-
Radiography (London, England : 1995) Jan 2024A positive experience in mammography is essential for increasing patient attendance and reattendance at these examinations, whether conducted for diagnostic or screening... (Review)
Review
INTRODUCTION
A positive experience in mammography is essential for increasing patient attendance and reattendance at these examinations, whether conducted for diagnostic or screening purposes. Mammograms indeed facilitate early disease detection, enhance the potential for cure, and consequently reduce breast cancer mortality. The main objective of this review was to identify and map the strategies aiming to improve the patient experience in diagnostic and screening mammography.
METHODS
This scoping review was performed following the JBI methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Searches were performed through databases of MEDLINE, Embase.com, CINAHL, APA PsycINFO, Cochrane Central Register of Controlled Trials, Web of Science, ProQuest Dissertation and Theses, and three clinical trial registries. This review considered studies evaluating the effect of interventions, occurring within the mammography department, on the patient experience.
RESULTS
The literature search yielded 8113 citations of which 60, matching the inclusion criteria, were included. The strategies were classified into eight categories. The most represented one was breast compression and positioning, followed by relaxation techniques and analgesic care, communication and information, screening equipment, examination procedures, patient-related factors, physical environment, and finally staff characteristics. The studied outcomes related to patient experience were mainly pain, anxiety, comfort, and satisfaction. Other types of outcomes were also considered in the studies such as image quality, technical parameters, or radiation dose. Most studies were conducted by radiographers, on female patients, and none mentioned the inclusion of male or transgender patients.
CONCLUSION
This review outlined a diversity of strategies to improve patient experience, although technique-based interventions were predominant. Further research is warranted, notably on psychological strategies, and on men and transgender people.
IMPLICATIONS FOR PRACTICE
This scoping review provides guidance to healthcare providers and services for better patient/client-centered care.
Topics: Female; Humans; Breast Neoplasms; Early Detection of Cancer; Mammography; Pain; Patient Satisfaction
PubMed: 38141428
DOI: 10.1016/j.radi.2023.11.016 -
Statistical Methods in Medical Research May 2023Screening mammography is the primary preventive strategy for early detection of breast cancer and an essential input to breast cancer risk prediction and application of...
Screening mammography is the primary preventive strategy for early detection of breast cancer and an essential input to breast cancer risk prediction and application of prevention/risk management guidelines. Identifying regions of interest within mammogram images that are associated with 5- or 10-year breast cancer risk is therefore clinically meaningful. The problem is complicated by the irregular boundary issue posed by the semi-circular domain of the breast area within mammograms. Accommodating the irregular domain is especially crucial when identifying regions of interest, as the true signal comes only from the semi-circular domain of the breast region, and noise elsewhere. We address these challenges by introducing a proportional hazards model with imaging predictors characterized by bivariate splines over triangulation. The model sparsity is enforced with the group lasso penalty function. We apply the proposed method to the motivating Joanne Knight Breast Health Cohort to illustrate important risk patterns and show that the proposed method is able to achieve higher discriminatory performance.
Topics: Humans; Female; Mammography; Breast Neoplasms; Early Detection of Cancer; Risk; Proportional Hazards Models
PubMed: 36951095
DOI: 10.1177/09622802231160551 -
The British Journal of Radiology Feb 2020In this article, we explore the evidence around the relative benefits and harms of breast cancer screening using a single radiologist to examine each female's mammograms... (Review)
Review
In this article, we explore the evidence around the relative benefits and harms of breast cancer screening using a single radiologist to examine each female's mammograms for signs of cancer (single reading), or two radiologists (double reading). First, we briefly explore the historical evidence using film-screen mammography, before providing an in-depth description of evidence using digital mammography. We classify studies according to which exact version of double reading they use, because the evidence suggests that effectiveness of double reading is contingent on whether the two radiologists are blinded to one another's decisions, and how the decisions of the two radiologists are integrated. Finally, we explore the implications for future mammography, including using artificial intelligence as the second reader, and applications to more complex three-dimensional imaging techniques such as tomosynthesis.
Topics: Aged; Aged, 80 and over; Breast Neoplasms; Early Detection of Cancer; Female; Forecasting; Humans; Mammography; Middle Aged
PubMed: 31617741
DOI: 10.1259/bjr.20190610 -
Tomography (Ann Arbor, Mich.) Jun 2023Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use... (Review)
Review
Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess risk. Applications of artificial intelligence methods (AI) such as deep learning (DL) and convolutional neural networks (CNNs) to evaluate individual patient information and imaging showed promise as personalized risk models. We reviewed the current literature for studies related to deep learning and convolutional neural networks with digital mammography for assessing breast cancer risk. We discussed the literature and examined the ongoing and future applications of deep learning techniques in breast cancer risk modeling.
Topics: Female; Humans; Breast Neoplasms; Artificial Intelligence; Deep Learning; Mammography; Breast
PubMed: 37368543
DOI: 10.3390/tomography9030091 -
The British Journal of Radiology Nov 2019Intramammary lymph nodes (IMLN) are one of the most common benign findings at screening mammography. However, abnormal IMLN features, such as diminished or absent hilum,... (Review)
Review
Intramammary lymph nodes (IMLN) are one of the most common benign findings at screening mammography. However, abnormal IMLN features, such as diminished or absent hilum, thickened cortex, not circumscribed margins, increased size or interval change, warrants additional follow-up or pathologic analysis to exclude malignancy. Some benign inflammatory conditions may be associated with imaging-detected suspected abnormal IMLN, such as reactive hyperplasia and silicone-induced lymphadenopathy. In patients with known breast cancer, IMLN are a potential site of locoregional spread, which can change the prognosis and management. In some cases, initial breast carcinomas can also mimic IMLN. Breast radiologists must also be aware of the typical and atypical characteristics of IMLN to suggest further investigation when it is necessary.
Topics: Breast Neoplasms; Carcinoma, Ductal, Breast; Diagnosis, Differential; Female; Humans; Lymphadenitis; Lymphatic Metastasis; Magnetic Resonance Imaging; Mammography; Multimodal Imaging; Prognosis
PubMed: 31322919
DOI: 10.1259/bjr.20190517 -
Contrast Media & Molecular Imaging 2022We aimed to determine the difference between contrast-enhanced spectral mammography (CESM) and contrast-enhanced magnetic resonance imaging (CE-MRI) in detecting...
OBJECTIVES
We aimed to determine the difference between contrast-enhanced spectral mammography (CESM) and contrast-enhanced magnetic resonance imaging (CE-MRI) in detecting multifocal and multicentric breast cancer (MMBC).
METHODS
: This study was conducted among breast cancer patients between July 1, 2017, and May 30, 2021. The sensitivity, specificity, and accuracy of CESM and CE-MRI in the diagnosis of MMBC were evaluated with pathological results as the gold standard.
RESULTS
A total of 188 lesions were detected in 54 patients with MMBC, including 177 breast cancer and 11 benign lesions. Based on CESM and CE-MRI, 4 false-positive cases and 3 false-negative cases and 7 false-positive cases and 1 false-negative case, respectively, were found. The accuracy of CESM was higher than that of MRI (96.3% vs 95.7%), and the specificity was higher than that of MRI (63.6% vs 36.4%). There were no significant differences in the sensitivity, specificity, and accuracy for the detection of MMBC between CESM and CE-MRI ( = 0.500; = 0.250; = 0.792).
CONCLUSION
CESM is an effective method for the detection of MMBC, which is consistent with the sensitivity and accuracy of CE-MRI.
Topics: Breast Neoplasms; Contrast Media; Early Detection of Cancer; Female; Humans; Magnetic Resonance Imaging; Mammography; Sensitivity and Specificity
PubMed: 35585943
DOI: 10.1155/2022/4224701 -
Breast Cancer Research : BCR Feb 2022Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection... (Review)
Review
BACKGROUND
Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening.
MAIN BODY
This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman's inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field.
CONCLUSIONS
We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.
Topics: Artificial Intelligence; Breast Neoplasms; Early Detection of Cancer; Female; Humans; Mammography
PubMed: 35184757
DOI: 10.1186/s13058-022-01509-z -
The British Journal of Radiology Apr 2022Contrast-enhanced spectral mammography (CESM) breast biopsy has been recently introduced into clinical practice. This short communication describes the technique and...
OBJECTIVE
Contrast-enhanced spectral mammography (CESM) breast biopsy has been recently introduced into clinical practice. This short communication describes the technique and potential as an alternative to MRI-guided biopsy.
METHODS AND MATERIALS
An additional abnormality was detected on a breast MRI examination in a patient with lobular carcinoma. The lesion was occult on conventional mammography, tomosynthesis and ultrasound and required histological diagnosis. Traditionally, this would have necessitated an MRI-guided breast biopsy, but was performed under CESM guidance.
RESULTS
A diagnostic CESM study was performed to ensure the lesion visibility with CESM and then targeted under CESM guidance. A limited diagnostic study, CESM scout and paired images for stereotactic targeting were obtained within a 10 min window following a single injection of iodinated contrast agent. The time from positioning in the biopsy device to releasing compression after biopsy and marker clip placement was 15 min. The biopsy confirmed the presence of multifocal breast cancer.
CONCLUSION
CESM-guided breast biopsy is a new technique that can be successfully used as an alternative to MRI-guided breast biopsy.
ADVANCES IN KNOWLEDGE
CESM-guided biopsy can be used to sample breast lesions which remain occult on standard mammography and ultrasound.
Topics: Breast Neoplasms; Contrast Media; Female; Humans; Image-Guided Biopsy; Magnetic Resonance Imaging; Mammography; Sensitivity and Specificity
PubMed: 35015574
DOI: 10.1259/bjr.20211287 -
JPMA. the Journal of the Pakistan... Apr 2023Since the publication of the first imaging-guided wire localisation technique, the art of breast treatments has made great strides. Radiologists like Hall, Frank,... (Review)
Review
Since the publication of the first imaging-guided wire localisation technique, the art of breast treatments has made great strides. Radiologists like Hall, Frank, Kopans, DeLuca, and Homer were all the pioneers in innovative breast interventional radiology field. Their approaches and gadgets for enhancing surgical outcomes in cases with breast diseases aided progress in the discipline and have withstood the ravages of time. Many of their methods are still in use. We are all standing together at the beginning of a new chapter in medicine. Cost effectiveness, comparative effectiveness studies, and an older population are all causing clinicians to reconsider what they perform. Similarly, we are now united on a global scale. The studies described in the current narrative review relate to multiple nations around the world. Breast cancer is a worldwide health problem. With the expansion of technological advances, as well as the apparent ease of worldwide travel, we must all collaborate to improve the outcome in the battle against breast cancer.
Topics: Humans; Female; Radiology, Interventional; Mammography; Breast; Breast Neoplasms; Medicine; Ultrasonography, Interventional
PubMed: 37052001
DOI: 10.47391/JPMA.4624 -
Journal of Clinical Oncology : Official... Jun 2023Artificial intelligence (AI) algorithms improve breast cancer detection on mammography, but their contribution to long-term risk prediction for advanced and interval...
PURPOSE
Artificial intelligence (AI) algorithms improve breast cancer detection on mammography, but their contribution to long-term risk prediction for advanced and interval cancers is unknown.
METHODS
We identified 2,412 women with invasive breast cancer and 4,995 controls matched on age, race, and date of mammogram, from two US mammography cohorts, who had two-dimensional full-field digital mammograms performed 2-5.5 years before cancer diagnosis. We assessed Breast Imaging Reporting and Data System density, an AI malignancy score (1-10), and volumetric density measures. We used conditional logistic regression to estimate odds ratios (ORs), 95% CIs, adjusted for age and BMI, and C-statistics (AUC) to describe the association of AI score with invasive cancer and its contribution to models with breast density measures. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance.
RESULTS
On mammograms between 2-5.5 years prior to cancer, a one unit increase in AI score was associated with 20% greater odds of invasive breast cancer (OR, 1.20; 95% CI, 1.17 to 1.22; AUC, 0.63; 95% CI, 0.62 to 0.64) and was similarly predictive of interval (OR, 1.20; 95% CI, 1.13 to 1.27; AUC, 0.63) and advanced cancers (OR, 1.23; 95% CI, 1.16 to 1.31; AUC, 0.64) and in dense (OR, 1.18; 95% CI, 1.15 to 1.22; AUC, 0.66) breasts. AI score improved prediction of all cancer types in models with density measures ( values < .001); discrimination improved for advanced cancer (ie, AUC for dense volume increased from 0.624 to 0.679, Δ AUC 0.065, = .01) but did not reach statistical significance for interval cancer.
CONCLUSION
AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, in particular, advanced cancer.
Topics: Female; Humans; Breast Neoplasms; Artificial Intelligence; Mammography; Breast; Breast Density; Early Detection of Cancer; Retrospective Studies
PubMed: 37104728
DOI: 10.1200/JCO.22.01153