-
Medical Journal of the Islamic Republic... 2023Breast cancer is a non-communicable and common disease that accounts for a high percentage of deaths. Early diagnosis of this disease reduces the death rate. Screening... (Review)
Review
BACKGROUND
Breast cancer is a non-communicable and common disease that accounts for a high percentage of deaths. Early diagnosis of this disease reduces the death rate. Screening methods such as digital mammography can help prevent or identify the disease earlier. Therefore, this study aims to analyze the cost-benefit of breast cancer using digital mammography.
METHODS
This systematic review was conducted based on PRISMA 2020 checklist. PubMed, Scopus, Web of Science, ProQuest, Cochrane Library, and Google Scholar were searched without any time limitation on June 2022. The quality of the studies was evaluated with the CHEERS checklist. After data extraction, the results were synthesized by thematic content analysis.
RESULTS
During the search, 3468 records were identified, of which 1061 were duplicates. 2407 titles and abstracts screened in terms of inclusion criteria. Finally, after studying 20 fulltexts, three of them were included in the study. The quality of these articles was scored between 10 and 16. These studies were from Spain, Denmark, and the United States from 2000 to 2019. Two studies showed that digital mammography is not as effective as other screening methods.
CONCLUSION
The results of this study showed that digital mammography is not very cost-benefit for the health care system. An increase in its repetition frequency imposes more costs on the health system and doesn't have more benefits for it.
PubMed: 37750094
DOI: 10.47176/mjiri.37.89 -
Breast (Edinburgh, Scotland) Oct 2021We conducted a systematic review and meta-analysis to compare the screening performance of synthesized mammography (SM) plus digital breast tomosynthesis (DBT) with... (Meta-Analysis)
Meta-Analysis Review
PURPOSE
We conducted a systematic review and meta-analysis to compare the screening performance of synthesized mammography (SM) plus digital breast tomosynthesis (DBT) with digital mammography (DM) plus DBT or DM alone.
METHODS
Medline, Embase, Web of Science, and the Cochrane Library databases were searched from January 2010 to January 2021. Eligible population-based studies on breast cancer screening comparing SM/DBT with DM/DBT or DM in asymptomatic women were included. A random-effect model was used in this meta-analysis. Data were summarized as risk differences (RDs), with 95 % confidence intervals (CIs).
RESULTS
Thirteen studies involving 1,370,670 participants were included. Compared with DM/DBT, screening using SM/DBT had similar breast cancer detection rate (CDR) (RD = -0.1/1000 screens, 95 % CI = -0.4 to 0.2, p = 0.557, I = 0 %), but lower recall rate (RD = -0.56 %, 95 % CI = -1.03 to -0.08, p = 0.022, I = 90 %) and lower biopsy rate (RD = -0.33 %, 95 % CI = -0.56 to -0.10, p = 0.005, I = 78 %). Compared with DM, SM/DBT improved CDR (RD = 2.0/1000 screens, 95 % CI = 1.4 to 2.6, p < 0.001, I = 63 %) and reduced recall rate (RD = -0.95 %, 95 % CI = -1.91 to -0.002, p = 0.049, I = 99 %). However, SM/DBT and DM had similar interval cancer rate (ICR) (RD = 0.1/1000 screens, 95 % CI = -0.6 to 0.8, p = 0.836, I = 71 %) and biopsy rate (RD = -0.05 %, 95 % CI = -0.35 to 0.24, p = 0.727, I = 93 %).
CONCLUSIONS
Screening using SM/DBT has similar breast cancer detection but reduces recall and biopsy when compared with DM/DBT. SM/DBT improves CDR when compared with DM, but they have little difference in ICR. SM/DBT could replace DM/DBT in breast cancer screening to reduce radiation dose.
Topics: Biopsy; Breast; Breast Neoplasms; Early Detection of Cancer; Female; Humans; Mammography; Mass Screening
PubMed: 34329948
DOI: 10.1016/j.breast.2021.07.016 -
International Journal of Environmental... Aug 2022It is well established that access to preventative care, such as breast or cervical cancer screening, can reduce morbidity and mortality. Certain groups may be missed... (Meta-Analysis)
Meta-Analysis Review
It is well established that access to preventative care, such as breast or cervical cancer screening, can reduce morbidity and mortality. Certain groups may be missed out of these healthcare services, such as women with disabilities, as they face many access barriers due to underlying inequalities and negative attitudes. However, the data have not been reviewed on whether women with disabilities face inequalities in the uptake of these services. A systematic review and meta-analysis were conducted to compare the uptake of breast and cervical cancer screening in women with and without disabilities. A search was conducted in July 2021 across four databases: PubMed, MEDLINE, Global Health, and CINAHL. Quantitative studies comparing the uptake of breast or cervical cancer screening between women with and without disabilities were eligible. Twenty-nine studies were included, all from high-income settings. One third of the 29 studies (34.5%, 10) were deemed to have a high risk of bias, and the remainder a low risk of bias. The pooled estimates showed that women with disabilities have 0.78 (95% CI: 0.72-0.84) lower odds of attending breast cancer screening and have 0.63 (95% CI: 0.45-0.88) lower odds of attending cervical cancer screening, compared to women without disabilities. In conclusion, women with disabilities face disparities in receipt of preventative cancer care. There is consequently an urgent need to evaluate and improve the inclusivity of cancer screening programs and thereby prevent avoidable morbidity and mortality.
Topics: Breast Neoplasms; Disabled Persons; Early Detection of Cancer; Female; Humans; Mammography; Mass Screening; Uterine Cervical Neoplasms
PubMed: 35954824
DOI: 10.3390/ijerph19159465 -
Insights Into Imaging Dec 2023Calcifications on mammography can be indicative of breast cancer, but the prognostic value of their appearance remains unclear. This systematic review and meta-analysis... (Review)
Review
BACKGROUND
Calcifications on mammography can be indicative of breast cancer, but the prognostic value of their appearance remains unclear. This systematic review and meta-analysis aimed to evaluate the association between mammographic calcification morphology descriptors (CMDs) and clinicopathological factors.
METHODS
A comprehensive literature search in Medline via Ovid, Embase.com, and Web of Science was conducted for articles published between 2000 and January 2022 that assessed the relationship between CMDs and clinicopathological factors, excluding case reports and review articles. The risk of bias and overall quality of evidence were evaluated using the QUIPS tool and GRADE. A random-effects model was used to synthesize the extracted data. This systematic review is reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA).
RESULTS
Among the 4715 articles reviewed, 29 met the inclusion criteria, reporting on 17 different clinicopathological factors in relation to CMDs. Heterogeneity between studies was present and the overall risk of bias was high, primarily due to small, inadequately described study populations. Meta-analysis demonstrated significant associations between fine linear calcifications and high-grade DCIS [pooled odds ratio (pOR), 4.92; 95% confidence interval (CI), 2.64-9.17], (comedo)necrosis (pOR, 3.46; 95% CI, 1.29-9.30), (micro)invasion (pOR, 1.53; 95% CI, 1.03-2.27), and a negative association with estrogen receptor positivity (pOR, 0.33; 95% CI, 0.12-0.89).
CONCLUSIONS
CMDs detected on mammography have prognostic value, but there is a high level of bias and variability between current studies. In order for CMDs to achieve clinical utility, standardization in reporting of CMDs is necessary.
CRITICAL RELEVANCE STATEMENT
Mammographic calcification morphology descriptors (CMDs) have prognostic value, but in order for CMDs to achieve clinical utility, standardization in reporting of CMDs is necessary.
SYSTEMATIC REVIEW REGISTRATION
CRD42022341599 KEY POINTS: • Mammographic calcifications can be indicative of breast cancer. • The prognostic value of mammographic calcifications is still unclear. • Specific mammographic calcification morphologies are related to lesion aggressiveness. • Variability between studies necessitates standardization in calcification evaluation to achieve clinical utility.
PubMed: 38051355
DOI: 10.1186/s13244-023-01529-z -
BMC Bioinformatics Oct 2023Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the...
BACKGROUND
Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images.
OBJECTIVE AND METHODS
This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers.
RESULTS
A total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images.
CONCLUSION
Using this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT.
Topics: Humans; Female; Deep Learning; Radiographic Image Enhancement; Breast; Breast Neoplasms; Mammography
PubMed: 37884877
DOI: 10.1186/s12859-023-05515-6 -
The Indian Journal of Radiology &... Jul 2024Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely... (Review)
Review
Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely variable. To evaluate published literature on breast cancer detection in mammography for reproducibility and to ascertain best practices for model design. The PubMed and Scopus databases were searched to identify records that described the use of deep learning to detect lesions or classify images into cancer or noncancer. A modification of Quality Assessment of Diagnostic Accuracy Studies (mQUADAS-2) tool was developed for this review and was applied to the included studies. Results of reported studies (area under curve [AUC] of receiver operator curve [ROC] curve, sensitivity, specificity) were recorded. A total of 12,123 records were screened, of which 107 fit the inclusion criteria. Training and test datasets, key idea behind model architecture, and results were recorded for these studies. Based on mQUADAS-2 assessment, 103 studies had high risk of bias due to nonrepresentative patient selection. Four studies were of adequate quality, of which three trained their own model, and one used a commercial network. Ensemble models were used in two of these. Common strategies used for model training included patch classifiers, image classification networks (ResNet in 67%), and object detection networks (RetinaNet in 67%). The highest reported AUC was 0.927 ± 0.008 on a screening dataset, while it reached 0.945 (0.919-0.968) on an enriched subset. Higher values of AUC (0.955) and specificity (98.5%) were reached when combined radiologist and Artificial Intelligence readings were used than either of them alone. None of the studies provided explainability beyond localization accuracy. None of the studies have studied interaction between AI and radiologist in a real world setting. While deep learning holds much promise in mammography interpretation, evaluation in a reproducible clinical setting and explainable networks are the need of the hour.
PubMed: 38912238
DOI: 10.1055/s-0043-1775737 -
Radiology Jan 2022Background Advances in computer processing and improvements in data availability have led to the development of machine learning (ML) techniques for mammographic... (Meta-Analysis)
Meta-Analysis
Background Advances in computer processing and improvements in data availability have led to the development of machine learning (ML) techniques for mammographic imaging. Purpose To evaluate the reported performance of stand-alone ML applications for screening mammography workflow. Materials and Methods Ovid Embase, Ovid Medline, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science literature databases were searched for relevant studies published from January 2012 to September 2020. The study was registered with the PROSPERO International Prospective Register of Systematic Reviews (protocol no. CRD42019156016). Stand-alone technology was defined as a ML algorithm that can be used independently of a human reader. Studies were quality assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and the Prediction Model Risk of Bias Assessment Tool, and reporting was evaluated using the Checklist for Artificial Intelligence in Medical Imaging. A primary meta-analysis included the top-performing algorithm and corresponding reader performance from which pooled summary estimates for the area under the receiver operating characteristic curve (AUC) were calculated using a bivariate model. Results Fourteen articles were included, which detailed 15 studies for stand-alone detection ( = 8) and triage ( = 7). Triage studies reported that 17%-91% of normal mammograms identified could be read by adapted screening, while "missing" an estimated 0%-7% of cancers. In total, an estimated 185 252 cases from three countries with more than 39 readers were included in the primary meta-analysis. The pooled sensitivity, specificity, and AUC was 75.4% (95% CI: 65.6, 83.2; = .11), 90.6% (95% CI: 82.9, 95.0; = .40), and 0.89 (95% CI: 0.84, 0.98), respectively, for algorithms, and 73.0% (95% CI: 60.7, 82.6), 88.6% (95% CI: 72.4, 95.8), and 0.85 (95% CI: 0.78, 0.97), respectively, for readers. Conclusion Machine learning (ML) algorithms that demonstrate a stand-alone application in mammographic screening workflows achieve or even exceed human reader detection performance and improve efficiency. However, this evidence is from a small number of retrospective studies. Therefore, further rigorous independent external prospective testing of ML algorithms to assess performance at preassigned thresholds is required to support these claims. ©RSNA, 2021 See also the editorial by Whitman and Moseley in this issue.
Topics: Breast Neoplasms; Female; Humans; Machine Learning; Mammography; Radiographic Image Interpretation, Computer-Assisted; Sensitivity and Specificity; Workflow
PubMed: 34665034
DOI: 10.1148/radiol.2021210391 -
Journal of Cancer 2023To provide a systematic review and meta-analysis that evaluates the diagnostic accuracy of contrast-enhanced mammography (CEM) compared to standard contrast-enhanced... (Review)
Review
To provide a systematic review and meta-analysis that evaluates the diagnostic accuracy of contrast-enhanced mammography (CEM) compared to standard contrast-enhanced breast magnetic resonance imaging (breast MRI). Like breast MRI, CEM enables tumour visualization by contrast accumulation. CEM seems to be a viable substitute for breast MRI. This systematic search assessed the diagnostic accuracy of these techniques in women with suspicious breast lesions on prior imaging or physical examination, who have undergone both breast MRI and CEM. CEM had to be performed on a commercially available system. The MRI sequence parameters had to be described sufficiently to ensure that standard breast MRI sequence protocols were used. Pooled values of sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio (DOR), were estimated using bivariate mixed-effects logistic regression modeling. Hierarchical summary receiver operating characteristic curves for CEM and breast MRI were also constructed. Six studies (607 patients with 775 lesions) met the predefined inclusion criteria. Pooled sensitivity was 96% for CEM and 97% for breast MRI. Pooled specificity was 77% for both modalities. DOR was 79.5 for CEM and 122.9 for breast MRI. Between-study heterogeneity expressed as the -index was substantial with values over 80%. Pooled sensitivity was high for both CEM and breast MRI, with moderate specificity. The pooled DOR estimates, however, indicate higher overall diagnostic performance of breast MRI compared to CEM. Nonetheless, current scientific evidence is too limited to prematurely discard CEM as an alternative for breast MRI.
PubMed: 36605487
DOI: 10.7150/jca.79747 -
Radiography (London, England : 1995) Aug 2023Mammography screening programs have been implemented in European countries as prevention tools aimed at reducing breast cancer mortality through early detection in... (Review)
Review
INTRODUCTION
Mammography screening programs have been implemented in European countries as prevention tools aimed at reducing breast cancer mortality through early detection in asymptomatic women. Nordic countries (Denmark, Finland, Iceland, Norway, Sweden, the Faroe Islands, and Greenland) demonstrated high participation rates; however, breast cancer mortality could be limited by further optimizing screening. This review aimed to explore factors that affect women's participation in mammography screening in Nordic countries.
METHOD
A systematic review of segregated mixed research synthesis using a deductive approach was conducted. The following databases and platforms were searched to identify relevant studies: CINAHL with Full Text (EBSCOHost), MEDLINE (EBSCOHost), PsycInfo (ProQuest), Scopus (Elsevier) and Web of Science Core Collection (SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, and ESCI). The Critical Appraisal Skills Program was used for quality assessment. The Health Promotion Model was applied to integrate findings from qualitative and qualitative research. All methodological steps followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
RESULTS
The final selection (16 articles) included studies from three Nordic countries: Denmark (four quantitative studies), Norway (one qualitative and four quantitative studies), and Sweden (three qualitative and seven quantitative studies). Sixty-three factors were identified as barriers, facilitators, or factors with no influence.
CONCLUSION
A substantial number of obtained factors, spread across a wide spectrum, describe (non-)participation in mammography screening as a versatile phenomenon.
IMPLICATIONS FOR PRACTICE
The findings of this review could benefit the mammography staff and providers regarding possible interventions aimed at improving screening participation rates.
Topics: Female; Humans; Mammography; Breast Neoplasms; Scandinavian and Nordic Countries; Qualitative Research; Norway
PubMed: 37421878
DOI: 10.1016/j.radi.2023.06.010 -
Journal of Personalized Medicine Mar 2023The current systematic review and meta-analysis was conducted to estimate the incidence of overdiagnosis due to screening mammography for breast cancer among women aged... (Review)
Review
The current systematic review and meta-analysis was conducted to estimate the incidence of overdiagnosis due to screening mammography for breast cancer among women aged 40 years and older. A PRISMA systematic search appraisal and meta-analysis were conducted. A systematic literature search of English publications in PubMed, Web of Science, EMBASE, Scopus, and Google Scholar was conducted without regard to the region or time period. Generic, methodological, and statistical data were extracted from the eligible studies. A meta-analysis was completed by utilizing comprehensive meta-analysis software. The effect size estimates were calculated using the fail-safe N test. The funnel plot and the Begg and Mazumdar rank correlation tests were employed to find any potential bias among the included articles. The strength of the association between two variables was assessed using Kendall's tau. Heterogeneity was measured using the I-squared (I2) test. The literature search in the five databases yielded a total of 4214 studies. Of those, 30 articles were included in the final analysis, with sample sizes ranging from 451 to 1,429,890 women. The vast majority of the articles were retrospective cohort designs (24 articles). The age of the recruited women ranged between 40 and 89 years old. The incidence of overdiagnosis due to screening mammography for breast cancer among women aged 40 years and older was 12.6%. There was high heterogeneity among the study articles (I2 = 99.993), and the pooled event rate was 0.126 (95% CI: 15 0.101-0.156). Despite the random-effects meta-analysis showing a high degree of heterogeneity among the articles, the screening tests have to allow for a certain degree of overdiagnosis (12.6%) due to screening mammography for breast cancer among women aged 40 years and older. Furthermore, efforts should be directed toward controlling and minimizing the harmful consequences associated with breast cancer screening.
PubMed: 36983705
DOI: 10.3390/jpm13030523