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BMJ Open Feb 2023To systematically identify interventions that increase the use of mammography screening in women living in low-income and middle-income countries (LMICs).
OBJECTIVE
To systematically identify interventions that increase the use of mammography screening in women living in low-income and middle-income countries (LMICs).
DESIGN
Systematic review.
DATA SOURCES
MEDLINE, Embase, Global Health, CINAHL, PsycINFO, Web of Science, Cochrane Central Register of Controlled Trials, Google Scholar and African regional databases.
ELIGIBILITY CRITERIA
Studies conducted in LMICs, published between 1 January 1990 and 30 June 2021, in the English language. Studies whose population included asymptomatic women eligible for mammography screening. Studies with a reported outcome of using mammography by either self-report or medical records. No restrictions were set on the study design.
DATA EXTRACTION AND SYNTHESIS
Screening, data extraction and risk-of-bias assessment were conducted by two independent reviewers. A narrative synthesis of the included studies was conducted.
RESULTS
Five studies met the inclusion criteria consisting of two randomised controlled trials, one quasi-experiment and two cross-sectional studies. All included studies employed client-oriented intervention strategies including one-on-one education, group education, mass and small media, reducing client out-of-pocket costs, reducing structural barriers, client reminders and engagement of community health workers (CHWs). Most studies used multicomponent interventions, resulting in increases in the rate of use of mammography than those that employed a single strategy.
CONCLUSION
Mass and small media, group education, reduction of economic and structural barriers, client reminders and engagement of CHWs can increase use of mammography among women in LMICs. Promoting the adoption of these interventions should be considered, especially the multicomponent interventions, which were significantly effective relative to a single strategy in increasing use of mammography.
PROSPERO REGISTRATION NUMBER
CRD42021269556.
Topics: Female; Humans; Developing Countries; Cross-Sectional Studies; Mammography; Self Report
PubMed: 36750281
DOI: 10.1136/bmjopen-2022-066928 -
Journal of Ultrasound Jun 2023The purpose of this study was to assess the diagnostic performance of mammography (MMG) and ultrasound (US) imaging for detecting breast cancer. (Meta-Analysis)
Meta-Analysis Review
PURPOSE
The purpose of this study was to assess the diagnostic performance of mammography (MMG) and ultrasound (US) imaging for detecting breast cancer.
METHODS
Comprehensive searches of PubMed, Scopus and EMBASE from 2008 to 2021 were performed. A summary receiver operating characteristic curve (SROC) was constructed to summarize the overall test performance of MMG and US. Histopathologic analysis and/or close clinical and imaging follow-up for at least 6 months were used as golden reference.
RESULTS
Analysis of the studies revealed that the overall validity estimates of MMG and US in detecting breast cancer were as follows: pooled sensitivity per-patient were 0.82 (95% CI 0.76-0.87) and 0.83 (95% CI 0.71-0.91) respectively, The pooled specificities for detection of breast cancer using MMG, and US were 0.84 (95% CI 0.73-0.92) and 0.84 (95% CI 0.74-0.91) respectively. AUC of MMG, and US were 0.8933 and 0.8310 respectively. Pooled sensitivity and specificity per-lesion was 76% (95% CI 0.62-0.86) and 82% (95% CI 0.66-0.91) for MMG and 94% (95% CI 0.87-0.97) and 84% (95% CI 0.74-0.91) for US.
CONCLUSIONS
The meta-analysis found that, US and MMG has similar diagnostic performance in detecting breast cancer on per-patient basis after corrected threshold effect. However, on a per-lesion basis US was found to have a better diagnostic accuracy than MMG.
Topics: Female; Humans; Breast Neoplasms; Mammography; Ultrasonography, Mammary; Ultrasonography; Sensitivity and Specificity
PubMed: 36696046
DOI: 10.1007/s40477-022-00755-3 -
A systematic review of the impact of the COVID-19 pandemic on breast cancer screening and diagnosis.Breast (Edinburgh, Scotland) Feb 2023Breast cancer care has been affected by the COVID-19 pandemic. This systematic review aims to describe the observed pandemic-related changes in clinical and health... (Review)
Review
BACKGROUND
Breast cancer care has been affected by the COVID-19 pandemic. This systematic review aims to describe the observed pandemic-related changes in clinical and health services outcomes for breast screening and diagnosis.
METHODS
Seven databases (January 2020-March 2021) were searched to identify studies of breast cancer screening or diagnosis that reported observed outcomes before and related to the pandemic. Findings were presented using a descriptive and narrative approach.
RESULTS
Seventy-four studies were included in this systematic review; all compared periods before and after (or fluctuations during) the pandemic. None were assessed as being at low risk of bias. A reduction in screening volumes during the pandemic was found with over half of studies reporting reductions of ≥49%. A majority (66%) of studies reported reductions of ≥25% in the number of breast cancer diagnoses, and there was a higher proportion of symptomatic than screen-detected cancers. The distribution of cancer stage at diagnosis during the pandemic showed lower proportions of early-stage (stage 0-1/I-II, or Tis and T1) and higher proportions of relatively more advanced cases than that in the pre-pandemic period, however population rates were generally not reported.
CONCLUSIONS
Evidence of substantial reductions in screening volume and number of diagnosed breast cancers, and higher proportions of advanced stage cancer at diagnosis were found during the pandemic. However, these findings reflect short term outcomes, and higher-quality research examining the long-term impact of the pandemic is needed.
Topics: Humans; Female; Breast Neoplasms; COVID-19; Pandemics; Early Detection of Cancer; Neoplasm Staging; COVID-19 Testing
PubMed: 36646004
DOI: 10.1016/j.breast.2023.01.001 -
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 -
Breast Cancer Research : BCR Dec 2022This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to the prediction of future breast cancer. The... (Review)
Review
This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to the prediction of future breast cancer. The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021 to extract published articles in English describing the relationship of parenchymal texture features with the risk of breast cancer. Twenty-eight articles published since 2016 were included in the final review. The identification of parenchymal texture features varied from using a predefined list to machine-driven identification. A reduction in the number of features chosen for subsequent analysis in relation to cancer incidence then varied across statistical approaches and machine learning methods. The variation in approach and number of features identified for inclusion in analysis precluded generating a quantitative summary or meta-analysis of the value of these features to improve predicting risk of future breast cancers. This updated overview of the state of the art revealed research gaps; based on these, we provide recommendations for future studies using parenchymal features for mammogram images to make use of accumulating image data, and external validation of prediction models that extend to 5 and 10 years to guide clinical risk management. Following these recommendations could enhance the applicability of models, helping improve risk classification and risk prediction for women to tailor screening and prevention strategies to the level of risk.
Topics: Female; Humans; Breast Density; Breast Neoplasms; Mammography; Risk Assessment
PubMed: 36585732
DOI: 10.1186/s13058-022-01600-5 -
Frontiers in Oncology 2022This study aimed to perform a meta-analysis to evaluate the diagnostic performance of radiomics in predicting axillary lymph node metastasis (ALNM) and sentinel lymph...
BACKGROUND
This study aimed to perform a meta-analysis to evaluate the diagnostic performance of radiomics in predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis (SLNM) in breast cancer.
MATERIALS AND METHODS
Multiple electronic databases were systematically searched to identify relevant studies published before April 29, 2022: PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The overall diagnostic odds ratio (DOR), sensitivity, specificity, and area under the curve (AUC) were calculated to evaluate the diagnostic performance of radiomic features for lymph node metastasis (LNM) in patients with breast cancer. Spearman's correlation coefficient was determined to assess the threshold effect, and meta-regression and subgroup analyses were performed to explore the possible causes of heterogeneity.
RESULTS
A total of 30 studies with 5611 patients were included in the meta-analysis. Pooled estimates suggesting overall diagnostic accuracy of radiomics in detecting LNM were determined: DOR, 23 (95% CI, 16-33); sensitivity, 0.86 (95% CI, 0.82-0.88); specificity, 0.79 (95% CI, 0.73-0.84); and AUC, 0.90 (95% CI, 0.87-0.92). The meta-analysis showed significant heterogeneity between sensitivity and specificity across the included studies, with no evidence for a threshold effect. Meta-regression and subgroup analyses showed that combined clinical factors, modeling method, region, and imaging modality (magnetic resonance imaging [MRI], ultrasound, computed tomography [CT], and X-ray mammography [MMG]) contributed to the heterogeneity in the sensitivity analysis ( < 0.05). Furthermore, modeling methods, MRI, and MMG contributed to the heterogeneity in the specificity analysis ( < 0.05).
CONCLUSION
Our results show that radiomics has good diagnostic performance in predicting ALNM and SLNM in breast cancer. Thus, we propose this approach as a clinical method for the preoperative identification of LNM.
PubMed: 36518318
DOI: 10.3389/fonc.2022.1046005 -
JAMA Network Open Dec 2022A discrepancy on current guidelines and clinical practice exists regarding routine imaging surveillance after mastectomy, mainly regarding the lack of adequate evidence... (Meta-Analysis)
Meta-Analysis
IMPORTANCE
A discrepancy on current guidelines and clinical practice exists regarding routine imaging surveillance after mastectomy, mainly regarding the lack of adequate evidence for imaging in this setting.
OBJECTIVE
To investigate the usefulness of imaging surveillance in terms of cancer detection and interval cancer rates after mastectomy with or without reconstruction for patients with prior breast cancer.
DATA SOURCES
A comprehensive literature search was conducted in 3 electronic databases-PubMed, ISI Web of Science, and Scopus-without year restriction. References from relevant reviews and eligible studies were also manually searched.
STUDY SELECTION
Eligible studies were defined as those conducting surveillance imaging (mammography, ultrasonography, or magnetic resonance imaging [MRI]) of patients with prior breast cancer after mastectomy with or without reconstruction that presented adequate data to calculate cancer detection rates for each surveillance method.
DATA EXTRACTION AND SYNTHESIS
Independent data extraction by 2 investigators with consensus on discrepant results was performed. A quality assessment of studies was performed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) template. The generalized linear mixed model framework with both fixed-effects and random-effects models was used to meta-analyze the proportion of cases across studies including 3 variables: surveillance method, reconstruction after mastectomy, and surveillance measure.
MAIN OUTCOMES AND MEASURES
Three outcome measures were calculated for each eligible study and each surveillance imaging method within studies: overall cancer detection (defined as ipsilateral cancer, both palpable and nonpalpable) rate per 1000 examinations, clinically occult (nonpalpable) cancer detection rate per 1000 examinations, and interval cancer rate per 1000 examinations.
RESULTS
In total, 16 studies were eligible for the meta-analysis. The pooled overall cancer detection rates per 1000 examinations were 1.86 (95% CI, 1.05-3.30) for mammography, 2.66 (95% CI, 1.48-4.76) for ultrasonography, and 5.17 (95% CI, 1.49-17.75) for MRI. For mastectomy without reconstruction, the rate of clinically occult (nonpalpable) cancer per 1000 examinations (2.96; 95% CI, 1.38-6.32) and the interval cancer rate per 1000 examinations (3.73; 95% CI, 0.84-3.98) were lower than the overall cancer detection rate (including both palpable and nonpalpable lesions) per 1000 examinations (6.41; 95% CI, 3.09-13.25) across all imaging modalities. The interval cancer rate per 1000 examinations for mastectomy with reconstruction (3.73; 95% CI, 0.41-2.73) was comparable to the pooled cancer detection rate per 1000 examinations (4.73; 95% CI, 2.32-9.63) across all imaging modalities. In all clinical scenarios and imaging modalities, lower rates of clinically occult cancer compared with cancer detection rates were observed.
CONCLUSIONS AND RELEVANCE
Lower detection rates of clinically occult-compared with overall-cancer across all 3 imaging modalities challenge the use of imaging surveillance after mastectomy, with or without reconstruction. Findings suggest that imaging surveillance in this context is unnecessary in clinical practice, at least until further studies demonstrate otherwise. Future studies should consider using the clinically occult cancer detection rate as a more clinically relevant measure in this setting.
Topics: Humans; Female; Mastectomy; Breast Neoplasms; Mammography; Physical Examination; Consensus
PubMed: 36454573
DOI: 10.1001/jamanetworkopen.2022.44212 -
Cancers Oct 2022Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control... (Review)
Review
Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in an early stage of cancer, from which all survived. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false-detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting the diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually, which leads to an increase in the risk of wrong decisions for cancer detection. Thus, the utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and the availability of datasets obtained from various imaging modalities have opened an opportunity to surpass the limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast-cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.
PubMed: 36358753
DOI: 10.3390/cancers14215334 -
Cancers Sep 2022Breast cancer is the most common solid tumor and the second highest cause of death in the United States. Detection and diagnosis of breast tumors includes various... (Review)
Review
PURPOSE
Breast cancer is the most common solid tumor and the second highest cause of death in the United States. Detection and diagnosis of breast tumors includes various imaging modalities, such as mammography (MMG), ultrasound (US), and contrast-enhancement MRI. Breast-specific gamma imaging (BSGI) is an emerging tool, whereas morphological imaging has the disadvantage of a higher absorbed dose. Our aim was to assess if this imaging method is a more valuable choice in detecting breast malignant lesions compared to morphological counterparts.
METHODS
research on Medline from 1995 to June 2022 was conducted. Studies that compared at least one anatomical imaging modality with BSGI were screened and assessed through QUADAS2 for risk of bias and applicability concerns assessment. Sensitivity, specificity, positive and negative predictive value (PPV and NPV) were reported.
RESULTS
A total of 15 studies compared BSGI with MMG, US, and MRI. BSGI sensitivity was similar to MRI, but specificity was higher. Specificity was always higher than MMG and US. BSGI had higher PPV and NPV. When used for the evaluation of a suspected breast lesion, the overall sensitivity was better than the examined overall sensitivity when BSGI was excluded. Risk of bias and applicability concerns domain showed mainly low risk of bias.
CONCLUSION
BSGI is a valuable imaging modality with similar sensitivity to MRI but higher specificity, although at the cost of higher radiation burden.
PubMed: 36230540
DOI: 10.3390/cancers14194619 -
Breast (Edinburgh, Scotland) Dec 2022Mammographic density is a well-defined risk factor for breast cancer and having extremely dense breast tissue is associated with a one-to six-fold increased risk of... (Meta-Analysis)
Meta-Analysis Review
OBJECTIVES
Mammographic density is a well-defined risk factor for breast cancer and having extremely dense breast tissue is associated with a one-to six-fold increased risk of breast cancer. However, it is questioned whether this increased risk estimate is applicable to current breast density classification methods. Therefore, the aim of this study was to further investigate and clarify the association between mammographic density and breast cancer risk based on current literature.
METHODS
Medline, Embase and Web of Science were systematically searched for articles published since 2013, that used BI-RADS lexicon 5th edition and incorporated data on digital mammography. Crude and maximally confounder-adjusted data were pooled in odds ratios (ORs) using random-effects models. Heterogeneity regarding breast cancer risks were investigated using I statistic, stratified and sensitivity analyses.
RESULTS
Nine observational studies were included. Having extremely dense breast tissue (BI-RADS density D) resulted in a 2.11-fold (95% CI 1.84-2.42) increased breast cancer risk compared to having scattered dense breast tissue (BI-RADS density B). Sensitivity analysis showed that when only using data that had adjusted for age and BMI, the breast cancer risk was 1.83-fold (95% CI 1.52-2.21) increased. Both results were statistically significant and homogenous.
CONCLUSIONS
Mammographic breast density BI-RADS D is associated with an approximately two-fold increased risk of breast cancer compared to having BI-RADS density B in general population women. This is a novel and lower risk estimate compared to previously reported and might be explained due to the use of digital mammography and BI-RADS lexicon 5th edition.
Topics: Female; Humans; Breast Density; Breast Neoplasms; Mammography; Breast; Risk Factors
PubMed: 36183671
DOI: 10.1016/j.breast.2022.09.007