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European Journal of Radiology Jun 2022According to the World Health Organization (WHO), at the end of 2020, 7.8 million women alive were diagnosed with breast cancer in the past 5 years, making it the...
According to the World Health Organization (WHO), at the end of 2020, 7.8 million women alive were diagnosed with breast cancer in the past 5 years, making it the world's most prevalent cancer. It is largely recognized and demonstrated that early detection represents the first strategy to follow in the fight against cancer. The effectiveness of mammography screening for early breast cancer detection has been proven in several surveys and studies over the last three decades. The estimation of the Mean Glandular Dose (MGD) is important to understand the radiation-associated risk from breast x-ray imaging exams. It continues to be the subject of numerous studies and debates, since its accuracy is directly related to risk estimation and for optimizing breast cancer screening programs. This manuscript reviews the main dosimetry formalisms used to estimate the MGD in mammography and to understand the continuing efforts to reduce the absorbed dose over the last forty years. The dosimetry protocols were formulated initially for mammography. Digital breast tomosynthesis (DBT) either in conjunction with synthesized digital mammogram (SDM) or with digital mammography (DM), is routinely used in many breast cancer screening programs and consequently the dosimetry protocols were extended for these techniques.
Topics: Breast; Breast Neoplasms; Early Detection of Cancer; Female; Humans; Mammography; Mass Screening; X-Rays
PubMed: 35430441
DOI: 10.1016/j.ejrad.2022.110278 -
BioMed Research International 2022Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more...
Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more frequent in women. Although medical therapy has improved breast cancer diagnostic and treatment methods, still the death rate remains high due to failure of diagnosing breast cancer in its early stages. A classification approach for mammography images based on nonsubsampled contourlet transform (NSCT) is proposed in order to investigate it. The proposed method uses multiresolution NSCT decomposition to the region of interest (ROI) of mammography images and then uses Z-moments for extracting features from the NSCT-decomposed images. The matrix is formed by the components that are extracted from the region of interest and are then subjected to singular value decomposition (SVD) in order to remove the essential features that can generalize globally. The method employs a support vector machine (SVM) classification algorithm to categorize mammography pictures into normal, benign, and malignant and to identify and classify the breast lesions. The accuracy of the proposed model is 96.76 percent, and the training time is greatly decreased, as evident from the experiments performed. The paper also focuses on conducting the feature extraction experiments using morphological spectroscopy. The experiment combines 16 different algorithms with 4 classification methods for achieving exceptional accuracy and time efficiency outcomes as compared to other existing state-of-the-art approaches.
Topics: Algorithms; Breast; Breast Neoplasms; Female; Humans; Mammography; Support Vector Machine
PubMed: 35993044
DOI: 10.1155/2022/6392206 -
Value in Health : the Journal of the... Jul 2018Personalized breast cancer screening has so far been economically evaluated under the assumption of full screening adherence. This is the first study to evaluate the... (Comparative Study)
Comparative Study
OBJECTIVE
Personalized breast cancer screening has so far been economically evaluated under the assumption of full screening adherence. This is the first study to evaluate the effects of nonadherence on the evaluation and selection of personalized screening strategies.
METHODS
Different adherence scenarios were established on the basis of findings from the literature. A Markov microsimulation model was adapted to evaluate the effects of these adherence scenarios on three different personalized strategies.
RESULTS
First, three adherence scenarios describing the relationship between risk and adherence were identified: 1) a positive association between risk and screening adherence, 2) a negative association, or 3) a curvilinear relationship. Second, these three adherence scenarios were evaluated in three personalized strategies. Our results show that it is more the absolute adherence rate than the nature of the risk-adherence relationship that is important to determine which strategy is the most cost-effective. Furthermore, probabilistic sensitivity analyses showed that there are risk-stratified screening strategies that are more cost-effective than routine screening if the willingness-to-pay threshold for screening is below US $60,000.
CONCLUSIONS
Our results show that "nonadherence" affects the relative performance of screening strategies. Thus, it is necessary to include the true adherence level to evaluate personalized screening strategies and to select the best strategy.
Topics: Aged; Breast Neoplasms; Clinical Decision-Making; Computer Simulation; Cost-Benefit Analysis; Decision Support Techniques; Early Detection of Cancer; Female; Health Care Costs; Humans; Mammography; Markov Chains; Middle Aged; Models, Economic; Patient Compliance; Precision Medicine; Predictive Value of Tests; Prognosis; Risk Factors; Time Factors
PubMed: 30005752
DOI: 10.1016/j.jval.2017.12.022 -
Patient Education and Counseling Sep 2021The evaluation of the effect of a mammography decision aid (DA) designed for older women at risk for lower health literacy (LHL) on their knowledge of mammography's...
OBJECTIVE
The evaluation of the effect of a mammography decision aid (DA) designed for older women at risk for lower health literacy (LHL) on their knowledge of mammography's benefits and harms and decisional conflict.
METHODS
Using a pretest-posttest design, women > 75 years at risk for LHL reviewing a mammography DA before and after their [B] primary care provider visit. Women were recruited from an academic medical center and community health centers and clinics.
RESULTS
Of 147 eligible women approached, 43 participated. Receipt of the DA significantly affected knowledge of mammography's benefits and harms [B] (pre-test (M = 3.75, SD = 1.05) to post-test (M = 4.42, SD = 1.19), p = .03). Receipt of the DA did not significantly affect decisional conflict (pre-test (M = 3.10, SD = .97) to post-test (M = 3.23, SD = 1.02), p = .71, higher scores = lower decisional conflict). The majority of the women (97%) indicated that the DA was helpful.
CONCLUSIONS
Women found a mammography screening DA helpful and its use was associated with these women having increased knowledge of mammography's benefits and harms.
PRACTICE IMPLICATIONS
With the shift toward shared decision-making for women > 75 years, there is a need to engage women of all literacy levels to participate in these decisions and have tools such as the one tested in this study.
Topics: Aged; Decision Making; Decision Making, Shared; Decision Support Techniques; Early Detection of Cancer; Female; Health Literacy; Humans; Mammography
PubMed: 33637391
DOI: 10.1016/j.pec.2021.02.020 -
PloS One 2023Breast cancer claims 11,400 lives on average every year in the UK, making it one of the deadliest diseases. Mammography is the gold standard for detecting early signs of...
Breast cancer claims 11,400 lives on average every year in the UK, making it one of the deadliest diseases. Mammography is the gold standard for detecting early signs of breast cancer, which can help cure the disease during its early stages. However, incorrect mammography diagnoses are common and may harm patients through unnecessary treatments and operations (or a lack of treatment). Therefore, systems that can learn to detect breast cancer on their own could help reduce the number of incorrect interpretations and missed cases. Various deep learning techniques, which can be used to implement a system that learns how to detect instances of breast cancer in mammograms, are explored throughout this paper. Convolution Neural Networks (CNNs) are used as part of a pipeline based on deep learning techniques. A divide and conquer approach is followed to analyse the effects on performance and efficiency when utilising diverse deep learning techniques such as varying network architectures (VGG19, ResNet50, InceptionV3, DenseNet121, MobileNetV2), class weights, input sizes, image ratios, pre-processing techniques, transfer learning, dropout rates, and types of mammogram projections. This approach serves as a starting point for model development of mammography classification tasks. Practitioners can benefit from this work by using the divide and conquer results to select the most suitable deep learning techniques for their case out-of-the-box, thus reducing the need for extensive exploratory experimentation. Multiple techniques are found to provide accuracy gains relative to a general baseline (VGG19 model using uncropped 512 × 512 pixels input images with a dropout rate of 0.2 and a learning rate of 1 × 10-3) on the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) dataset. These techniques involve transfer learning pre-trained ImagetNet weights to a MobileNetV2 architecture, with pre-trained weights from a binarised version of the mini Mammography Image Analysis Society (mini-MIAS) dataset applied to the fully connected layers of the model, coupled with using weights to alleviate class imbalance, and splitting CBIS-DDSM samples between images of masses and calcifications. Using these techniques, a 5.6% gain in accuracy over the baseline model was accomplished. Other deep learning techniques from the divide and conquer approach, such as larger image sizes, do not yield increased accuracies without the use of image pre-processing techniques such as Gaussian filtering, histogram equalisation and input cropping.
Topics: Humans; Female; Deep Learning; Mammography; Breast Neoplasms; Neural Networks, Computer; Breast
PubMed: 37235566
DOI: 10.1371/journal.pone.0280841 -
Physica Medica : PM : An International... Mar 2020A tracking and reporting system was developed to monitor radiation dose in X-ray breast imaging. We used our tracking system to characterize and compare the mammographic... (Comparative Study)
Comparative Study
A tracking and reporting system was developed to monitor radiation dose in X-ray breast imaging. We used our tracking system to characterize and compare the mammographic practices of five breast imaging centers located in the United States and Brazil. Clinical data were acquired using eight mammography systems comprising three modalities: computed radiography (CR), full-field digital mammography (FFDM), and digital breast tomosynthesis (DBT). Our database consists of metadata extracted from 334,234 images. We analyzed distributions and correlations of compressed breast thickness (CBT), compression force, target-filter combinations, X-ray tube voltage, and average glandular dose (AGD). AGD reference curves were calculated based on AGD distributions as a function of CBT. These curves represent an AGD reference for a particular population and system. Differences in AGD and imaging settings were attributed to a combination of factors, such as improvements in technology, imaging protocol, and patient demographics. The tracking system allows the comparison of various imaging settings used in screening mammography, as well as the tracking of patient- and population-specific breast data collected from different populations.
Topics: Algorithms; Brazil; Breast; Breast Neoplasms; Compressive Strength; Early Detection of Cancer; Female; Humans; Image Processing, Computer-Assisted; Mammography; Phantoms, Imaging; Quality Assurance, Health Care; Radiation Dosage; Radiographic Image Enhancement; Retrospective Studies; Tomography, X-Ray Computed; United States
PubMed: 32143121
DOI: 10.1016/j.ejmp.2020.02.018 -
Health Literacy Research and Practice Apr 2021Guidelines recommend that before being offered mammography screening, women age 75 years and older be informed of the uncertainty of benefit and potential for harm...
BACKGROUND
Guidelines recommend that before being offered mammography screening, women age 75 years and older be informed of the uncertainty of benefit and potential for harm (e.g., being diagnosed with a breast cancer that would otherwise never have shown up in one's lifetime); however, few older women are informed of the risks of mammography screening and most overestimate its benefits.
OBJECTIVE
The aim of this study was to learn from women older than age 75 years who have predisposing risk factors for low health literacy (LHL) how they make decisions about mammography screening, whether an existing decision aid (DA) on mammography screening for them was acceptable and helpful, and suggestions for improving the DA.
METHODS
We conducted semi-structured interviews with 18 women who were between ages 75 and 89 years and had predisposing risk factors for LHL (i.e., answered somewhat to not at all confident to the health literacy screening question "How confident are you filling out medical forms by yourself?" and/or had an education level of some college or less).
KEY RESULTS
Findings indicate that women in this study lacked knowledge and understanding that one can decide on mammography screening based on their personal values. Women were enthusiastic about screening based on an interest in taking care of themselves but rely on their providers for health care decisions. Overall, most women found the DA helpful and would recommend the use of the DA.
CONCLUSIONS
Findings from this study provide formative data to test the efficacy of the modified DA in practice. Failing to consider the informational needs of adults with LHL in design of DAs could inadvertently exacerbate existing inequalities in health. It is essential that DAs consider older women's diverse backgrounds and educational levels to support their decision-making. Plain Language Summary: The goal of this research was to understand how women older than age 75 years with risk factors for low health literacy made decisions about getting mammograms, whether an educational pamphlet was helpful, and suggestions for improving it. This research helps in understanding how to involve this population in the process of designing patient-related materials for mammogram decision-making.
Topics: Aged; Aged, 80 and over; Decision Support Techniques; Early Detection of Cancer; Female; Health Literacy; Humans; Mammography; Risk Factors
PubMed: 34213995
DOI: 10.3928/24748307-20210308-01 -
Sensors (Basel, Switzerland) Dec 2022In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought...
In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought on by a change in the genes that regulate cell division and growth, which leads to the development of a nodule or tumour. These tumours can be either benign, which poses no health risk, or malignant, also known as cancerous, which puts patients' lives in jeopardy and has the potential to spread. The most common way to diagnose this problem is via mammograms. This kind of examination enables the detection of abnormalities in breast tissue, such as masses and microcalcifications, which are thought to be indicators of the presence of disease. This study aims to determine how histogram-based image enhancement methods affect the classification of mammograms into five groups: benign calcifications, benign masses, malignant calcifications, malignant masses, and healthy tissue, as determined by a CAD system of automatic mammography classification using convolutional neural networks. Both Contrast-limited Adaptive Histogram Equalization (CAHE) and Histogram Intensity Windowing (HIW) will be used (CLAHE). By improving the contrast between the image's background, fibrous tissue, dense tissue, and sick tissue, which includes microcalcifications and masses, the mammography histogram is modified using these procedures. In order to help neural networks, learn, the contrast has been increased to make it easier to distinguish between various types of tissue. The proportion of correctly classified images could rise with this technique. Using Deep Convolutional Neural Networks, a model was developed that allows classifying different types of lesions. The model achieved an accuracy of 62%, based on mini-MIAS data. The final goal of the project is the creation of an update algorithm that will be incorporated into the CAD system and will enhance the automatic identification and categorization of microcalcifications and masses. As a result, it would be possible to increase the possibility of early disease identification, which is important because early discovery increases the likelihood of a cure to almost 100%.
Topics: Humans; Female; Mammography; Breast Neoplasms; Breast Diseases; Neural Networks, Computer; Calcinosis
PubMed: 36616832
DOI: 10.3390/s23010235 -
Diagnostic and Interventional Radiology... 2017High social awareness of breast diseases and the rise in breast imaging facilities have led to an increase in the detection of even rare benign and malignant breast... (Review)
Review
High social awareness of breast diseases and the rise in breast imaging facilities have led to an increase in the detection of even rare benign and malignant breast lesions. Breast lesions are associated with a broad spectrum of imaging characteristics, and each radiologic imaging technique reflects different characteristics of them. We aimed to increase familiarity of the radiologist with these uncommon lesions as well as correlate histopathologic findings with the radiologic imaging features of the tumors. Histopathologic examination is necessary in the evaluation of such breast lesions, particularly when radiologic images are not definitive for a specific diagnosis.
Topics: Breast; Breast Diseases; Female; Humans; Mammography
PubMed: 28508760
DOI: 10.5152/dir.2017.16085 -
Journal of Medical Radiation Sciences Sep 2020There are increasing concerns about radiation exposure among women who undergo full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT). The main aim... (Comparative Study)
Comparative Study
INTRODUCTION
There are increasing concerns about radiation exposure among women who undergo full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT). The main aim of this study was to compare the entrance surface dose (ESD) and average glandular dose (AGD) from FFDM and DBT for different breast thicknesses.
METHODS
The ESD and AGD for FFDM in craniocaudal, mediolateral oblique and DBT in craniocaudal projection were recorded from a GE Senographe Essential FFDM unit. The accuracy of the ESD and AGD from the FFDM unit was verified during regular quality assurance programme. Patients were categorised according to their compressed breast thicknesses. X-ray tube potential and target filter combinations were varied with ESD and AGD recorded directly from the FFDM unit. The non-parametric Kruskal-Wallis, Mann-Whitney and Wilcoxon signed-rank tests were performed.
RESULTS
The median and interquartile range (IQR) age of the patients were 48 and 11 years, respectively. The highest median for ESD and median total AGD for different breast thicknesses were ranged from 3.3 to 9.1 mGy and 3.3 to 6.0 mGy, respectively, for two-view FFDM. However, it ranged from 3.1 to 8.9 mGy and 1.8 to 4.0 mGy, respectively, for single-view DBT. Both ESD and AGD were significantly lower for DBT (P < 0.001) compared with FFDM. There was a significant difference (P = 0.001) in the ESD and AGD values for different breast thicknesses in FFDM and DBT techniques.
CONCLUSIONS
The AGD for a single-view DBT was lower than the two-view FFDM technique.
Topics: Adult; Aged; Breast; Female; Humans; Mammography; Middle Aged; Radiation Dosage; Radiographic Image Enhancement
PubMed: 32495513
DOI: 10.1002/jmrs.405