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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 -
Breast (Edinburgh, Scotland) Aug 2022Breast cancer screening guidelines could provide valuable tools for clinical decision making by reviewing the available evidence and providing recommendations. Little... (Review)
Review
OBJECTIVES
Breast cancer screening guidelines could provide valuable tools for clinical decision making by reviewing the available evidence and providing recommendations. Little information is known about how many countries have issued breast cancer screening guidelines and the differences among existing guidelines. We systematically reviewed current guidelines and summarized corresponding recommendations, to provide references for good clinical practice in different countries.
METHODS
Systematic searches of MEDLINE, EMBASE, Web of Science, and Scopus from inception to March 27th, 2021 were conducted and supplemented by reviewing the guideline development organizations. The quality of screening guidelines was assessed from six domains of the Appraisal of Guidelines for Research and Evaluation Ⅱ (AGREE Ⅱ) instrument by two appraisers. The basic information and recommendations of the issued guidelines were extracted and summarized.
RESULTS
A total of 23 guidelines issued between 2010 and 2021 in 11 countries or regions were identified for further review. The content and quality varied across the guidelines. The average AGREE Ⅱ scores of the guidelines ranged from 33.3% to 87.5%. The highest domain score was "clarity of presentation" while the domain with the lowest score was "applicability". For average-risk women, most of the guidelines recommended mammographic screening for those aged 40-74 years, specifically, those aged 50-69 years were regarded as the optimal age group for screening. Nine of 23 guidelines recommended against an upper age limit for breast cancer screening. Mammography (MAM) was recommended as the primary screening modality for average-risk women by all included guidelines. Most guidelines suggested annual or biennial mammographic screening. Risk factors of breast cancer identified in the guidelines mainly fell within five categories which could be broadly summarized as the personal history of pre-cancerous lesions and/or breast cancer; the family history of breast cancer; the known genetic predisposition of breast cancer; the history of mantle or chest radiation therapy; and dense breasts. For women at higher risk, there was a consensus among most guidelines that annual MAM or annual magnetic resonance imaging (MRI) should be given, and the screening should begin earlier than the average-risk group.
CONCLUSIONS
The majority of 23 included international guidelines were issued by developed countries which contained roughly the same but not identical recommendations on breast cancer screening age, methods, and intervals. Most guidelines recommended annual or biennial mammographic screening between 40 and 74 years for average-risk populations and annual MAM or annual MRI starting from a younger age for high-risk populations. Current guidelines varied in quality and increased efforts are needed to improve the methodological quality of guidance documents. Due to lacking clinical practice guidelines tailored to different economic levels, low- and middle-income countries (LMICs) should apply and implement the evidence-based guidelines with higher AGREE Ⅱ scores considering local adaption.
Topics: Breast; Breast Neoplasms; Early Detection of Cancer; Female; Humans; Mammography; Mass Screening
PubMed: 35636342
DOI: 10.1016/j.breast.2022.04.003 -
Radiology Jun 2023Background There is considerable interest in the potential use of artificial intelligence (AI) systems in mammographic screening. However, it is essential to critically... (Meta-Analysis)
Meta-Analysis
Background There is considerable interest in the potential use of artificial intelligence (AI) systems in mammographic screening. However, it is essential to critically evaluate the performance of AI before it can become a modality used for independent mammographic interpretation. Purpose To evaluate the reported standalone performances of AI for interpretation of digital mammography and digital breast tomosynthesis (DBT). Materials and Methods A systematic search was conducted in PubMed, Google Scholar, Embase (Ovid), and Web of Science databases for studies published from January 2017 to June 2022. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) values were reviewed. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Comparative (QUADAS-2 and QUADAS-C, respectively). A random effects meta-analysis and meta-regression analysis were performed for overall studies and for different study types (reader studies vs historic cohort studies) and imaging techniques (digital mammography vs DBT). Results In total, 16 studies that include 1 108 328 examinations in 497 091 women were analyzed (six reader studies, seven historic cohort studies on digital mammography, and four studies on DBT). Pooled AUCs were significantly higher for standalone AI than radiologists in the six reader studies on digital mammography (0.87 vs 0.81, = .002), but not for historic cohort studies (0.89 vs 0.96, = .152). Four studies on DBT showed significantly higher AUCs in AI compared with radiologists (0.90 vs 0.79, < .001). Higher sensitivity and lower specificity were seen for standalone AI compared with radiologists. Conclusion Standalone AI for screening digital mammography performed as well as or better than radiologists. Compared with digital mammography, there is an insufficient number of studies to assess the performance of AI systems in the interpretation of DBT screening examinations. © RSNA, 2023 See also the editorial by Scaranelo in this issue.
Topics: Female; Humans; Artificial Intelligence; Breast Neoplasms; Early Detection of Cancer; Mammography; Breast; Retrospective Studies
PubMed: 37219445
DOI: 10.1148/radiol.222639 -
Journal of Medical Internet Research Jul 2019Machine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations....
BACKGROUND
Machine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. More and more automated methods are emerging with deep feature learning and representations. Recent advancements of ML with deeper and extensive representation approaches, commonly known as deep learning (DL) approaches, have made a very significant impact on improving the diagnostics capabilities of the CAD systems.
OBJECTIVE
This review aimed to survey both traditional ML and DL literature with particular application for breast cancer diagnosis. The review also provided a brief insight into some well-known DL networks.
METHODS
In this paper, we present an overview of ML and DL techniques with particular application for breast cancer. Specifically, we search the PubMed, Google Scholar, MEDLINE, ScienceDirect, Springer, and Web of Science databases and retrieve the studies in DL for the past 5 years that have used multiview mammogram datasets.
RESULTS
The analysis of traditional ML reveals the limited usage of the methods, whereas the DL methods have great potential for implementation in clinical analysis and improve the diagnostic capability of existing CAD systems.
CONCLUSIONS
From the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.
Topics: Breast Neoplasms; Deep Learning; Female; Humans; Machine Learning; Mammography
PubMed: 31350843
DOI: 10.2196/14464 -
La Clinica Terapeutica 2020The aim of this systematic review was to summarize the scientific literature concerning the use of the Precede-Proceed model (PPM) applied to educational programs and...
OBJETCTIVE
The aim of this systematic review was to summarize the scientific literature concerning the use of the Precede-Proceed model (PPM) applied to educational programs and health screenings contextsV.
STUDY DESIGN
Systematic review.
METHODS
The search process was based on a selection of publications listed in Medline and Scopus. The keywords used were "Precede-Proceed" AND ("screening" OR "educational programs"). Studies included in the systematic review were subdivided into those applying the model in a screening context, and those applying it within educational programs.
RESULTS
Twenty-seven studies were retrieved, mostly performed in the USA and, generally, the promoting center was the University. In the context of cancer screening, the PPM model was most of all applied to Mammography Screening (5 of 13 studies in cancer screening), and Cervical Cancer Screening (5 of 13). Another three studies within the cancer field investigated Menopause-Inducing Cancer Treatments, Oral cancer prevention, and cancer screening in general. In the remaining studies, the model was applied in various screening areas, particularly chronic and degenerative diseases. There were many different study designs, most of which cross-sectional (8), though several RTCs (8) and focus groups (5) were also found. For the cross-sectional studies the methodological quality varied between 3/10 and 9/10, whilst for the RCTs it ranged from 2/5 to 3/5.
CONCLUSIONS
The PPM provides an excellent framework for health intervention programs especially in screening contexts, and could improve the understanding of the relationship between variables such as knowledge and screening. Given the complexity of a behavioral change process, certain important predisposing factors could be measured in future studies, and during health intervention planning.
Topics: Biobehavioral Sciences; Cross-Sectional Studies; Early Detection of Cancer; Humans; Mass Screening; Neoplasms; Public Health
PubMed: 32141490
DOI: 10.7417/CT.2020.2208 -
Journal of Ambient Intelligence and... 2023Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine...
Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.
PubMed: 35039756
DOI: 10.1007/s12652-021-03612-z -
BMJ (Clinical Research Ed.) Sep 2021To examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice.
OBJECTIVE
To examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice.
DESIGN
Systematic review of test accuracy studies.
DATA SOURCES
Medline, Embase, Web of Science, and Cochrane Database of Systematic Reviews from 1 January 2010 to 17 May 2021.
ELIGIBILITY CRITERIA
Studies reporting test accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in women's digital mammograms in screening practice, or in test sets. Reference standard was biopsy with histology or follow-up (for screen negative women). Outcomes included test accuracy and cancer type detected.
STUDY SELECTION AND SYNTHESIS
Two reviewers independently assessed articles for inclusion and assessed the methodological quality of included studies using the QUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A single reviewer extracted data, which were checked by a second reviewer. Narrative data synthesis was performed.
RESULTS
Twelve studies totalling 131 822 screened women were included. No prospective studies measuring test accuracy of AI in screening practice were found. Studies were of poor methodological quality. Three retrospective studies compared AI systems with the clinical decisions of the original radiologist, including 79 910 women, of whom 1878 had screen detected cancer or interval cancer within 12 months of screening. Thirty four (94%) of 36 AI systems evaluated in these studies were less accurate than a single radiologist, and all were less accurate than consensus of two or more radiologists. Five smaller studies (1086 women, 520 cancers) at high risk of bias and low generalisability to the clinical context reported that all five evaluated AI systems (as standalone to replace radiologist or as a reader aid) were more accurate than a single radiologist reading a test set in the laboratory. In three studies, AI used for triage screened out 53%, 45%, and 50% of women at low risk but also 10%, 4%, and 0% of cancers detected by radiologists.
CONCLUSIONS
Current evidence for AI does not yet allow judgement of its accuracy in breast cancer screening programmes, and it is unclear where on the clinical pathway AI might be of most benefit. AI systems are not sufficiently specific to replace radiologist double reading in screening programmes. Promising results in smaller studies are not replicated in larger studies. Prospective studies are required to measure the effect of AI in clinical practice. Such studies will require clear stopping rules to ensure that AI does not reduce programme specificity.
STUDY REGISTRATION
Protocol registered as PROSPERO CRD42020213590.
Topics: Artificial Intelligence; Breast Neoplasms; Early Detection of Cancer; Female; Humans; Mammography; Mass Screening
PubMed: 34470740
DOI: 10.1136/bmj.n1872 -
Cureus Nov 2023Breast cancer is a prevalent global health concern, necessitating accurate diagnostic tools for effective management. Diagnostic imaging plays a pivotal role in breast... (Review)
Review
Breast cancer is a prevalent global health concern, necessitating accurate diagnostic tools for effective management. Diagnostic imaging plays a pivotal role in breast cancer diagnosis, staging, treatment planning, and outcome evaluation. Radiomics is an emerging field of study in medical imaging that contains a broad set of computational methods to extract quantitative features from radiographic images. This can be utilized to guide diagnosis, treatment response, and prognosis in clinical settings. A systematic review was performed in concordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. Quality was assessed using the radiomics quality score. Diagnostic sensitivity and specificity of radiomics analysis, with 95% confidence intervals (CIs), were included for meta-analysis. The area under the curve analysis was recorded. An extensive statistical analysis was performed following the Cochrane guidelines. Statistical significance was determined if p-values were less than 0.05. Statistical analyses were conducted using Review Manager (RevMan), Version 5.4.1. A total of 31 manuscripts involving 8,773 patients were included, with 17 contributing to the meta-analysis. The cohort comprised 56.2% malignant breast cancers and 43.8% benign breast lesions. MRI demonstrated a sensitivity of 0.91 (95% CI: 0.89-0.92) and a specificity of 0.84 (95% CI: 0.82-0.86) in differentiating between benign and malignant breast cancers. Mammography-based radiomic features predicted breast cancer subtype with a sensitivity of 0.79 (95% CI: 0.76-0.82) and a specificity of 0.81 (95% CI: 0.79-0.84). Ultrasound-based analysis yielded a sensitivity of 0.92 (95% CI: 0.90-0.94) and a specificity of 0.85 (95% CI: 0.83-0.88). Only one study reported the results of radiomic evaluation from CT, which had a sensitivity of 0.95 (95% CI: 0.88-0.99) and a specificity of 0.56 (95% CI: 0.45-0.67). Across different imaging modalities, radiomics exhibited robust diagnostic accuracy in differentiating benign and malignant breast lesions. The results underscore the potential of radiomic assessment as a minimally invasive alternative or adjunctive diagnostic tool for breast cancer. This is pioneering data that reports on a novel diagnostic approach that is understudied and underreported. However, due to study limitations, the complexity of this technology, and the need for future development, biopsy still remains the current gold standard method of determining breast cancer type.
PubMed: 38024014
DOI: 10.7759/cureus.49015 -
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 -
Diagnostics (Basel, Switzerland) Aug 2022Contrast-enhanced mammography (CEM) and contrast-enhanced magnetic resonance imaging (CE-MRI) are commonly used in the screening of breast cancer. The present systematic... (Review)
Review
BACKGROUND
Contrast-enhanced mammography (CEM) and contrast-enhanced magnetic resonance imaging (CE-MRI) are commonly used in the screening of breast cancer. The present systematic review aimed to summarize, critically analyse, and meta-analyse the available evidence regarding the role of CE-MRI and CEM in the early detection, diagnosis, and preoperative assessment of breast cancer.
METHODS
The search was performed on PubMed, Google Scholar, and Web of Science on 28 July 2021 using the following terms "breast cancer", "preoperative staging", "contrast-enhanced mammography", "contrast-enhanced spectral mammography", "contrast enhanced digital mammography", "contrast-enhanced breast magnetic resonance imaging" "CEM", "CESM", "CEDM", and "CE-MRI". We selected only those papers comparing the clinical efficacy of CEM and CE-MRI. The study quality was assessed using the QUADAS-2 criteria. The pooled sensitivities and specificity of CEM and CE-MRI were computed using a random-effects model directly from the STATA "metaprop" command. The between-study statistical heterogeneity was tested (I-statistics).
RESULTS
Nineteen studies were selected for this systematic review. Fifteen studies (1315 patients) were included in the metanalysis. Both CEM and CE-MRI detect breast lesions with a high sensitivity, without a significant difference in performance (97% and 96%, respectively).
CONCLUSIONS
Our findings confirm the potential of CEM as a supplemental screening imaging modality, even for intermediate-risk women, including females with dense breasts and a history of breast cancer.
PubMed: 36010240
DOI: 10.3390/diagnostics12081890