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JAMA Internal Medicine Nov 2023Cancer screening tests are promoted to save life by increasing longevity, but it is unknown whether people will live longer with commonly used cancer screening tests. (Meta-Analysis)
Meta-Analysis
IMPORTANCE
Cancer screening tests are promoted to save life by increasing longevity, but it is unknown whether people will live longer with commonly used cancer screening tests.
OBJECTIVE
To estimate lifetime gained with cancer screening.
DATA SOURCES
A systematic review and meta-analysis was conducted of randomized clinical trials with more than 9 years of follow-up reporting all-cause mortality and estimated lifetime gained for 6 commonly used cancer screening tests, comparing screening with no screening. The analysis included the general population. MEDLINE and the Cochrane library databases were searched, and the last search was performed October 12, 2022.
STUDY SELECTION
Mammography screening for breast cancer; colonoscopy, sigmoidoscopy, or fecal occult blood testing (FOBT) for colorectal cancer; computed tomography screening for lung cancer in smokers and former smokers; or prostate-specific antigen testing for prostate cancer.
DATA EXTRACTION AND SYNTHESIS
Searches and selection criteria followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline. Data were independently extracted by a single observer, and pooled analysis of clinical trials was used for analyses.
MAIN OUTCOMES AND MEASURES
Life-years gained by screening was calculated as the difference in observed lifetime in the screening vs the no screening groups and computed absolute lifetime gained in days with 95% CIs for each screening test from meta-analyses or single randomized clinical trials.
RESULTS
In total, 2 111 958 individuals enrolled in randomized clinical trials comparing screening with no screening using 6 different tests were eligible. Median follow-up was 10 years for computed tomography, prostate-specific antigen testing, and colonoscopy; 13 years for mammography; and 15 years for sigmoidoscopy and FOBT. The only screening test with a significant lifetime gain was sigmoidoscopy (110 days; 95% CI, 0-274 days). There was no significant difference following mammography (0 days: 95% CI, -190 to 237 days), prostate cancer screening (37 days; 95% CI, -37 to 73 days), colonoscopy (37 days; 95% CI, -146 to 146 days), FOBT screening every year or every other year (0 days; 95% CI, -70.7 to 70.7 days), and lung cancer screening (107 days; 95% CI, -286 days to 430 days).
CONCLUSIONS AND RELEVANCE
The findings of this meta-analysis suggest that current evidence does not substantiate the claim that common cancer screening tests save lives by extending lifetime, except possibly for colorectal cancer screening with sigmoidoscopy.
Topics: Male; Humans; Early Detection of Cancer; Prostate-Specific Antigen; Mass Screening; Prostatic Neoplasms; Lung Neoplasms; Randomized Controlled Trials as Topic; Colorectal Neoplasms; Colonoscopy; Occult Blood
PubMed: 37639247
DOI: 10.1001/jamainternmed.2023.3798 -
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 Clinical Nursing Oct 2023Older people in the nursing home environment are much less mobile and capable of taking care of themselves as they age, and most of them face the plight of loneliness,... (Review)
Review
BACKGROUND
Older people in the nursing home environment are much less mobile and capable of taking care of themselves as they age, and most of them face the plight of loneliness, which seriously affects the quality of life of older people in their later years.
AIMS
A systematic review and synthesis of older people's experiences of loneliness in nursing homes.
DESIGN
Following ENTREQ, do a systematic evaluation and synthesis of qualitative investigations.
METHODS
A search of PubMed, Cochrane Library, Web of Science, Embase, the Chinese biomedical literature service system, the China National Knowledge Infrastructure, the Wanfang Database and the Wipu Database for qualitative studies of older people's experiences of loneliness in nursing homes was conducted with a search time frame of March 2023. Evaluation of the quality of the literature using the Joanna Briggs Institute's Australian Centre for Evidence-Based Health Care Quality Assessment Criteria for Qualitative Research, And the data were synthesised using Thomas and Harden's method of thematic and content analysis.
RESULTS
A total of 13 papers were included, and 36 research findings were distilled and integrated into three themes: causes of loneliness; feelings of loneliness; coping with loneliness; and seven sub-themes: aging and loss; environmental transformation; loneliness is a pain; loneliness is a choice; participation; strengthening social ties; and diverting attention.
CONCLUSIONS
Older people in nursing homes face varying degrees of loneliness, which is a subjective feeling influenced by the interplay between personal awareness and the external environment, so future care interventions should be developed in a comprehensive manner, taking into account the characteristics of the older people themselves and their external environment.
NO PATIENT OR PUBLIC CONTRIBUTION
This study is a meta-synthesis and does not require relevant contributions from patients or the public.
Topics: Humans; Aged; Loneliness; Quality of Life; Australia; Emotions; Nursing Homes
PubMed: 37605069
DOI: 10.1111/jocn.16842 -
European Journal of Radiology Oct 2023The growing application of deep learning in radiology has raised concerns about cybersecurity, particularly in relation to adversarial attacks. This study aims to... (Review)
Review
PURPOSE
The growing application of deep learning in radiology has raised concerns about cybersecurity, particularly in relation to adversarial attacks. This study aims to systematically review the literature on adversarial attacks in radiology.
METHODS
We searched for studies on adversarial attacks in radiology published up to April 2023, using MEDLINE and Google Scholar databases.
RESULTS
A total of 22 studies published between March 2018 and April 2023 were included, primarily focused on image classification algorithms. Fourteen studies evaluated white-box attacks, three assessed black-box attacks and five investigated both. Eleven of the 22 studies targeted chest X-ray classification algorithms, while others involved chest CT (6/22), brain MRI (4/22), mammography (2/22), abdominal CT (1/22), hepatic US (1/22), and thyroid US (1/22). Some attacks proved highly effective, reducing the AUC of algorithm performance to 0 and achieving success rates up to 100 %.
CONCLUSIONS
Adversarial attacks are a growing concern. Although currently the threats are more theoretical than practical, they still represent a potential risk. It is important to be alert to such attacks, reinforce cybersecurity measures, and influence the formulation of ethical and legal guidelines. This will ensure the safe use of deep learning technology in medicine.
Topics: Humans; Radiography; Radiology; Mammography; Tomography, X-Ray Computed; Algorithms
PubMed: 37699278
DOI: 10.1016/j.ejrad.2023.111085 -
JAMA Jun 2024Among all US women, breast cancer is the second most common cancer and the second most common cause of cancer death. In 2023, an estimated 43 170 women died of breast...
IMPORTANCE
Among all US women, breast cancer is the second most common cancer and the second most common cause of cancer death. In 2023, an estimated 43 170 women died of breast cancer. Non-Hispanic White women have the highest incidence of breast cancer and non-Hispanic Black women have the highest mortality rate.
OBJECTIVE
The USPSTF commissioned a systematic review to evaluate the comparative effectiveness of different mammography-based breast cancer screening strategies by age to start and stop screening, screening interval, modality, use of supplemental imaging, or personalization of screening for breast cancer on the incidence of and progression to advanced breast cancer, breast cancer morbidity, and breast cancer-specific or all-cause mortality, and collaborative modeling studies to complement the evidence from the review.
POPULATION
Cisgender women and all other persons assigned female at birth aged 40 years or older at average risk of breast cancer.
EVIDENCE ASSESSMENT
The USPSTF concludes with moderate certainty that biennial screening mammography in women aged 40 to 74 years has a moderate net benefit. The USPSTF concludes that the evidence is insufficient to determine the balance of benefits and harms of screening mammography in women 75 years or older and the balance of benefits and harms of supplemental screening for breast cancer with breast ultrasound or magnetic resonance imaging (MRI), regardless of breast density.
RECOMMENDATION
The USPSTF recommends biennial screening mammography for women aged 40 to 74 years. (B recommendation) The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of screening mammography in women 75 years or older. (I statement) The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of supplemental screening for breast cancer using breast ultrasonography or MRI in women identified to have dense breasts on an otherwise negative screening mammogram. (I statement).
Topics: Humans; Breast Neoplasms; Female; Mammography; Early Detection of Cancer; Middle Aged; Aged; Adult; Magnetic Resonance Imaging; Age Factors; Ultrasonography, Mammary; United States; Mass Screening
PubMed: 38687503
DOI: 10.1001/jama.2024.5534 -
The Canadian Journal of Cardiology Dec 2023Recent studies have shown that breast arterial calcification (BAC) detected on screening mammography is linked to cardiovascular diseases via medial calcification.... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Recent studies have shown that breast arterial calcification (BAC) detected on screening mammography is linked to cardiovascular diseases via medial calcification. However, its effect on cardiovascular outcomes remains unclear. Therefore, we conducted a meta-analysis to determine the effect of BAC on cardiovascular outcomes in patients.
METHODS
Three electronic databases (Pubmed, Embase, and Scopus) were searched on May 1, 2022, for studies examining the relationship between BAC and cardiovascular outcomes including cardiac death, acute myocardial infarction, ischemic heart disease, stroke, peripheral artery disease, and heart failure. A random-effects meta-analysis model was used to summarise the studies.
RESULTS
A total of 5 longitudinal studies were included with a combined cohort of 87,865 patients. Significantly, the pooled risk ratio (RR) of the association between BAC and cardiac death was 2.06 (P < 0.00001). BAC was associated with a significantly increased risk of developing other cardiovascular diseases, such as ischemic/hemorrhagic stroke (RR 1.51; P = 0.003), ischemic stroke (RR 1.82; P < 0.00001), peripheral vascular disease (RR 1.24; P = 0.003), and heart failure (RR 1.84; P < 0.00001). There was no significant relationship for developing myocardial infarction or for total cardiovascular diseases.
CONCLUSIONS
Our findings suggest that BAC was associated with an increased risk of cardiovascular mortality, and certain cardiovascular outcomes. There is thus a potential to use BAC as a sex-specific cardiovascular risk assessment tool. Furthermore, there is a need for more widespread reporting of BAC to better understand the pathophysiologic mechanisms behind its correlation with cardiovascular disease and to apply it in clinical practice.
Topics: Female; Male; Humans; Cardiovascular Diseases; Breast; Mammography; Breast Neoplasms; Risk Factors; Early Detection of Cancer; Breast Diseases; Myocardial Infarction; Heart Failure; Death
PubMed: 37506765
DOI: 10.1016/j.cjca.2023.07.024 -
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 -
Journal of the American College of... Feb 2024To summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for...
PURPOSE
To summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for future breast cancer risk prediction.
MATERIALS AND METHODS
A systematic literature review was performed using six databases (medRixiv, bioRxiv, Embase, Engineer Village, IEEE Xplore, and PubMed) from 2012 through September 30, 2022. Studies were included if they used real-world screening mammography examinations to validate AI algorithms for future risk prediction based on images alone or in combination with clinical risk factors. The quality of studies was assessed, and predictive accuracy was recorded as the area under the receiver operating characteristic curve (AUC).
RESULTS
Sixteen studies met inclusion and exclusion criteria, of which 14 studies provided AUC values. The median AUC performance of AI image-only models was 0.72 (range 0.62-0.90) compared with 0.61 for breast density or clinical risk factor-based tools (range 0.54-0.69). Of the seven studies that compared AI image-only performance directly to combined image + clinical risk factor performance, six demonstrated no significant improvement, and one study demonstrated increased improvement.
CONCLUSIONS
Early efforts for predicting future breast cancer risk based on mammography images alone demonstrate comparable or better accuracy to traditional risk tools with little or no improvement when adding clinical risk factor data. Transitioning from clinical risk factor-based to AI image-based risk models may lead to more accurate, personalized risk-based screening approaches.
Topics: Humans; Female; Breast Neoplasms; Mammography; Artificial Intelligence; Early Detection of Cancer; Breast; Retrospective Studies
PubMed: 37949155
DOI: 10.1016/j.jacr.2023.10.018 -
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