both developed and underdeveloped nations worldwide. Effective network learning is crucial to more reliably identify and categorize breast carcinoma in vast and unbalanced image datasets. The absence of early cancer symptoms makes the early identification process challenging. Therefore, from the perspectives of diagnosis, prevention, and therapy, cancer continues to be among the healthcare concerns that numerous researchers work to advance. It is highly essential to design an innovative breast cancer detection model by considering the complications presented in the classical techniques. Initially, breast cancer images are gathered from online sources and it is further subjected to the segmentation region. Here, it is segmented using Adaptive Trans-Dense-Unet (A-TDUNet), and their parameters are tuned using the developed Modified Sheep Flock Optimization Algorithm (MSFOA). The segmented images are further subjected to the breast cancer detection stage and effective breast cancer detection is performed by Multiscale Dilated Densenet with Attention Mechanism (MDD-AM). Throughout the result validation, the Net Present Value (NPV) and accuracy rate of the designed approach are 96.719% and 93.494%. Hence, the implemented breast cancer detection model secured a better efficacy rate than the baseline detection methods in diverse experimental conditions.

Hybrid machine learning-based breast cancer segmentation framework using ultrasound images with optimal weighted features.

One of the most dangerous conditions in clinical practice is breast cancer because it affects the entire life of women in recent days. Nevertheless, the existing techniques for diagnosing breast cancer are complicated, expensive, and inaccurate. Many trans-disciplinary and computerized systems are recently created to prevent human errors in both quantification and diagnosis. Ultrasonography is a crucial imaging technique for cancer detection. Therefore, it is essential to develop a system that enables the healthcare sector to rapidly and effectively detect breast cancer. Due to its benefits in predicting crucial feature identification from complicated breast cancer datasets, machine learning is widely employed in the categorization of breast cancer patterns. The performance of machine learning models is limited by the absence of a successful feature enhancement strategy. There are a few issues that need to be handled with the traditional breast cancer detection method. Thus, a novel breast cancer detection model is designed based on machine learning approaches and employing ultrasonic images. At first, ultrasound images utilized for the analysis is acquired from the benchmark resources and offered as the input to preprocessing phase. The images are preprocessed by utilizing a filtering and contrast enhancement approach and attained the preprocessed image. Then, the preprocessed images are subjected to the segmentation phase. In this phase, segmentation is performed by employing Fuzzy C-Means, active counter, and watershed algorithm and also attained the segmented images. Later, the segmented images are provided to the pixel selection phase. Here, the pixels are selected by the developed hybrid model Conglomerated Aphid with Galactic Swarm Optimization (CAGSO) to attain the final segmented pixels. Then, the selected segmented pixel is fed in to feature extraction phase for attaining the shape features and the textual features. Further, the acquired features are offered to the optimal weighted feature selection phase, and also their weights are tuned tune by the developed CAGSO. Finally, the optimal weighted features are offered to the breast cancer detection phase. Finally, the developed breast cancer detection model secured an enhanced performance rate than the classical approaches throughout the experimental analysis.

Transfer learning for the generalization of artificial intelligence in breast cancer detection: a case-control study.

BACKGROUND: Some researchers have questioned whether artificial intelligence (AI) systems maintain their performance when used for women from populations not considered during the development of the system. PURPOSE: To evaluate the impact of transfer learning as a way of improving the generalization of AI systems in the detection of breast cancer. MATERIAL AND METHODS: This retrospective case-control Finnish study involved 191 women diagnosed with breast cancer and 191 matched healthy controls. We selected a state-of-the-art AI system for breast cancer detection trained using a large US dataset. The selected baseline system was evaluated in two experimental settings. First, we examined our private Finnish sample as an independent test set that had not been considered in the development of the system (unseen population). Second, the baseline system was retrained to attempt to improve its performance in the unseen population by means of transfer learning. To analyze performance, we used areas under the receiver operating characteristic curve (AUCs) with DeLong's test. RESULTS: Two versions of the baseline system were considered: ImageOnly and Heatmaps. The ImageOnly and Heatmaps versions yielded mean AUC values of 0.82±0.008 and 0.88±0.003 in the US dataset and 0.56 (95% CI=0.50-0.62) and 0.72 (95% CI=0.67-0.77) when evaluated in the unseen population, respectively. The retrained systems achieved AUC values of 0.61 (95% CI=0.55-0.66) and 0.69 (95% CI=0.64-0.75), respectively. There was no statistical difference between the baseline system and the retrained system. CONCLUSION: Transfer learning with a small study sample did not yield a significant improvement in the generalization of the system.

Point-of-care devices based on fluorescence imaging and spectroscopy for tumor margin detection during breast cancer surgery: Towards breast conservation treatment.

OBJECTIVE: Fluorescence-based methods are highly specific and sensitive and have potential in breast cancer detection. Simultaneous fluorescence imaging and spectroscopy during intraoperative procedures of breast cancer have great advantages in detection of tumor margin as well as in classification of tumor to healthy tissues. Intra-operative real-time confirmation of breast cancer tumor margin is the aim of surgeons, and therefore, there is an urgent need for such techniques and devices which fulfill the surgeon's priorities. METHODS: In this article, we propose the development of fluorescence-based smartphone imaging and spectroscopic point-of-care multi-modal devices for detection of invasive ductal carcinoma in tumor margin during removal of tumor. These multimodal devices are portable, cost-effective, noninvasive, and user-friendly. Molecular level sensitivity of fluorescence process shows different behavior in normal, cancerous and marginal tissues. We observed significant spectral changes, such as, red-shift, full-width half maximum (FWHM), and increased intensity as we go towards tumor center from normal tissue. High contrast in fluorescence images and spectra are also recorded for cancer tissues compared to healthy tissues. Preliminary results for the initial trial of the devices are reported in this article. RESULTS: A total 44 spectra from 11 patients of invasive ductal carcinoma (11 spectra for invasive ductal carcinoma and rest are normal and negative margins) are used. Principle component analysis is used for the classification of invasive ductal carcinoma with an accuracy of 93%, specificity of 75% and sensitivity of 92.8%. We obtained an average 6.17 ± 1.66 nm red shift for IDC with respect to normal tissue. The red shift and maximum fluorescence intensity indicates p < 0.01. These results described here are supported by histopathological examination of the same sample. CONCLUSION: In the present manuscript, simultaneous fluorescence-based imaging and spectroscopy is accomplished for the classification of IDC tissues and breast cancer margin detection.

Review and Analysis of Tumour Detection and Image Quality Analysis in Experimental Breast Microwave Sensing.

This review evaluates the methods used for image quality analysis and tumour detection in experimental breast microwave sensing (BMS), a developing technology being investigated for breast cancer detection. This article examines the methods used for image quality analysis and the estimated diagnostic performance of BMS for image-based and machine-learning tumour detection approaches. The majority of image analysis performed in BMS has been qualitative and existing quantitative image quality metrics aim to describe image contrast-other aspects of image quality have not been addressed. Image-based diagnostic sensitivities between 63 and 100% have been achieved in eleven trials, but only four articles have estimated the specificity of BMS. The estimates range from 20 to 65%, and do not demonstrate the clinical utility of the modality. Despite over two decades of research in BMS, significant challenges remain that limit the development of this modality as a clinical tool. The BMS community should utilize consistent image quality metric definitions and include image resolution, noise, and artifacts in their analyses. Future work should include more robust metrics, estimates of the diagnostic specificity of the modality, and machine-learning applications should be used with more diverse datasets and with robust methodologies to further enhance BMS as a viable clinical technique.

Evaluation and comparison of a CdTe based photon counting detector with an energy integrating detector for X-ray phase sensitive imaging of breast cancer.

PURPOSE: To compare imaging performance of a cadmium telluride (CdTe) based photon counting detector (PCD) with a CMOS based energy integrating detector (EID) for potential phase sensitive imaging of breast cancer. METHODS: A high energy inline phase sensitive imaging prototype consisting of a microfocus X-ray source with geometric magnification of 2 was employed. The pixel pitch of the PCD was 55µm, while 50µm for EID. The spatial resolution was quantitatively and qualitatively assessed through modulation transfer function (MTF) and bar pattern images. The edge enhancement visibility was assessed by measuring edge enhancement index (EEI) using the acrylic edge acquired images. A contrast detail (CD) phantom was utilized to compare detectability of simulated tumors, while an American College of Radiology (ACR) accredited phantom for mammography was used to compare detection of simulated calcification clusters. A custom-built phantom was employed to compare detection of fibrous structures. The PCD images were acquired at equal, and 30% less mean glandular dose (MGD) levels as of EID images. Observer studies along with contrast to noise ratio (CNR) and signal to noise ratio (SNR) analyses were performed for comparison of two detection systems. RESULTS: MTF curves and bar pattern images revealed an improvement of about 40% in the cutoff resolution with the PCD. The excellent spatial resolution offered by PCD system complemented superior detection of the diffraction fringes at boundaries of the acrylic edge and resulted in an EEI value of 3.64 as compared to 1.44 produced with EID image. At equal MGD levels (standard dose), observer studies along with CNR and SNR analyses revealed a substantial improvement of PCD acquired images in detection of simulated tumors, calcification clusters, and fibrous structures. At 30% less MGD, PCD images preserved image quality to yield equivalent (slightly better) detection as compared to the standard dose EID images. CONCLUSION: CdTe-based PCDs are technically feasible to image breast abnormalities (low/high contrast structures) at low radiation dose levels using the high energy inline phase sensitive imaging technique.

Decision Support System for Breast Cancer Detection Using Biomarker Indicators.

Breast cancer is the second most common type of cancer among women in the USA, and it is very common to appear in its invasive form. Detecting its presence in the early stages can potentially aid in the mortality rate depletion since at that point large tumours are highly unlikely to have developed. Technological advances of the last decades have provided advanced tools that employ machine learning for early detection. Common techniques include tumour imaging using special equipment that in most cases is not widely accessible. In order to overcome this limitation, new techniques that employ blood-based biomarkers are being explored. In the current work machine learning algorithms are exploited for the development of a decision support system for breast cancer using easily obtainable user information, age, body mass index, glucose and resistin. The explored algorithms include Logistic Regression, Naive Bayes, Support Vector Machine and Gradient Boosting Classification, all of which are used for the classification of new patients based on a dataset that includes information from previous breast cancer incidents. The results depict that the optimal algorithm based on the current methodology and implementation is the Gradient Boosting Classification which exhibits the highest prediction scores. In order to ensure wide accessibility, a mobile application is developed. The user can easily provide the required information for the prediction to the application and obtain the results rapidly.

Health Beliefs and Breast Cancer Screening Practices Among African American Women in California.

BACKGROUND: African American women continue to have higher mortality rates of breast cancer when compared to other women, and evidence suggests that early detection of breast cancer can lead to favorable outcomes, yet there remains a paucity of literature about health beliefs and the utilization of three screening practices, namely breast self-examination, clinical breast examination and mammography in California, a state that currently has one of the highest breast cancer mortality rates among African American women. PURPOSE: To investigate the relationship between health beliefs and three breast cancer detection practices, e.g. breast self-examination, clinical breast examination, and mammography in a cohort of African American women. METHODS: Using a descriptive correlational design, a convenience sample of two hundred and eighty-two (n = 282) self-identified women from six regional chapters of a national Black women's political organization in California, completed a Demographic Data Questionnaire and Champion's Health Belief Model Scale which assessed the hypothesized relationships of health beliefs and breast cancer detection practices. RESULTS: Among this culturally diverse group of women (49.8% American, 28.8% African, 21.4% West Indian), health motivation was positively related to the practice of BSE and annual physician visitation for clinical breast examinations. Health locus of control was positively related to the practice of BSE. Having relatives and friends who were diagnosed with breast cancer was strongly associated with having a mammogram and annual physician visitation for clinical breast examinations. CONCLUSION: These findings may be used to target and develop interventions that are tailored to the unique characteristics of these diverse women.

Accurate identification of breast cancer margins in microenvironments of ex-vivo basal and luminal breast cancer tissues using Raman spectroscopy.

Better knowledge of the breast tumor microenvironment is required for surgical resection and understanding the processes of tumor development. Raman spectroscopy is a promising tool that can assist in uncovering the molecular basis of disease and provide quantifiable molecular information for diagnosis and treatment evaluation. In this work, eighty-eight frozen breast tissue sections, including forty-four normal and forty-four tumor sections, were mapped in their entirety using a 250-µm-square measurement grid. Two or more smaller regions of interest within each tissue were additionally mapped using a 25 µm-square step size. A deep learning algorithm, convolutional neural network (CNN), was developed to distinguish histopathologic features with-in individual and across multiple tissue sections. Cancerous breast tissue were discriminated from normal breast tissue with 90 % accuracy, 88.8 % sensitivity and 90.8 % specificity with an excellent Area Under the Receiver Operator Curve (AUROC) of 0.96. Features that contributed significantly to the model were identified and used to generate RGB images of the tissue sections. For each grid point (pixel) on a Raman map, color was assigned to intensities at frequencies of 1002 cm-1 (Phenylalanine), 869 cm-1 (Proline, CC stretching of hydroxyproline-collagen assignment, single bond stretching vibrations for the amino acids proline, valine and polysaccharides) and 1309 cm-1 (CH3/CH2 twisting or bending mode of lipids). The Raman images clearly associate with hematoxylin and eosin stained tissue sections and allow clear visualization of boundaries between normal adipose, connective tissue and tumor. We demonstrated that this simple imaging technique allows high-resolution, straightforward molecular interpretation of Raman images. Raman spectroscopy provides rapid, label-free imaging of microscopic features with high accuracy. This method has application as laboratory tool and can assist with intraoperative tissue assessment during Breast Conserving surgery.

[Research Progress on Electrochemical Biosensors for Breast Cancer Detection Based on Nanomaterials].

Breast cancer is one of the most serious diseases threatening women's life and health in the world, and the mortality rate is the second in the world. With the progress of nanotechnology and the advantages of nanomaterials in the field of electrochemistry and biosensor, various nanomaterials have been applied in electrochemical biosensors. This makes the electrochemical nano-biosensor in the field of rapid detection of breast cancer has been widely concerned and studied. This paper introduces the important components of electrochemical nano-biosensor for breast cancer detection and the research progress of each component in breast cancer detection, as well as the performance of electrochemical nano biosensor in breast cancer detection and the prospect of its application.

Breast Cancer Detection Using Adaptable Textile Antenna Design.

In the field of medicinal applications, early / (Timely) detection of Breast cancer is a key diagnosing process which provides effective medical treatment and also reduces women mortality. Due to the advancement and growth of medical sciences, efficient antennas are needed for imaging, diagnosing and providing superior treatment to the patients. Since tumor is tiny in size at the early stage, the knowledge of its precise location is chiefly required. For this purpose several antennas with high accuracy are designed. Among them, Flexible antenna has several advantages compared to other antennas. The main advantage of flexible antenna is its simple construction, high gain and cost-efficiency. The proposed research work implements a novel flexible antenna for detection of early breast cancer, with and without tumor application. In the study, (for the sake of comprehensive analysis and accuracy / familiarity / simplicity), Jean material is used as dielectric substrate with dielectric constant 1.7. The flexible antennas are designed with a slot loaded over the patch and with ground plane that are made up of copper as the conducting material. The jeans cloth material with 1 mm thickness is considered as a substrate, which is to be placed on the breast surface. Co-axial feeding method is chosen for the proposed antenna which improves the antenna performance. In addition to this, the antenna is a wearable textile type designed for ISM (Industrial, Scientific and Medical) band 2.4 GHz applications. The antenna is simulated using HFSS (High Frequency Structure Spectrum) software. From the simulation analysis, Return loss (S11), Gain in dB, Radiation pattern, axial ratio (AR) and VSWR are obtained and analyzed. Finally, the simulation results are compared with the existing methodologies.

The Effect of the 2009 USPSTF breast cancer screening recommendations on breast cancer in Michigan: A longitudinal study.

In 2009, the revised United States Preventive Services Task Force (USPSTF) guidelines recommended against routine screening mammography for women age 40-49 years and against teaching self-breast examinations (SBE). The aim of this study was to analyze whether breast cancer method of presentation changed following the 2009 USPSTF screening recommendations in a large Michigan cohort. Data were collected on women with newly diagnosed stage 0-III breast cancer participating in the Michigan Breast Oncology Quality Initiative (MiBOQI) registry at 25 statewide institutions from 2006 to 2015. Data included method of detection, cancer stage, treatment type, and patient demographics. In all, 30 008 women with breast cancer detected via mammogram or palpation with an average age of 60.1 years were included. 38% of invasive cancers were identified by palpation. Presentation with palpable findings decreased slightly over time, from 34.6% in 2006 to 28.9% in 2015 (P < .001). Over the 9-year period, there was no statistically significant change in rate of palpation-detected tumors for women age <50 years or ≥50 years (P = .27, .30, respectively). Younger women were more likely to present with palpable tumors compared to older women in a statewide registry. This rate did not increase following publication of the 2009 USPSTF breast cancer screening recommendations.

Preliminary Results of a New Auxiliary Mechatronic Near-Field Radar System to 3D Mammography for Early Detection of Breast Cancer.

Accurate and early detection of breast cancer is of high importance, as it is directly associated with the patients' overall well-being during treatment and their chances of survival. Uncertainties in current breast imaging methods can potentially cause two main problems: (1) missing newly formed or small tumors; and (2) false alarms, which could be a source of stress for patients. A recent study at the Massachusetts General Hospital (MGH) indicates that using Digital Breast Tomosynthesis (DBT) can reduce the number of false alarms, when compared to conventional mammography. Despite the image quality enhancement DBT provides, the accurate detection of cancerous masses is still limited by low radiological contrast (about 1%) between the fibro-glandular tissue and affected tissue at X-ray frequencies. In a lower frequency region, at microwave frequencies, the contrast is comparatively higher (about 10%) between the aforementioned tissues; yet, microwave imaging suffers from low spatial resolution. This work reviews conventional X-ray breast imaging and describes the preliminary results of a novel near-field radar imaging mechatronic system (NRIMS) that can be fused with the DBT, in a co-registered fashion, to combine the advantages of both modalities. The NRIMS consists of two antipodal Vivaldi antennas, an XY positioner, and an ethanol container, all of which are particularly designed based on the DBT physical specifications. In this paper, the independent performance of the NRIMS is assessed by (1) imaging a bearing ball immersed in sunflower oil and (2) computing the heat Specific Absorption Rate (SAR) due to the electromagnetic power transmitted into the breast. The preliminary results demonstrate that the system is capable of generating images of the ball. Furthermore, the SAR results show that the system complies with the standards set for human trials. As a result, a configuration based on this design might be suitable for use in realistic clinical applications.

[Time trend in programme sensitivity within the German mammography screening programme in North Rhine-Westphalia and Lower Saxony]. / Zeitliche Entwicklung der Programmsensitivität des deutschen Mammographie-Screening-Programms in Nordrhein-Westfalen und Niedersachsen.

BACKGROUND: The programme sensitivity is a performance indicator for evaluating the quality of the mammography screening programme (MSP). OBJECTIVES: We analysed the development of the programme sensitivity over time in two federal states of Germany, North Rhine-Westphalia (NRW) and Lower Saxony (NDS). MATERIALS AND METHODS: Data from 2,717,801 (NRW) and 1,197,660 (NDS) screening examinations between 2006 and 2011 were linked with data of the State Cancer Registry NRW and the Epidemiological Cancer Registry NDS, respectively. Breast cancers (invasive and in situ) were either detected at screening or diagnosed within the 24-month interval after an inconspicuous screening result outside the programme. The crude and age-standardized programme sensitivity was calculated per calendar year. The German mammography screening office provided aggregated recall rates. RESULTS: The age-standardized programme sensitivity increased markedly for initial screening examinations from 2006 to 2011 from 75.0% (95% CI: 72.1-77.9) to 80.5% (95% CI: 78.5-82.5) in NRW, and from 74.9% (95% CI: 71.4-78.5) to 84.7% (95% CI: 81.1-88.3) in NDS. Concurrently, recall rates increased as well. For subsequent screening examinations, the programme sensitivity increased from 2008 to 2011 from 68.1% (95% CI: 63.1-73.1) to 71.9% (95% CI: 70.2-73.6) in NRW, and from 69.8% (95% CI: 64.2-75.4) to 74.9% (95% CI: 72.3-77.5) in NDS, whereas the recall rates remained relatively constant. CONCLUSIONS: In both federal states, the programme sensitivity increased over time. This increase, possibly indicating an improved quality of diagnosis within the MSP as a learning system, is discussed under consideration of the age distribution of screening participants and the recall rates.

Gold Nanoparticle-Based Fluorescent Contrast Agent with Enhanced Sensitivity.

Gold nanoparticle (GNP) based contrast agents that are highly specific and sensitive for both optical and X-ray/CT imaging modalities are being developed for detecting the cancer expressing nucleolin and matrix metallo-proteinase 14 (MMP-14) on the cell membrane: Nucleolin is normally present in the nucleus. For many cancer cells, however, it is over-expressed on the cell membrane, having it to be a good cancer marker. Aptamer AS1411 is known to be an excellent target for nucleolin and also known to treat several cancer types; and MMP-14 in cancer is involved in tumor angiogenesis, blood vessel re-organization, and metastasis. In the proposed agent, AS1411 is selected as the cancer targeting molecule; and the unique property of GNPs of modulating fluorescence are utilized to allow the agent to trigger its fluorescence upon reacting with MMP-14, at an enhanced fluorescence level. GNPs are also natural X-ray/CT contrast agent. Here, as a part of on-going development of the dual-modality contrast agent, we report that conjugating a safe, NIR fluorophore Cypate at a precisely determined distance from the GNP enhanced the Cypate fluorescence up to two times. In addition, successful conjugation of the nucleolin target AS1411 onto the GNP was confirmed and among the GNPs size range 5-30 nm tested, 10 nm GNPs showed the highest X-ray/CT enhancement.

Can diffusion tensor anisotropy indices assist in breast cancer detection?

PURPOSE: To evaluate whether the various anisotropy indices derived from breast diffusion tensor imaging (DTI) can characterize the healthy breast structure and differentiate cancer from normal breast tissue. MATERIALS AND METHODS: Six healthy volunteers and retrospectively selected 24 breast cancer patients were imaged at 3T. DTI included two b-values 0 and 700 sec/mm2 with 20-64 gradient directions and TE of 120 or 90 msec. The normalized anisotropy indices: fractional anisotropy (FA), relative anisotropy (RA), and 1-volume ratio (1-VR), as well as the absolute maximal anisotropy index (λ1 -λ3 ) were compared. RESULTS: The spatial distribution of the various anisotropy indices in healthy volunteers exhibited a high congruence (Pearson correlation coefficients range: 0.79-1.0). All indices showed a statistically significant reduction (P < 0.001) following shortening of the diffusion time. Significantly lower λ1 -λ3 values were found in cancers as compared to normal breast tissue (P < 6.0 × 10-7 ), while the values of the normalized indices in cancers were not significantly different from those in normal breast tissue (P < 0.65 for FA, P < 0.6 for RA, and P < 0.2 for 1-VR). The contrast-to-noise ratio of λ1 -λ3 was significantly higher (P < 0.001) than those of the normalized anisotropy indices, and the area under the curve in a receiver operating characteristic analysis exhibited the highest value for λ1 -λ3 (0.89 ± 0.04 vs. 0.51-0.54 for the other anisotropy indices). CONCLUSION: Water diffusion anisotropy in the healthy breast can be similarly mapped by the normalized indices and by λ1 -λ3 . However, the normalized anisotropy indices fail to differentiate cancer from normal breast tissue, whereas λ1 -λ3 can assist in differentiating cancer from normal breast tissue. J. Magn. Reson. Imaging 2016;44:1624-1632.

Comparative Diagnostic Utility of Low-Dose Breast-Specific Gamma Imaging to Current Clinical Standard.

To retrospectively compare low-dose (7-10 mCi) to high-dose (15-30 mCi) breast-specific gamma imaging (BSGI) in the detection of breast cancer. A retrospective review of 223 consecutive women who underwent BSGI exam between February 2011 and August 2013 with subsequent pathologic analysis was performed. Women were divided into low-dose and high-dose groups. The results of BSGI and pathology were compared, and the sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were determined. A subgroup analysis was performed to evaluate specificity using benign follow-up imaging to establish true-negative results. There were 223 women who met inclusion criteria with 109 patients with 153 lesions in the low-dose group and 114 patients with 145 lesions in the high-dose group. Pathologic correlation demonstrates sensitivities of 97.6% (95% CI = 90.9-99.6%) and 94.6% (95% CI = 84.2-98.6%; p = 0.093), PPVs of 62.1% (95% CI = 53.2-70.3%) and 50.5% (95% CI = 40.6-60.3%, p = 0.089), and NPVs of 90.5% (95% CI = 68.2-98.3%) and 92.5% (95% CI = 78.5-98.0%, p = 0.781) in the low-dose and high-dose groups, respectively. Subgroup analysis included 72 patients with 98 lesions in the low-dose group and 116 patients with 132 lesions in the high-dose group, with a specificity of 53.7% (95% CI = 39.7-67.1%) and 66.3% (95% CI = 56.2-75.2%%, p = 0.143), respectively. Low-dose BSGI demonstrated high sensitivity and NPV in the detection of breast cancer comparable to the current standard dose BSGI, with moderate specificity and PPV in a limited subgroup analysis, which was associated with a substantial number of false-positives.

MMP-14 Triggered Fluorescence Contrast Agent.

Matrix metalloproteinase-14 (MMP-14) is involved in cancer invasion, metastasis, and angiogenesis. Therefore, it is considered to be a biomarker for aggressive cancer types, including some of the triple-negative breast cancer. Accurate (i.e., specific) and sensitive detection of MMP-14 can, thus, be important for the early diagnosis of and accurate prognosis for aggressive cancer, including the breast cancer caused by cell line MDA-MB 231. Fluorophore-mediated molecular sensing has been used for detecting biomarkers, for a long time. One way to increase the specificity of the sensing is designing the fluorophore to emit its fluorescence only when it encounters the biomarker of interest. When a fluorophore is placed on the surface of, or very close to a gold nanoparticle (GNP), its fluorescence is quenched. Applying this relationship between the GNP and fluorophore, we have developed a GNP-based, near-infrared fluorescent contrast agent that is highly specific for MMP-14. This agent normally emits only 14-17 % fluorescence of the free fluorophore. When the agent encounters MMP-14, its fluorescence gets fully restored, allowing MMP-14 specific optical signal emission.