Breast Cancer Screening Techniques Research Articles

Classifying Breast Tumors in Digital Tomosynthesis by Combining Image Quality-Aware Features and Tumor Texture Descriptors

Digital breast tomosynthesis (DBT) is a 3D breast cancer screening technique that can overcome the limitations of standard 2D digital mammography. However, DBT images often suffer from artifacts stemming from acquisition conditions, a limited angular range, and low radiation doses. These artifacts have the potential to degrade the performance of automated breast tumor classification tools. Notably, most existing automated breast tumor classification methods do not consider the effect of DBT image quality when designing the classification models. In contrast, this paper introduces a novel deep learning-based framework for classifying breast tumors in DBT images. This framework combines global image quality-aware features with tumor texture descriptors. The proposed approach employs a two-branch model: in the top branch, a deep convolutional neural network (CNN) model is trained to extract robust features from the region of interest that includes the tumor. In the bottom branch, a deep learning model named TomoQA is trained to extract global image quality-aware features from input DBT images. The quality-aware features and the tumor descriptors are then combined and fed into a fully-connected layer to classify breast tumors as benign or malignant. The unique advantage of this model is the combination of DBT image quality-aware features with tumor texture descriptors, which helps accurately classify breast tumors as benign or malignant. Experimental results on a publicly available DBT image dataset demonstrate that the proposed framework achieves superior breast tumor classification results, outperforming all existing deep learning-based methods.

A study on awareness of breast cancer and breast self-examination practices among women above 35 years of age in rural areas of Wardha District, Maharashtra: a protocol

Background Breast cancer is a major cause of morbidity and mortality in women and a global health issue. Breast self-examination (BSE) is a recommended breast cancer screening technique that a woman can use on her own, in the privacy of her home. Hence, awareness regarding it and knowledge to identify any changes will be a boon to the early identification and promotion of treatment. This study aimed to increase awareness and educate women about breast self-examination (BSE) to facilitate the early detection of breast cancer in rural areas of Wardha District. Methods A community-based cross-sectional study was conducted on rural women in Wardha district. Women older than 35 years of age will be included in the study and assessed using a ‘pre-designed’ and ‘pre-structured’ questionnaire inquiring about their socio-demographic profile, awareness of breast cancer, and breast self-examination. Research implications Most studies show how to self-examine breasts, but awareness and knowledge regarding the subject are not well demonstrated and applied in many studies. Although we now understand that women do not always need to use a specific BSE technique, they should still be taught and encouraged to recognize breast changes. BSE for early detection of breast cancer has undergone a paradigm change or development into breast awareness. The progression towards breast awareness is a concept where a woman is familiar with her breasts so that she detects any change that might emerge and brings this to the attention of her healthcare practitioner right away, whereas BSE is monthly palpation of the breasts in a specific manner in which women are professionally taught. Early detection of breast cancer relies heavily on breast awareness, which is why it should be covered in general breast health education.

The Relationship Between Breast Cancer Risk Factors and Mammographic Density

Background: Mammography is the most important evaluation technique for breast cancer screening. The importance of understanding mammographic density becomes evident when considering its relationship with breast cancer risk. There exists a positive correlation between high mammographic density and an increased susceptibility to breast cancer. Therefore, knowing the prevalence of breast density and its associated factors helps consider possible interventions and medical management to alter the density and properly follow up with the patients. Methods: A total of 350 women in Azerbaijan enrolled in this cross-sectional study. Patient’s questionnaires included demographic data such as age, weight, height, education, menarche age, menopause status and age, oophorectomy history, marital status, number of successful deliveries, age of first delivery, number of breast-feeding times, duration of breastfeeding, oral contraceptives/ hormone replacement therapy (HRT) usage, menstrual status, familial history of cancer, history of benign breast lesion biopsy, and alcohol/smoking status. The evaluation of breast density was done according to the American College of Radiology grading A to D. Results: The average age of the studied patients was 45.68±6.96 years with a median of 44.5 years. The population of women with breast density above 50% was higher compared to those with low breast density (51.4% versus 48.6%). In women without a history of breastfeeding, the frequency of breast density above 50% was observed in 76.4% of women. Significantly, lower densities were observed during menopause, and higher densities were observed during nonmenopause (P=0.001). Furthermore, no relationship was observed between breast composition and nulliparity, history of oophorectomy, duration of breastfeeding, marital status, menstrual status, and oral contraceptive pill (OCP) consumption. Conclusion: The main risk factors of high breast density include young age, menstrual status (non-menopausal patients), lack of breastfeeding after childbirth, older age of first delivery, and lower body mass index

Novel Technologies in Breast Imaging: A Scoping Review.

Breast cancer is one of the leading causes of death in the United States and can cause considerable suffering for not only the patient but their families as well. The current mainstay of screening is mammography, although this screening modality has its drawbacks. Multiple technologies have been recently explored in hopes of increasing breast cancer detection rates and decreasing false positive rates. Overall, improving breast cancer screening techniques has the potential to decrease cost, patient anxiety, and the use of unnecessary procedures. This review discusses multiple modalities including digital breast tomosynthesis, contrast-enhanced dual-energy digital mammography (CE DE DM), MRI with diffusion-weighted sequences and proton magnetic resonance spectroscopy. This paper was written with the objective of synthesizing information across several databasesto provide clinicians with a more accessible tool to understand the underlying concepts behind these imaging modalities, as well as present reviewed data which highlights the benefits and drawbacks of these breast cancer-detecting techniques.

Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection

Breast cancer is a serious medical condition that requires early detection for successful treatment. Mammography is a commonly used imaging technique for breast cancer screening, but its analysis can be time-consuming and subjective. This study explores the use of deep learning-based methods for mammogram analysis, with a focus on improving the performance of the analysis process. The study is focused on applying different computer vision models, with both CNN and ViT architectures, on a publicly available dataset. The innovative approach is represented by the data augmentation technique based on synthetic images, which are generated to improve the performance of the models. The results of the study demonstrate the importance of data pre-processing and augmentation techniques for achieving high classification performance. Additionally, the study utilizes explainable AI techniques, such as class activation maps and centered bounding boxes, to better understand the models’ decision-making process.

Application of serum SERS technology based on thermally annealed silver nanoparticle composite substrate in breast cancer.

Liquid biopsy is currently a non-destructive and convenient method of cancer screening, due to human blood containing a variety of cancer-related biomolecules. Therefore, the development of an accurate and rapid breast cancer screening technique combined with breast cancer serum is crucial for the treatment and prognosis of breast cancer patients. In this study, the surface enhanced Raman spectroscopy (SERS) technique is used to enhance the Raman spectroscopy (RS) signal of serum based on a high sensitivity thermally annealed silver nanoparticle/porous silicon bragg mirror (AgNPs/PSB) composite substrate. Compared with RS, SERS reflects more and stronger spectral peak information, which is beneficial to discover new biomarkers of breast cancer. At the same time, to further explore the diagnostic ability of SERS technology for breast cancer. In this study, the raw spectral data are processed by baseline correction, polynomial smoothing, and normalization. Then, the relevant feature information of SERS and RS is extracted by principal component analysis (PCA), and five classification models are established to compare the diagnostic performance of SERS and RS models respectively. The experimental results show that the breast cancer diagnosis model based on the improved SERS substrate combined with the machine learning algorithm can be used to distinguish breast cancer patients from controls. The accuracy, sensitivity, specificity and AUC values of the SVM model are 100%, 100%, 100% and 100%, respectively, as well as the training time of 4ms. The above experimental results show that the SERS technology based on AgNPs/PSB composite substrate, combined with machine learning methods, has great potential in the rapid and accurate identification of breast cancer patients.

Evaluation of CZT Drift Strip Detectors for Use in 3-D Molecular Breast Imaging

X-ray mammography is a widely used technique for breast cancer screening. However, the technique is imprecise when it comes to radiographically dense breast tissue, and therefore a supplemental screening technique, such as molecular breast imaging (MBI), could increase the cancer detection rate. Emerging technologies within MBI require excellent detector performance, preferably with a submillimeter intrinsic spatial resolution. A collaboration between Kromek and DTU Space aims to advance the DTU Space developed 3-D CdZnTe (CZT) drift strip technology, for application in new emerging MBI systems. This collaboration has resulted in ten compact 3-D MBI test modules with the goal of producing high-performance and high yield detectors. In this article, we present overall excellent detector performance, submillimeter position resolution at both 122 and 661.6 keV, and good energy resolution for the applied electrode deposition and contact optimization. Although the current experimental setup and results suffer from high electronic noise (using discrete NIM standard charge sensitive preamplifiers), we conclude that the measured spatial and spectral performance fulfills the expected requirements for the modules, with room for improvement, especially within limiting electronic noise. The detector test modules indicate a promising future for the 3-D CZT drift strip technology within future emerging MBI systems.

Thermography as a Breast Cancer Screening Technique: A Review Article.

Globally, breast cancer is the most frequently occurring cancer in women and is the reason for more disability-adjusted life years lost than any other type of cancer. Hence, early screening plays a vital role in reducing breast cancer mortality. Although mammography is the standard procedure used for screening and diagnosis of breast cancer, it still has some limitations. Other methods used for screening include ultrasound and clinical breast examination. Despite its limitations, mammography is the gold standard for screening breast malignancy. Another emerging method for screening is thermography. With recent technological advances, breast cancer screening through thermography has demonstrated several advantages over existing modalities. For this review, a literature search was performed using databases such as PubMed, Google Scholar, and ScienceDirect. The keywords searched included breast cancer, early detection, breast cancer screening, mammography, and thermography. This review discusses the benefits of thermography showing that it can be a significant modality for breast cancer screening. The recent developments in thermal sensors, imaging protocols, and computer-aided software diagnostics hold great promise for making this technique a mainstream screening method for cancer. Moreover, the use of artificial intelligence and thermal imaging to detect early-stage breast cancer can provide impressive results. Therefore, thermography will be a promising technology for the early detection of breast cancer.

Assessment of Knowledge About Breast Cancer Screening Among Female Faculty of University of Lahore

Carcinoma of breast is now the most frequent of all cancers, both in male and female malignancies. It is also the fifth leading cause of death from all types of malignancies, and the first among those tumors that kill women. Objective: To assess the knowledge about breast cancer screening among female faculty of University of Lahore, Pakistan. Methods: This was a cross-sectional descriptive research that used a non-probability purposive sampling technique. After getting permission from the participants, data from 356 female faculty members at the University of Lahore was obtained. SPSS version 22 was used to evaluate and enter the data. Results: The findings suggested that female faculty members had a moderate understanding of cancer screening. Even though majority of the participants were aware that mammography is a fundamental and conventional technique for breast cancer screening, they were unaware of when to begin and how frequently it should be done. Respondents also knew what breast self-examination (BSE) and clinical breast examination (CBE) were, but they didn't know when they should begin or how often they should be conducted. Conclusions: In summary, female academic staff at the University of Lahore have a moderate level of knowledge. It is suggested that breast cancer screening methods be made more widely known and educated about through frequent conferences and seminars highlighting the proper age and time for starting BSE, CBE, and Mammography, as well as the need of doing these procedures on a regular basis.

Breast Cancer Screening Practices Amongst Female Students in Pakistan

Cancer is currently responsible for around 6 million deaths worldwide. Cancer-related deaths are predicted to rise to 74% in the next several years which is alarming for the developing countries. Breast cancer is one of the most common cancers in women and is also the leading cause mortality. Objective: The purpose of this study was to determine the practices opted by Pakistani women about breast self and clinical Examination and screening modalities. Methods: Total 453 selected female University students using the "Epitool" online sample calculation website with a 95% confidence interval and a 5% margin of error were selected. All girls between the age of 18-55 years who were currently enrolled in one of the study programs and could complete the English questionnaire were included in the study. The Breast Cancer Investigation Questionnaire (BCIQ) was used in study. SPSS version 25.0 was used to analyze the data. Results: The respondents' mean age was 22.87± 4.67 years. The prevalence of knowledge of breast cancer screening techniques was high 341 (75.3%), 245(53.3%) and 273 (60%), respectively, for breast self-examination, clinical breast examination, and mammography. However, only 345 (75.1%), 303 (36.9%), and 255 (56.2%) knew BSE, CBE, and mammography are helpful in breast cancer diagnosis respectively. Conclusion: Even though many females were aware of breast cancer and self-examination, the number of women who do BSE was alarmingly low.

Advances in breast cancer screening modalities and status of global screening programs.

Breast cancer (BC) is the most prevalent malignancy worldwide, and a continued upward trend has been predicted in the coming decades. Screening in selected targeted populations, which is effective in reducing cancer‐related mortality, has been widely implemented in many countries. This review summarizes the advances in BC screening techniques, organized or opportunistic BC screening programs across different countries, and screening modalities recommended by different academic authorities. Mammography is the most widely used and effective technique for BC screening. Other complementary techniques include ultrasound, clinical breast examination, and magnetic resonance imaging. Novel screening tests, including digital breast tomosynthesis and liquid biopsies, are still under development. Globally, the implementation status of BC screening programs is uneven, which is reflected by differences in screening modes, techniques, and population coverage. The recommended optimal screening strategies varied according to the authoritative guidelines. The effectiveness of current screening programs is influenced by several factors, including low detection rate, high false‐positive rate, and unsatisfactory coverage and uptake rates. Exploration of accurate BC risk prediction models and the development of risk‐stratified screening strategies are highly warranted in future research.

Generating Full-Field Digital Mammogram From Digitized Screen-Film Mammogram for Breast Cancer Screening With High-Resolution Generative Adversarial Network.

PurposeDeveloping deep learning algorithms for breast cancer screening is limited due to the lack of labeled full-field digital mammograms (FFDMs). Since FFDM is a new technique that rose in recent decades and replaced digitized screen-film mammograms (DFM) as the main technique for breast cancer screening, most mammogram datasets were still stored in the form of DFM. A solution for developing deep learning algorithms based on FFDM while leveraging existing labeled DFM datasets is a generative algorithm that generates FFDM from DFM. Generating high-resolution FFDM from DFM remains a challenge due to the limitations of network capacity and lacking GPU memory.MethodIn this study, we developed a deep-learning-based generative algorithm, HRGAN, to generate synthesized FFDM (SFFDM) from DFM. More importantly, our algorithm can keep the image resolution and details while using high-resolution DFM as input. Our model used FFDM and DFM for training. First, a sliding window was used to crop DFMs and FFDMs into 256 × 256 pixels patches. Second, the patches were divided into three categories (breast, background, and boundary) by breast masks. Patches from the DFM and FFDM datasets were paired as inputs for training our model where these paired patches should be sampled from the same category of the two different image sets. U-Net liked generators and modified discriminators with two-channels output, one channel for distinguishing real and SFFDMs and the other for representing a probability map for breast mask, were used in our algorithm. Last, a study was designed to evaluate the usefulness of HRGAN. A mass segmentation task and a calcification detection task were included in the study.ResultsTwo public mammography datasets, the CBIS-DDSM dataset and the INbreast dataset, were included in our experiment. The CBIS-DDSM dataset includes 753 calcification cases and 891 mass cases with verified pathology information, resulting in a total of 3568 DFMs. The INbreast dataset contains a total of 410 FFDMs with annotations of masses, calcifications, asymmetries, and distortions. There were 1784 DFMs and 205 FFDM randomly selected as Dataset A. The remaining DFMs from the CBIS-DDSM dataset were selected as Dataset B. The remaining FFDMs from the INbreast dataset were selected as Dataset C. All DFMs and FFDMs were normalized to 100μm × 100μm in our experiments. A study with a mass segmentation task and a calcification detection task was performed to evaluate the usefulness of HRGAN.ConclusionsThe proposed HRGAN can generate high-resolution SFFDMs from DFMs. Extensive experiments showed the SFFDMs were able to help improve the performance of deep-learning-based algorithms for breast cancer screening on DFM when the size of the training dataset is small.

‘I want to live for them’; Determinants of Uptake of Clinical Breast Examination: A Qualitative Inquiry

BackgroundClinical breast examination is still relevant in resource-constrained countries for the early detection of breast cancer. Uptake of breast cancer screening programmes in developing countries remains low. It is imperative to explore the factors that influence women’s decision to participate in screening services.ObjectiveTo examine the perceptions, motivations, and barriers to uptake of clinical breast examination.MethodsThis was a descriptive qualitative inquiry. Twelve women who participated in clinical breast examinations were purposively sampled for semi-structured interviews. Interviews were audio-taped, transcribed verbatim, and thematically analysed.ResultsFear of death, personal interest, excellent customer service, and public education motivated participation in the clinical breast examination. Perceived benefits included psychological relief, early detection, inspiring other women, and the acquisition of skills and knowledge. Anxiety about being diagnosed with breast cancer, financial constraints, poor service delivery, and peer pressure were the perceived barriers. Participants who felt anxious about breast cancer performed self-breast examinations more often than the standard practice. The knowledge of participants about breast cancer and breast cancer screening techniques was generally poor.ConclusionHealth interventions designed to promote health seeking behaviors need to be realigned to reflect the evidence on the factors influencing the uptake of these interventions.
 Rwanda J Med Health Sci 2022;5(1):85-98

Influential factors and prediction model of mammographic density among Chinese women.

To evaluate the characteristics and influential factors of breast density and establish a new model for predicting breast density in Chinese women, so as to provide a basis for breast cancer screening techniques and duration.A total of 9412 women who were selected from screening and intervention techniques for Breast and Cervical Cancer Project between April 2018 and June 2019 were enrolled in this study. Selected women were randomly assigned to training and validation sets in a ratio of 1:1. Univariable and multivariable analyzes were performed by Logistic regression model. Nomogram was generated according to the results of multivariate analysis. Calibration, area under curve (AUC) and akaike information criterion (AIC) were used for measuring accuracy of prediction model.There were 377 (4.0%) women in breast imaging reporting and data system (BI-RADS) A category, 2164 (23.0%) in B category, 5749 (61.1%) in C category and 1122 (11.9%) in D category. Age duration, educational attainment, history of benign diseases, breastfeeding history, menopausal status, and body mass index (BMI) were imputed as independent influential factors for breast density in multivariable analysis. The AUC and AIC of training and validation set were 0.7158, 0.7139, and 4915.378, 4998.665, respectively.This study indicated that age, educational attainment, history of benign breast disease, breastfeeding history, menopausal status and BMI were independent influential factors of breast density. Nomogram generated on the basis of these factors could relatively predict breast density, which in turn could be used for recommendations of breast cancer screening techniques.

Effect of GLCM Texture Features on the Medio-Lateral Oblique (MLO) View of Digital Mammograms for Breast Cancer Detection

Breast cancer is one among the most common cancers in women. The early detection of breast cancer reduces the risk of death. Mammograms are an efficient breast imaging technique for breast cancer screening. Computer aided diagnosis (CAD) systems reduce manual errors and helps radiologists to analyze the mammogram images. The mammogram images are typically in two views, cranial-caudal (CC) and medio lateral oblique (MLO) views. MLO contains pectoral muscles (chest muscles) at the upper right or left corner of the image. In this study, it was removed by using a semi-automated method. All the normal and abnormal images were filtered and enhanced to improve the quality. GLCM (Gray Level Co-occurrence Matrix) texture features were extracted and analyzed by changing the number of features in a feature set. Linear Support Vector Machine (LSVM) was used as classifier. The classification accuracy was improved as the number of features in GLCM feature set increases. Simulation results show an overall classification accuracy of 96.7% with 19 GLCM features using SVM classifiers.

Optimizing Infrared Camera Resolution for Small Object Detection using Subpixel Rendering and PIFS in Multiresolution Image Analysis.

Background: Breast cancer screening techniques have been developing rapidly in the field of imaging systems. One of these techniques is thermography, which is an alternative modality for mammography to detect breast lesions. Thermography utilization has been progressively developing as various models and methods of object processing improvement. Currently, the Fluke TIS20 infrared camera, with a resolution of 320 × 240, has not been used to measure precisely small objects such as early breast cancer lesions. Retrieval and processing of single images lead into imprecise object measurements and false positive results.Objective: Problems have been arisen due to the limitations of the camera resolution, object retrieval techniques and suboptimal image processing. The aim of this study was to detect accurately breast cancer lesions in rats, which were induced by carcinogenic compounds.Material and Methods: In this experimental study, development of models was conducted based on increasing image by optimizing the ability of low-resolution infrared (IR) cameras to identify s mall objects precisely. Image pixel density increased by adjusting the distance of the objects from the camera and multiple images of objects gradually shifting were used to measure object dimensions precisely.Results: The results showed that cancerous lesions as small as 1.27 mm could be detected. This method of lesion detection had a sensitivity and specificity of 93% and 77 % respectively.Conclusion: Small objects (cancerous lesions) were measured by increasing image resolution through splitting pixels into subpixels and combining several images using Partitioned Iterated Function Systems (PIFS).

Biomarker profiling for breast cancer detection: translational research to determine acceptance of a novel breast cancer screening technique

The current study seeks to determine how the psychosocial predictors of the health belief model are related to willingness to adopt biomarker screening practices among women above and below current screening age recommendations, as biomarker profiling can potentially detect cancer much earlier than current breast cancer detection methods. Patients (N = 205) at an Obstetrician/Gynaecology office in a mid-sized Midwest city. Participants completed a survey in the waiting room before their doctor appointment. Results revealed that benefits (p < .001), barriers (p = .02), cancer worry severity (p = .01), and self-efficacy (p = .002) were significant predictors of willingness to adopt biomarker profiling, and susceptibility was marginally related (p = .09). The direct effects are qualified by two interactions between psychosocial predictors of the health belief model and participants’ age. The model predicted willingness to adopt biomarker screening well (R2 = 28%), and may be used successfully as a framework to assess the diffusion of biomarker screening acceptability.

Mammogram image visual enhancement, mass segmentation and classification

Mammography is the most effective technique for breast cancer screening and detection of abnormalities. However, early detection of breast cancer is dependent on both the radiologist's ability to read mammograms and the quality of mammogram images. In this paper, the researchers have investigated combining several image enhancement algorithms to enhance the performance of breast-region segmentation. The masses that appear in mammogram images are further analyzed and classified into four categories that include: benign, probable benign and possible malignant, probable malignant and possible benign, and malignant. The main contribution of this work is to reveal the optimal combination of various enhancement methods and to segment breast region in order to obtain better visual interpretation, analysis, and classification of mammogram masses to assist radiologists in making more accurate decisions. The experimental dataset consists of a total of more than 1300 mammogram images from both the King Hussein Cancer Center and Jordan Hospital. Results achieved tumor classification accuracy values of 90.7%. Moreover, the results showed a sensitivity of 96.2% and a specificity of 94.4% for the mass classifying algorithm. Radiologists from both institutes have acknowledged the results and confirmed that this work has lead to better visual quality images and that the segmentation and classification of tumors has aided the radiologists in making their diagnoses.

Development and assessment of an e-learning course on breast imaging for radiographers: a stratified randomized controlled trial.

BackgroundMammography is considered the best imaging technique for breast cancer screening, and the radiographer plays an important role in its performance. Therefore, continuing education is critical to improving the performance of these professionals and thus providing better health care services.ObjectiveOur goal was to develop an e-learning course on breast imaging for radiographers, assessing its efficacy, effectiveness, and user satisfaction.MethodsA stratified randomized controlled trial was performed with radiographers and radiology students who already had mammography training, using pre- and post-knowledge tests, and satisfaction questionnaires. The primary outcome was the improvement in test results (percentage of correct answers), using intention-to-treat and per-protocol analysis.ResultsA total of 54 participants were assigned to the intervention (20 students plus 34 radiographers) with 53 controls (19+34). The intervention was completed by 40 participants (11+29), with 4 (2+2) discontinued interventions, and 10 (7+3) lost to follow-up. Differences in the primary outcome were found between intervention and control: 21 versus 4 percentage points (pp), P<.001. Stratified analysis showed effect in radiographers (23 pp vs 4 pp; P=.004) but was unclear in students (18 pp vs 5 pp; P=.098). Nonetheless, differences in students’ posttest results were found (88% vs 63%; P=.003), which were absent in pretest (63% vs 63%; P=.106). The per-protocol analysis showed a higher effect (26 pp vs 2 pp; P<.001), both in students (25 pp vs 3 pp; P=.004) and radiographers (27 pp vs 2 pp; P<.001). Overall, 85% were satisfied with the course, and 88% considered it successful.ConclusionsThis e-learning course is effective, especially for radiographers, which highlights the need for continuing education.

Breast Cancer Screening

Mammography remains the primary technique for breast cancer screening. Women with dense breast tissue may benefit from digital mammography and tomosynthesis, and women at high risk may benefit from magnetic resonance imaging. However, false-positive results are problematic. The North Carolina breast density law necessitates education about screening options for women with dense breasts.