Role In Breast Cancer Screening Research Articles

Genome-wide characterization of dynamic DNA 5-hydroxymethylcytosine and TET2-related DNA demethylation during breast tumorigenesis

BackgroundBreast tumorigenesis is a complex and multistep process accompanied by both genetic and epigenetic dysregulation. In contrast to the extensive studies on DNA epigenetic modifications 5-hydroxymethylcytosine (5hmC) and 5-methylcytosine (5mC) in malignant breast tumors, their roles in the early phases of breast tumorigenesis remain ambiguous.ResultsDNA 5hmC and 5mC exhibited a consistent and significant decrease from usual ductal hyperplasia to atypical ductal hyperplasia and subsequently to ductal carcinoma in situ (DCIS). However, 5hmC showed a modest increase in invasive ductal breast cancer compared to DCIS. Genomic analyses showed that the changes in 5hmC and 5mC levels occurred around the transcription start sites (TSSs), and the modification levels were strongly correlated with gene expression levels. Meanwhile, it was found that differentially hydroxymethylated regions (DhMRs) and differentially methylated regions (DMRs) were overlapped in the early phases and accompanied by the enrichment of active histone marks. In addition, TET2-related DNA demethylation was found to be involved in breast tumorigenesis, and four transcription factor binding sites (TFs: ESR1, FOXA1, GATA3, FOS) were enriched in TET2-related DhMRs/DMRs. Intriguingly, we also identified a certain number of common DhMRs between tumor samples and cell-free DNA (cfDNA).ConclusionsOur study reveals that dynamic changes in DNA 5hmC and 5mC play a vital role in propelling breast tumorigenesis. Both TFs and active histone marks are involved in TET2-related DNA demethylation. Concurrent changes in 5hmC signals in primary breast tumors and cfDNA may play a promising role in breast cancer screening.

The Implementation of Artificial Intelligence in Breast Cancer Screening

Breast cancer screening is traditionally administered by radiologists through mammography. Most women should be receiving mammograms once they reach their 40s to detect any signs of cancer early, especially those with high risk. Recently, the development of AI in medicine has proven to be useful in several fields, one of which is breast cancer screening. This literature review looks into the current status of AI in this field and evaluates different models of different types that could contribute to diagnosing breast cancer. The methodology used was searching key terms on the database PubMed, and then compiling and organizing papers into a table that highlighted similar results, which were summarized and analyzed. It was found that AI has the potential to be an accessible option. Deep learning, in particular, has seen the most experimentation. The advantages of using this technology include using it as a complementary tool for double reading, improving the time efficiency for the radiologists without losing accuracy, and its inclusivity throughout all populations. However, there are still limitations because many proposals themselves discuss the work that needs to be done before they can be generalized. External validation studies haven’t yielded ideal results, exposing the complications of using AI. Ethics of using this technology are also important to consider because AI is replacing human tasks. Implementing AI in a prominent role in breast cancer screening should take more examination, but promising developments have already set the framework.

One-step multiplex analysis of breast cancer exosomes using an electrochemical strategy assisted by gold nanoparticles.

Exosomes, as a non-invasive biomarker, perform an important role in breast cancer screening and prognosis monitoring. However, establishing a simple, sensitive, and reliable exosome analysis technique remains challenging. Herein, a one-step multiplex analysis electrochemical aptasensor based on a multi-probe recognition strategy was constructed to analyze breast cancer exosomes. HER2-positive breast cancer cell (SK-BR-3) exosomes were selected as the model targets and three aptamers including CD63, HER2 and EpCAM aptamers were used as the capture units. Methylene blue (MB) functionalized HER2 aptamer and ferrocene (Fc) functionalized EpCAM aptamer, which were modified on gold nanoparticles (Au NPs), i.e. MB-HER2-Au NPs and Fc-EpCAM-Au NPs, were used as signal units. When the mixture of target exosomes, MB-HER2-Au NPs and Fc-EpCAM-Au NPs were added on the CD63 aptamer modified gold electrode, two Au NPs modified by MB and Fc could be specifically captured on the electrode by the recognition of three aptamers with target exosomes. Then one-step multiplex analysis of exosomes was achieved by detecting two independent electrochemical signals. This strategy can not only distinguish breast cancer exosomes from other exosomes (including normal exosomes and other tumor exosomes) but also HER2-positive breast cancer exosomes and HER2-negative breast cancer exosomes. Besides, it had high sensitivity and can detect SK-BR-3 exosomes with a concentration as low as 3.4×103 particles mL-1. Crucially, this method can be applicable to the examination of exosomes in complicated samples, which is anticipated to afford assistance for the screening and prognosis of breast cancer.

Extracting keyframes of breast ultrasound video using deep reinforcement learning.

Ultrasound (US) plays a vital role in breast cancer screening, especially for women with dense breasts. Common practice requires a sonographer to recognize key diagnostic features of a lesion and record a single or several representative frames during the dynamic scanning before performing the diagnosis. However, existing computer-aided diagnosis tools often focus on the final diagnosis process while neglecting the influence of the keyframe selection. Moreover, the lesions could have highly-irregular shapes, varying sizes, and locations during the scanning. The recognition of diagnostic characteristics associated with the lesions is challenging and also faces severe class imbalance. To address these, we proposed a reinforcement learning-based framework that can automatically extract keyframes from breast US videos of unfixed length. It is equipped with a detection-based nodule filtering module and a novel reward mechanism that can integrate anatomical and diagnostic features of the lesions into keyframe searching. A simple yet effective loss function was also designed to alleviate the class imbalance issue. Extensive experiments illustrate that the proposed framework can benefit from both innovations and is able to generate representative keyframe sequences in various screening conditions.

An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach.

Simple SummaryBreast cancer is one of the foremost causes of cancer-related mortality in women. It is curable and controllable only if detected early. Microcalcifications in breast tissue are essential predictors for radiologists to detect early-stage breast cancer. This study proposes a method for detecting and classifying microcalcifications in mammogram images to predict breast lesions, using machine learning coupled with an interpretable radiomics approach. The method was evaluated using a publicly accessible dataset, which may aid radiologists and clinicians in identifying breast cancer in their regular clinical practices. This study contributes to the field of predictive modeling in healthcare.Microcalcifications in breast tissue can be an early sign of breast cancer, and play a crucial role in breast cancer screening. This study proposes a radiomics approach based on advanced machine learning algorithms for diagnosing pathological microcalcifications in mammogram images and provides radiologists with a valuable decision support system (in regard to diagnosing patients). An adaptive enhancement method based on the contourlet transform is proposed to enhance microcalcifications and effectively suppress background and noise. Textural and statistical features are extracted from each wavelet layer’s high-frequency coefficients to detect microcalcification regions. The top-hat morphological operator and wavelet transform segment microcalcifications, implying their exact locations. Finally, the proposed radiomic fusion algorithm is employed to classify the selected features into benign and malignant. The proposed model’s diagnostic performance was evaluated on the MIAS dataset and compared with traditional machine learning models, such as the support vector machine, K-nearest neighbor, and random forest, using different evaluation parameters. Our proposed approach outperformed existing models in diagnosing microcalcification by achieving an 0.90 area under the curve, 0.98 sensitivity, and 0.98 accuracy. The experimental findings concur with expert observations, indicating that the proposed approach is most effective and practical for early diagnosing breast microcalcifications, substantially improving the work efficiency of physicians.

Considering breast density for the classification of benign and malignant mammograms

Background and objectiveMammography plays a crucial role in breast cancer screening because it can be used to diagnose a breast mass and breast calcification region early. Mammogram has some drawbacks; for example, detecting breast cancer masses in extremely dense breast tissue is considerably difficult. Thus, this study combined breast density with benign and malignant masses to classify breast tumors. MethodThis study used data augmentation to preprocess mammograms and then convolutional neural network (CNN) models namely AlexNet, DenseNet, and ShuffleNet to classify the images. The accuracies of the three models before and after data augmentation were compared. ResultsBefore data augmentation, the accuracies for training and testing sets of AlexNet were 40.47 % and 38.57 %, respectively, those of DenseNet were 90.00 % and 46.39 %, respectively, and those of ShuffleNet were 96.48 % and 42.86 %, respectively. After data augmentation, the accuracies for training and testing sets of AlexNet were 99.35 % and 95.46 %, respectively, those of DenseNet were 99.91 % and 99.72 %, respectively, and those of ShuffleNet were 99.85 % and 97.84 % respectively. ConclusionsData augmentation allows the model to learn more pictures of different situations and angles to accurately classify new images. Although the accuracy of DenseNet was the highest, the execution time required was considerably longer than that required for ShuffleNet. Considering the accuracy and execution time, ShuffleNet is recommended to be used for the classification of benign and malignant mammography breast tumors.

Novel Approaches to Screening for Breast Cancer.

Screening for breast cancer reduces breast cancer-related mortality and earlier detection facilitates less aggressive treatment. Unfortunately, current screening modalities are imperfect, suffering from limited sensitivity and high false-positive rates. Novel techniques in the field of breast imaging may soon play a role in breast cancer screening: digital breast tomosynthesis, contrast material-enhanced spectral mammography, US (automated three-dimensional breast US, transmission tomography, elastography, optoacoustic imaging), MRI (abbreviated and ultrafast, diffusion-weighted imaging), and molecular breast imaging. Artificial intelligence and radiomics have the potential to further improve screening strategies. Furthermore, nonimaging-based screening tests such as liquid biopsy and breathing tests may transform the screening landscape. © RSNA, 2020 Online supplemental material is available for this article.

Dataset of breast mammography images with masses.

Among many cancers, breast cancer is the second most common cause of death in women. Early detection and early treatment reduce breast cancer mortality. Mammography plays an important role in breast cancer screening because it can detect early breast masses or calcification region. One of the drawbacks in breast mammography is breast cancer masses are more difficult to be found in extremely dense breast tissue. We select 106 breast mammography images with masses from INbreast database. Through data augmentation, the number of breast mammography images was increased to 7632. We utilize data augmentation on breast mammography images, and then apply the Convolutional Neural Networks (CNN) models including AlexNet, DenseNet, and ShuffleNet to classify these breast mammography images.

Demonstration of the value of diffusion weighted MR imaging for differentiation of benign from malignant breast lesions

Background: Radiologic evaluation of breast lesions is being achieved through several imaging modalities. Mammography has an established role in breast cancer screening and diagnosis. Still however, it shows some limitations particulary in dense breast.
 Methods : Magnetic resonance imaging is an attractive tool for the diagnosis of breast tumors1 and the use of magnetic resonance imaging of the breast is rapidly increasing as this technique becomes more widely available.1 As an adjunct to mammography and ultrasound, MRI can be a valuable addition to the work-up of a breast abnormality. MRI has the advantages of providing a three-dimensional view of the breast, performing with high sensitivity in dense breast tissue and using non-ionizing radiation.2
 Results : Recent advances in MRI have shown the potential in bridging the gap between sensitivity and specificity. Methods based on differences in physiological, cellular and biochemical characteristics of malignant, benign and normal tissues were developed to monitor changes in diffusion. Among these techniques is diffusion- weighted MRI (DWI).3
 Diffusion-weighted magnetic resonance imaging detects Brownian motion of water protons, thus reflecting the biologic character of tissue. The apparent diffusion coefficient (ADC) is used to quantify the Brownian motion4-5
 Conclusion :The use of DWI for breast tumors has recently been considered in clinical application, and many studies have shown lower ADC values for breast cancer compared to normal breast tissue or benign tumors 3.

El papel de la tomosíntesis en el cribado del cáncer de mama

Due to the limitations of screening mammography, especially in dense breasts, tomosynthesis has emerged as a new complementary technique. This tomographic technique is able to increase the detection of cancers not seen on conventional mammography and to reduce the false-positive results due to superimposed normal fibroglandular tissue. In this article, we review the role of tomosynthesis in screening by assessing 3 prospective European trials: OSLO, STORM and MALMÖ. Although the mammographic technique used in the MALMÖ trial differed from that used in the other 2 trials, sensitivity significantly increased in all 3 trials (32, 34 and 43%, respectively). Recall rates showed greater differences (+32%, −27%, +43%). Despite the increase in reading time, tomosynthesis is a cost-effective technique with little impact on overdiagnosis. Therefore, this X-ray imaging modality could play an important role in breast cancer screening.

Usefulness of Salivary and Serum Auto-antibodies Against Tumor Biomarkers HER2 and MUC1 in Breast Cancer Screening.

The aim of this work was to investigate if serum and salivary auto-antibodies, isotypes IgG and IgM, against HER2 and MUC1 tandem repeat fragments could play a role in breast cancer screening. Our case-control study was conducted in breast cancer patients, in early stages (n=29), at the gynecology service, Maternity Souissi Hospital, Rabat, Morocco and healthy woman (n=31). Salivary and serum auto-antibodies against HER2 and MUC1 (tandem repeat) were assessed by enzyme-linked immunosorbent assay (ELISA) and compared between patients and healthy women using the Mann-Whitney U test. A P-value <0.05 was considered to be statistically significant. Our data showed higher expression of all serum and salivary autoantibodies in patients as compared to healthy women p<0.05. However, serum IgM anti-MUC1 expression did not show a significant difference between cases and controls (p=0.79). Similarly, salivary IgG anti-HER2 expression did not differ (p=0.15). The correlation between the different isotypes of antibodies revealed that the highest correlation was between salivary IgG anti-HER2 and salivary IgG anti- MUC1(r=0.65). In fact, we have found in saliva the correlation between autoantibodies anti-MUC1 and anti-HER2 more important than in serum (r=0.59 and r=0. 55). However, the correlation between serum and saliva values for all antibodies was weak. Autoantibodies against HER2 and MUC1 may provide a useful approach in breast cancer screening when using both serum and saliva values.

A Metric for Reducing False Positives in the Computer-Aided Detection of Breast Cancer from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Screening Examinations of High-Risk Women.

Magnetic resonance imaging (MRI)-enabled cancer screening has been shown to be a highly sensitive method for the early detection of breast cancer. Computer-aided detection systems have the potential to improve the screening process by standardizing radiologists to a high level of diagnostic accuracy. This retrospective study was approved by the institutional review board of Sunnybrook Health Sciences Centre. This study compares the performance of a proposed method for computer-aided detection (based on the second-order spatial derivative of the relative signal intensity) with the signal enhancement ratio (SER) on MRI-based breast screening examinations. Comparison is performed using receiver operating characteristic (ROC) curve analysis as well as free-response receiver operating characteristic (FROC) curve analysis. A modified computer-aided detection system combining the proposed approach with the SER method is also presented. The proposed method provides improvements in the rates of false positive markings over the SER method in the detection of breast cancer (as assessed by FROC analysis). The modified computer-aided detection system that incorporates both the proposed method and the SER method yields ROC results equal to that produced by SER while simultaneously providing improvements over the SER method in terms of false positives per noncancerous exam. The proposed method for identifying malignancies outperforms the SER method in terms of false positives on a challenging dataset containing many small lesions and may play a useful role in breast cancer screening by MRI as part of a computer-aided detection system.

A hierarchical model for automated breast lesion detection from ultrasound 3D data.

Ultrasound imaging plays an important role in breast cancer screening for which early and accurate lesion detection is crucial for clinical practice. Many researches were performed on supporting the breast lesion detection based on ultrasound data. In the paper, a novel hierarchical model is proposed to automatically detect breast lesion from ultrasound 3D data. The model simultaneously considers the data information from low-level to high-level for the detection by processing with a joint probability. For each layer of the model, the corresponding algorithm is performed to denote the certain level image information. A dynamic programming approach is applied to efficiently obtain the optimal solution. With a preliminary dataset, the superior performance of the proposed model has been demonstrated for the automated detection of breast lesion with 0.375 false positive per case at 91.7% sensitivity.

Factors influencing breast cancer screening and risk assessment among young African American women: An integrative review of the literature.

This integrative review was conducted to examine factors that influence mammography screening and use of cancer genetic services among younger African American women at increased risk for developing breast cancer. Research articles were identified using PubMed, CINAHL, PsychINFO, and Cochrane library to find studies published from 2003 to 2013. Findings from this review indicate that while younger African American women receive mammograms more often than the general population, they are not being referred for genetic testing when appropriate. This is a major concern because African American women tend to experience more aggressive forms of breast cancer at an earlier age than the general population; it is imperative that they undergo genetic testing for optimal management of their breast cancer risk. Nurse practitioners have a significant role in breast cancer screening and genetic testing of at-risk women, particularly in identifying and referring young women for testing. Further communication efforts are needed to improve young women's knowledge of breast cancer risk and the benefits of genetic testing. Reducing barriers to breast healthcare services requires nursing efforts that focus on populations at greatest risk for poor health outcomes.

乳癌検診における産婦人科医の役割

乳癌検診における産婦人科医の役割

乳腺専門クリニックにおける高知県乳癌検診の役割

乳腺専門クリニックにおける高知県乳癌検診の役割

Breast cancer screening beliefs, recommendations and practices

Primary care physicians (PCPs) play a key role in breast cancer screening, yet no current data exist regarding PCP practices. The authors analyzed a nationally representative survey of PCPs that was fielded during September 2006 to May 2007 to investigate PCP breast cancer screening beliefs, recommendations, and practices. Most of the 1212 PCPs who participated in the survey (80%) reported that mammography for average-risk women aged ≥ 50 years was very effective in reducing cancer mortality, and 54% reported that it was very effective for women ages 40 to 49 years. Fewer respondents reported that clinical breast examination (CBE) or breast self-examination (BSE) was very effective, but the majority rated CBE and BSE as somewhat effective. The majority of PCPs routinely recommended mammography, CBE, and BSE to patients aged ≥ 40 years. In multivariate models, family/general practitioners (odds ratio [OR], 2.23; 95% confidence interval [CI], 1.57-3.17 for mammography; OR, 4.42; 95% CI, 2.60-7.52 for CBE) and internal medicine specialists (OR, 3.21; 95% CI, 2.21-4.66 for mammography; OR, 5.34; 95% CI, 3.21-8.88 for CBE) were more likely to recommend an upper age limit for screening than obstetrician/gynecologists. Physicians who reported that US Preventive Services Task Force guidelines were very influential were more likely to recommend an age at which they no longer recommend mammography and CBE. To the authors' knowledge, the current study is the first national study in over 2 decades to report the breast cancer screening practices of PCPs and provides baseline data for monitoring the impact of changes in clinical practice guidelines. The current findings suggested that virtually all PCPs routinely recommend mammography, CBE, and BSE to their patients aged ≥ 40 years, although recommendations vary by primary care specialty.

Breast Ct: Potential For Breast Cancer Screening and Diagnosis

Although screening mammography has been shown to be effective in reducing breast cancer mortality, a new technique called breast computed tomography (CT) is being studied in the hope that breast cancer can be detected even earlier. A prototype unit has been designed, fabricated and tested at the University of California, Davis, USA, and is currently being used in a Phase II clinical trial to study the feasibility of breast CT for breast cancer detection and diagnosis. A total of 46 volunteers and patients have been imaged, and the breast CT images show impressive anatomical detail of the breast that is not appreciated in mammography. The radiation dose levels needed to produce excellent image quality are equal to two-view mammography. Further study of breast CT is needed to better understand its potential role in breast cancer screening and diagnosis.

Breast self-examination: who teaches it, who is taught, and how often? (United States).

Although the effectiveness of breast self-examination (BSE) has not been established, it is widely practiced and taught. However, it is not clear which patients and providers are involved in BSE teaching, nor how often it occurs. We undertook this study at a large New England Health Maintenance Organization (HMO) to answer these questions. The study was a retrospective cohort of 2242 randomly selected women aged 40-69 with no history of breast cancer and at least one screening clinical breast examination, followed over a ten-year period, including their medical providers (n = 356). Data were collected via computerized medical records. Sixty-eight percent of women had documented BSE teaching at least once in the ten years, increasing from 13% taught in 1983 to 36% in 1993 (p = 0.001). Teaching was related to younger age, increasing numbers of clinical breast examinations, screening mammograms, and prior BSE teachings. Patient race, income, family history of breast cancer, and estrogen replacement use were not related to teaching. Sixty-one percent of all providers taught BSE at least once during the ten years. Internists (OR 6.37, 95% CI 2.23-17.3) and non-physician providers (OR 12.8, 95% CI 3.0-54.4) were more likely to teach at least half of their patients than were obstetrician-gynecologists. Recent medical school graduates taught BSE more often. Provider gender was not associated with teaching. Over two-thirds of women patients in this HMO setting were taught BSE and the majority of providers taught BSE at least once during the ten years. Rates of teaching tripled over the decade. It is important to establish the effectiveness of BSE and its appropriate role in breast cancer screening.

Cultural Barriers to Mammography, Clinical Breast Exam, and Breast Self-Exam among Chinese-American Women 60 and Older

Background: This study examined screening utilization at least once and regular adherence to mammography, clinical breast exam, and breast self-exam among older Chinese-American women.Method: One hundred women were recruited from senior centers in two metropolitan cities. Participants completed a questionnaire that included sections on demographics, health history, health insurance coverage, breast cancer screening, common and cultural barriers to screening, and acculturation.Results: Logistic regression models found insurance coverage for mammography and acculturation to be significant predictors of having had a mammogram at least once. Low perceived need/lack of physician recommendation and recency of physical examination were significant predictors of having had a mammogram in the past year. Acculturation and modesty were significant predictors of having had a clinical breast exam at least once, while recency of physical examination was a significant predictor of having had a clinical breast exam in the past year. Reliance on medial professionals for screening and forgetting were significant predictors of having performed breast self-exam at least once, and forgetting was a significant predictor of regular performance of breast self-exam.Conclusions: These findings suggest that both common and cultural barriers play a role in breast cancer screening among older Chinese-American women, with cultural factors being more influential in the initiation of cancer screening behavior.